0.12Asian Modelica and FMI Conference 2026
amfc2026
2026-09-202026-09-22300:05https://modelica.simtek.cchttps://modelica.simtek.cc/media/2026-modelica-test/img/logo_qxqtCdB.webpAsia/ShanghaiModelica Technology & AI (R1001)Workshop 2: OpenModelica Introduction and Hands-on PracticeWorkshop2026-09-20T13:00:00+08:0013:0002:00This tutorial provides a comprehensive overview of the OpenModelica architecture and development environment. It covers the basics of the Modelica language, OMEdit GUI operations, and model compilation principles. The hands-on session will guide beginners through component library usage and drag-and-drop modeling to complete a full workflow from environment setup to simulation execution.amfc2026-234-workshop-2-openmodelica-introduction-and-hands-on-practiceCuiting PengenSoftware Requirements: OpenModelica 1.26.3
More Information Link: https://product.dangdang.com/12449354467.html (The introductory books we published）falsehttps://modelica.simtek.cc/amfc2026/talk/QDN8AR/https://modelica.simtek.cc/amfc2026/talk/QDN8AR/feedback/Modelica Technology & AI (R1001)Workshop 4: Hands-on agentic modeling and best practices, with Wolfram System ModelerWorkshop2026-09-20T15:15:00+08:0015:1502:45Agentic workflows developed for software development have changed our software development methods. Most of the trivial coding tasks are solved by AI in a fraction of time of what the software engineer used to take. Instead of coding, the focus of a software engineer is now on specifying the work to be done, choosing the right architecture, orchestrating the agents doing the work, and verifying the results.
Modelica – from its earlier definition – has always included a duality between the language and the user interface artifacts. And the language is no different for an LLM than a programming language like C, C++, Python, Julia, Rust, Wolfram, etc. Therefore, it took only a few months before a similar change happened for modeling and simulation as for software engineering. We do not model the same way as we did a year ago.
In this tutorial, we will first discuss the recent and upcoming evolution of the role of the simulation engineer towards a rather System Engineer type of work, assisted by agents. The skills of today’s simulation engineer however remain critical for successful simulations. We will demonstrate this by hands-on examples. In a second step, we will leverage Wolfram System Modeler and their newest agent skills to develop a detailed Modelica in an agentic manner, pointing at best practices and pitfalls.
At the end of this workshop, you will be equipped to develop your own model in a much faster and efficient way, as AI-embracing simulation engineer.amfc2026-236-workshop-4-hands-on-agentic-modeling-and-best-practices-with-wolfram-system-modelerClément CoïcenExpected experience of participants:
Basic Modelica knowledge
Very basic understanding of using LLMs
Software requirements:
Windows / Linux / MacOs
Wolfram System Modeler 15 (get free trial before the workshop if needed)
Download or check out the Wolfram System Modeler skills
Access to your preferred LLM
Link to further information
https://www.wolfram.com/system-modeler/
https://www.linkedin.com/in/clementcoic/falsehttps://modelica.simtek.cc/amfc2026/talk/YFTZKP/https://modelica.simtek.cc/amfc2026/talk/YFTZKP/feedback/(Electric) Mobility & Buildings (R2003)Workshop 1: FMI for BeginnersWorkshop2026-09-20T13:00:00+08:0013:0002:00The Functional Mock-up Interface (FMI) standard plays a central role in enabling interoperability between simulation tools, particularly in cyber-physical and embedded system development. This tutorial introduces the core concepts of FMI and provides hands-on experience in exporting, simulating, and co-simulating Functional Mock-up Units (FMUs). Designed for both beginners and intermediate users, the tutorial is divided into three main sessions. The first session presents the motivation and history of FMI, key technical concepts, and an overview of FMI 3.0, followed by live demonstrations of FMU export using Dymola and Simulink. The second session dives into working with FMUs using Python and the FMPy library, including validation, simulation, and integration with Jupyter notebooks. Finally, we will set up co-simulations involving multiple FMUs, highlighting both graphical and command-line workflows, and demonstrating the impact of co-simulation parameters on accuracy and stability.
The tutorial concludes with a joint session summarizing the role of FMI in digital twins and highlighting real-world applications. Throughout the tutorial, attendees will have opportunities to ask questions and follow along with guided examples and shared materials.amfc2026-232-workshop-1-fmi-for-beginnersChristian BertschenExpected experience of participants
Basic familiarity with simulation concepts
Some programming experience (e.g., in Python)
Optional: Experience with Simulink, Modelica Tools, or co-simulation tools is beneficial but not required
Software requirements:
Python with fmpy[complete] (installation guide)
Optional: Matlab/Simulink
Optional: a Modelica tool such as OpenModelica or Dymola to export FMUs.
Optional: A Google account to use Google Colab for co-simulation notebooks
Link to further information: https://github.com/modelica/fmi-beginners-tutorial-2026/falsehttps://modelica.simtek.cc/amfc2026/talk/HKQ3AA/https://modelica.simtek.cc/amfc2026/talk/HKQ3AA/feedback/(Electric) Mobility & Buildings (R2003)Workshop 1: FMI for Beginners (Continued)Workshop2026-09-20T15:15:00+08:0015:1500:45The Functional Mock-up Interface (FMI) standard plays a central role in enabling interoperability between simulation tools, particularly in cyber-physical and embedded system development. This tutorial introduces the core concepts of FMI and provides hands-on experience in exporting, simulating, and co-simulating Functional Mock-up Units (FMUs). Designed for both beginners and intermediate users, the tutorial is divided into three main sessions. The first session presents the motivation and history of FMI, key technical concepts, and an overview of FMI 3.0, followed by live demonstrations of FMU export using Dymola and Simulink. The second session dives into working with FMUs using Python and the FMPy library, including validation, simulation, and integration with Jupyter notebooks. Finally, we will set up co-simulations involving multiple FMUs, highlighting both graphical and command-line workflows, and demonstrating the impact of co-simulation parameters on accuracy and stability.
The tutorial concludes with a joint session summarizing the role of FMI in digital twins and highlighting real-world applications. Throughout the tutorial, attendees will have opportunities to ask questions and follow along with guided examples and shared materials.amfc2026-233-workshop-1-fmi-for-beginners-continuedChristian Bertschenfalsehttps://modelica.simtek.cc/amfc2026/talk/9BFZKV/https://modelica.simtek.cc/amfc2026/talk/9BFZKV/feedback/(Electric) Mobility & Buildings (R2003)Workshop 3: EV thermal management modeling practice with TFS and YSSIM.YSLABWorkshop2026-09-20T16:00:00+08:0016:0002:00Thermal management modeling and simulation have become critical to electric vehicle (EV) development. Modelica is well-suited for this domain thanks to its multi-disciplinary, acausal modeling capabilities, which naturally capture the physical behavior of batteries, electric motors, power electronics, and their associated cooling systems.
The ThermofluidStream (TFS) library, originally developed at DLR, introduces a novel modeling paradigm in Modelica that enables efficient simulation of large-scale thermal-fluid network models. YSLAB, developed by Nanjing Yuansi SimTek, is a browser-based Modelica simulation environment that lowers the barrier to entry for system-level modeling and interactive exploration.
This tutorial is structured in two parts. We begin with an introduction to thermodynamics modeling at the component level using YSLAB. Participants will then engage in a hands-on session to build an EV thermal management system model using the TFS library, from component instantiation to system assembly and simulation.amfc2026-235-workshop-3-ev-thermal-management-modeling-practice-with-tfs-and-yssim-yslabJunjie ChenenPrerequisites:
Basic familiarity with Modelica
Fundamental understanding of vehicle thermal management principles
Software Requirements:
Operating System: Windows, Linux
A YSLAB account with the ThermalFluidStreams library installed (access details will be provided at the start of the tutorial)
Further Information:
To be provided at the start of the session.falsehttps://modelica.simtek.cc/amfc2026/talk/YNEHY3/https://modelica.simtek.cc/amfc2026/talk/YNEHY3/feedback/Main hall (R1016)Openning speechInvited Talk2026-09-21T09:00:00+08:0009:0000:05Opening speechamfc2026-215-openning-speechenfalsehttps://modelica.simtek.cc/amfc2026/talk/SKRQ78/https://modelica.simtek.cc/amfc2026/talk/SKRQ78/feedback/Main hall (R1016)News from the Modelica AssociationInvited Talk2026-09-21T09:05:00+08:0009:0500:15Newsamfc2026-216-news-from-the-modelica-associationDirk Zimmerenfalsehttps://modelica.simtek.cc/amfc2026/talk/LREMSQ/https://modelica.simtek.cc/amfc2026/talk/LREMSQ/feedback/Main hall (R1016)AI Safety and Development Tools for Autonomous DrivingKeynote2026-09-21T09:20:00+08:0009:2000:30The report focuses on AI safety and R&D toolchains for intelligent vehicles. Drawing on the team’s extensive research in the system safety of connected and autonomous vehicles (CAVs) and autonomous driving control, it analyzes failure risks in AI perception and decision-making algorithms, as well as functional safety and data security challenges. It further elaborates on a comprehensive safety protection framework tailored for full-scenario autonomous driving. Addressing key industry pain points, the report introduces an end-to-end R&D toolchain for intelligent driving, covering simulation testing and electronic control development platforms, while demonstrating their practical applications in algorithm verification and safety assessment. Finally, by integrating theoretical insights with engineering practice, it explores iterative pathways for advancing AI safety technologies and outlines future directions for domestically developed tools, offering actionable strategies to drive technological upgrades and real-world deployment across the industry.amfc2026-217-ai-safety-and-development-tools-for-autonomous-drivingShichun Yangenfalsehttps://modelica.simtek.cc/amfc2026/talk/LQJB7C/https://modelica.simtek.cc/amfc2026/talk/LQJB7C/feedback/Main hall (R1016)Model-based Digital Engineering Practice for AutomobilesKeynote2026-09-21T09:50:00+08:0009:5000:30Leveraging its full-industry-chain advantages, vertical integration, and massive proprietary R&D, BYD has achieved global leadership in new energy vehicle sales. To address the growing complexity of vehicle systems while sustaining this leadership, systematic application of systems engineering methods has become essential across the product lifecycle. This keynote focuses on digital engineering practices with Modelica, exploring three interrelated areas: 1) co-simulation design and validation methodologies for highly integrated vehicle systems; 2) high-fidelity and computationally efficient modeling approaches for diverse R&D scenarios; and 3) engineering implementation of toolchain integration and virtual validation. We are currently advancing Modelica-based digital engineering practices across multiple domains: developing a whole-vehicle digital engineering co-simulation platform that integrates powertrain, thermal management, and chassis control models, realizing seamless cross-system collaborative optimization of power performance, energy efficiency, and handling stability; creating digital models for HVAC thermal management systems that accurately predict cabin thermal load and system power consumption and energy flows, significantly enhancing both energy efficiency and cabin comfort; implementing digital development processes for seat systems that enable early-stage validation of control strategies, vibration-noise characteristics, and comfort attributes through model-driven design and simulation, substantially reducing physical prototype development-cycles; and pioneering AI-Modelica integration that leverages data-driven methods to accelerate model generation, parameter identification, and calibration, substantially improving the intelligence level of complex system modeling. These practices collectively validate Modelica’s critical role in complex multidisciplinary system modeling, offering a new paradigm for enhancing research and development efficiency in intelligent vehicle development.amfc2026-218-model-based-digital-engineering-practice-for-automobilesYilin Zhongenfalsehttps://modelica.simtek.cc/amfc2026/talk/7QBACH/https://modelica.simtek.cc/amfc2026/talk/7QBACH/feedback/Main hall (R1016)The Rise of Computer Intelligence and what it means for System SimulationKeynote2026-09-21T10:40:00+08:0010:4000:30We are undergoing a transition period where computer intelligence is rising to dominance. It will become widely available to everyone at an unprecedented level of intelligence and also at low cost. Such transition periods are often highly chaotic and short-term predictions may hence be rather inaccurate. Based on first principles, however, we can predict what ultimately will prevail after the transition has taken place. This keynote shall provide such an analysis to answer the most relevant questions from the perspective of system simulation: Will the use of simulation rise or fall? Will there be a need for system simulation? What will be the use of Modelica in the future? Will mathematical modeling still be a thing for humans and for computers? The keynote will also explain why it uses the term computer intelligence rather than artificial intelligence.amfc2026-219-the-rise-of-computer-intelligence-and-what-it-means-for-system-simulationDirk Zimmerenfalsehttps://modelica.simtek.cc/amfc2026/talk/DRKW9Y/https://modelica.simtek.cc/amfc2026/talk/DRKW9Y/feedback/Main hall (R1016)Joint Host SpotlightInvited Talk2026-09-21T11:10:00+08:0011:1000:25Reflections on the Development Stages and Engineering Deployment of Physics-AI Integration: Challenges, Solutions, and Case Studies of AI Agents in System Modeling and Simulationamfc2026-220-joint-host-spotlightAiguo Xuenfalsehttps://modelica.simtek.cc/amfc2026/talk/WEKZ8K/https://modelica.simtek.cc/amfc2026/talk/WEKZ8K/feedback/Main hall (R1016)T0S1-Session ChairChair2026-09-21T11:39:00+08:0011:3900:01This session is chaired by:amfc2026-243-t0s1-session-chairHongsi Qinenfalsehttps://modelica.simtek.cc/amfc2026/talk/NMCWGX/https://modelica.simtek.cc/amfc2026/talk/NMCWGX/feedback/Main hall (R1016)Digital Engineering Construction Practice for Complex System DevelopmentInvited Talk2026-09-21T11:40:00+08:0011:4000:25Addressing the challenges of surging complexity, cross-disciplinary collaboration barriers, and fragmented models and data in complex system development,
this paper presents the transformation path from "engineering object modeling" to "engineering relationship digitalization." Leveraging requirements traction,
system architecture-driven design, agile iteration, and multi-alternative trade-off as core capabilities, it integrates the full-lifecycle closed loop spanning requirements, design,
virtual verification, and manufacturing through a unified digital thread and authoritative model source. This enables traceable changes, decision-ready alternatives,
and higher R&D efficiency, offering a systematic construction roadmap and practical reference for the digital design of complex systems.amfc2026-221-digital-engineering-construction-practice-for-complex-system-developmentJian Tangenfalsehttps://modelica.simtek.cc/amfc2026/talk/8VRVL9/https://modelica.simtek.cc/amfc2026/talk/8VRVL9/feedback/Main hall (R1016)Trustworthy AI4MBSE Industrial PlatformInvited Talk2026-09-21T12:05:00+08:0012:0500:25Targeting the three major dilemmas of AI-empowered MBSE—model hallucination, reasoning black boxes, and application runaway—we propose a Trustworthy AI4MBSE industrial platform solution. It ensures confident outputs, traceable processes, and governable behaviors, thereby establishing a credible foundation for AI to enter the industrial core.amfc2026-222-trustworthy-ai4mbse-industrial-platformGuan Wangenfalsehttps://modelica.simtek.cc/amfc2026/talk/SESRMS/https://modelica.simtek.cc/amfc2026/talk/SESRMS/feedback/Main hall (R1016)Workshop 5: SMArtInt+ - Hands-on AI: Generation and Integration of Neural Networks and Surrogate Models into ModelicaWorkshop2026-09-21T13:50:00+08:0013:5003:00There is an increasing need for hybrid models consisting of physical and machine-learning or surrogate model parts. XRG has developed the SMArtInt+ Library, which enables the easy generation and integration of neural networks from different sources and types. This tutorial provides a hands-on introduction to the use of this library, which also includes a Python toolbox. The main use case is the creation and integration of a transient surrogate model for a thermodynamic application.
The tutorial focuses on the generation and utilization of training data, as well as the definition and training of neural network architectures, with the objective of developing high-performance surrogate models. Participants will be introduced to different concepts and approaches for creating data-driven surrogate models and will gain an understanding of the underlying methodologies and best practices.
A central part of the tutorial is the integration of the trained neural networks and surrogate models into Modelica models using the SMArtInt+ Interface-Blocks, allowing seamless substitution of physical components with learned surrogates. Additionally, several concepts of hybrid modeling that combine physical and data-driven approaches are introduced, with a strong focus on the practical integration of trained neural networks and surrogate models within the Modelica environment.
Finally, model performance is evaluated through comparisons with the original physical model.amfc2026-237-workshop-5-smartint-hands-on-ai-generation-and-integration-of-neural-networks-and-surrogate-models-into-modelicaTim HankeenExpected experience of participants:
• Basic knowledge of neural networks/AI and Python required
• Basic Modelica knowledge
Software requirements:
• Windows 10 or 11
• Dymola 2026 Refresh 1 or OpenModelica 1.26 (backup)
• Python IDE (PyCharm preferred)
Link to further information
https://xrg-simulation.de/en/seiten/smartint
Link to Tutorial resources
https://xrgsim-my.sharepoint.com/:f:/g/personal/hanke_xrg-simulation_de/IgDfMfXR1yLDTaeHTrhINoi7AZEAQaA1mcv2uHgCfbvdKus?e=kAU7fX
Password: 2FaywVe4false
Tutorial Files (Setup.exe & additional Data)
https://modelica.simtek.cc/amfc2026/talk/HAMFRX/https://modelica.simtek.cc/amfc2026/talk/HAMFRX/feedback/Modelica Technology & AI (R1001)T1S1-Session ChairChair2026-09-21T11:39:00+08:0011:3900:01This session is chaired by:amfc2026-239-t1s1-session-chairFrancesco Casellaenfalsehttps://modelica.simtek.cc/amfc2026/talk/QLPHQD/https://modelica.simtek.cc/amfc2026/talk/QLPHQD/feedback/Modelica Technology & AI (R1001)A Novel Parametric Reduced-Order Modeling Method for Transient Prediction of Modelica-Based Brayton CyclesOral presentation2026-09-21T11:40:00+08:0011:4000:25Repeated transient simulations of Brayton cycles are often required during parameter studies, control assessment, and design iteration, whereas direct Modelica simulation under many conditions can remain inefficient despite its flexibility for system-level dynamic modeling. A parametric reduced-order modeling method is developed for fast transient prediction of Modelica-based Brayton cycles under unseen parameters and new operating conditions. Transient data from a Modelica model of a Helium-Xenon Brayton cycle are used to construct a baseline-regularized Dynamic Mode Decomposition with control reduced-order model, in which state trajectories are formulated as deviations from local steady baselines and measured auxiliary inputs are incorporated through the control channel. A Secondary Dynamic Mode Decomposition strategy is further introduced to generalize reduced-order quantities along the parameter direction. Results show accurate interpolation and stable extrapolative prediction against Modelica reference simulations, supporting rapid transient evaluation of Brayton cycles and offering a new perspective for broader fast transient prediction.amfc2026-162-a-novel-parametric-reduced-order-modeling-method-for-transient-prediction-of-modelica-based-brayton-cyclesAo ZhangXiang WangenfalseFull paper PDFhttps://modelica.simtek.cc/amfc2026/talk/FSUHEP/https://modelica.simtek.cc/amfc2026/talk/FSUHEP/feedback/Modelica Technology & AI (R1001)Research on Simulation Efficiency of Supercritical Carbon Dioxide Brayton Power Generation System Based on ModelicaOral presentation2026-09-21T12:05:00+08:0012:0500:25Existing Modelica libraries have demonstrated effective modeling of sCO2 systems. However, the influence of momentum-equation formulation on solver behavior has not been systematically quantified under an identical system model and consistent numerical settings. This study develops a one-dimensional dynamic Modelica model of an sCO2 Brayton power system and systematically compares four momentum-equation formulations within the same system under identical numerical settings, quantitatively examining how state selection, flow inertia, and pressure feedback affect solver steps, Jacobian evaluations, and error-dominant variables. The conventional steady-state algebraic momentum formulation is compared with three differential formulations incorporating flow inertia, and their computational performance is assessed under both steadystate and transient conditions. For the investigated system model and operating cases, the differential formulations containing a mass-flow derivative term completed the transient simulations, whereas the conventional steadystate algebraic formulation did not converge under the same numerical settings. Furthermore, by introducing a first-order inertia term for the average pipe flow rate together with inlet/outlet pressure feedback, the steadystate computation time is reduced from 750.5 s to 73.2 s, the number of transient solver steps decreases from 7878 to 3967, the number of Jacobian evaluations is reduced by 52.8%, and the occurrence frequencies of the reported error-dominant variables decrease by approximately 62% to 77% relative to Fluid Equation 2. These results provide a quantitative, model-specific characterization of how state selection, flow inertia, and pressure feedback affect solver behavior under the tested conditions.amfc2026-165-research-on-simulation-efficiency-of-supercritical-carbon-dioxide-brayton-power-generation-system-based-on-modelicaSiqiang YiBaodi ZhangenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/9JXXRT/https://modelica.simtek.cc/amfc2026/talk/9JXXRT/feedback/Modelica Technology & AI (R1001)T1S2-Session ChairChair2026-09-21T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-244-t1s2-session-chairClément Coïcenfalsehttps://modelica.simtek.cc/amfc2026/talk/YFSEAH/https://modelica.simtek.cc/amfc2026/talk/YFSEAH/feedback/Modelica Technology & AI (R1001)Robust solution of inverse static problems in Modelica: the case for the initialSimplified() operatorOral presentation2026-09-21T13:50:00+08:0013:5000:25Solving inverse static initialization problems, in which the outputs of a process are prescribed, is often more numerically challenging than solving direct initialization problem,
where inputs are fixed instead. This paper proposes the introduction of a new initialSimplified() operator in Modelica, which allows to solve inverse problems with the same reliability as direct problems, using an intuitive, straightforward Modelica formulation. The proposed solution can be trivially implemented in Modelica tools and will be 100% portable among them, once the new operator is standardized. The usefulness of the proposed approach is demonstrated on a conceptual thermal power generation plant model.amfc2026-169-robust-solution-of-inverse-static-problems-in-modelica-the-case-for-the-initialsimplified-operatorFrancesco CasellaPhilip HannebohmMatteo Luigi De PascalienfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/KQQRWW/https://modelica.simtek.cc/amfc2026/talk/KQQRWW/feedback/Modelica Technology & AI (R1001)Large Network Examples for the Generation of Modular Simulation Code from ModelicaOral presentation2026-09-21T14:15:00+08:0014:1500:25This paper presents progress toward the simulation of large-scale networks in a strict subset of Modelica using Linear Implicit Equilibrium Dynamics (LIED). A specialized compiler generates modular simulation code that scales to millions of equations — beyond conventional monolithic approaches. Demonstrated with a ThermoLiquidStream library for district heating, fractal networks exceeding 10 million equations are successfully simulated on standard hardware. The results confirm that LIED-based modular compilation offers a practical, industrially relevant path for large network modeling while remaining fully compatible with existing Modelica tools.amfc2026-173-large-network-examples-for-the-generation-of-modular-simulation-code-from-modelicaDirk ZimmerenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/EZMAMG/https://modelica.simtek.cc/amfc2026/talk/EZMAMG/feedback/Modelica Technology & AI (R1001)SMC: A Cloud-Deployable Modelica Frontend Service in GoOral presentation2026-09-21T14:40:00+08:0014:4000:25Web-based modeling platforms and automated modelchecking workflows in CI/CD pipelines require remotely callable Modelica frontend services. This paper presents SMC (Simtek Modelica Compiler), a service-oriented Modelica frontend implemented in Go. SMC exposes model loading, structured queries, diagnostics, and flattening through a service API backed by a pipeline for parsing, instantiation, semantic analysis, connection processing, and flat-model generation. Drawing on the SMC implementation, we also discuss the engineering implications of Go for single-binary deployment, concurrent processing, representation of Modelica language constructs, and memory allocation. We demonstrate the prototype through integration with a web modeling platform. A common set of 606 comparable classes from the Modelica Standard Library (MSL) 4.0.0 is used both to assess frontend-result consistency against the OpenModelica Compiler (OMC) and to measure flattening time, memory allocation, and garbage-collection behavior. The evaluation covers frontend artifacts and in-process processing costs; backend processing, code generation, simulation, and FMU export are outside its scope.amfc2026-177-smc-a-cloud-deployable-modelica-frontend-service-in-goHailong WangQingda XuEnyang ZhangLongsheng SunRui GaoAiguo XuenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/NAPY3J/https://modelica.simtek.cc/amfc2026/talk/NAPY3J/feedback/Modelica Technology & AI (R1001)T1S3-Session ChairChair2026-09-21T15:34:00+08:0015:3400:01This session is chaired by:amfc2026-249-t1s3-session-chairClément Coïcenfalsehttps://modelica.simtek.cc/amfc2026/talk/NVZJRC/https://modelica.simtek.cc/amfc2026/talk/NVZJRC/feedback/Modelica Technology & AI (R1001)Research on PINN Data-Driven Modeling Method Based on Modelica ModelOral presentation2026-09-21T15:35:00+08:0015:3500:25In practical engineering, equipment parameters are difficult to obtain and measurement data is limited. This paper proposes a PINN-Modelica data-driven modeling method that couples a neural network with a Modelica model encapsulated as an FMU. Instead of directly constructing governing-equation residuals in the proposed method, the FMU supplies a modular physicsbased constraint during training. The neural network predicts both terminal voltage and time-varying physical parameters, while the FMU computes a physics-based voltage from the predicted parameters. Data and physics losses jointly constrain the neural-network voltage output, and the sensitivity of the physics loss to the FMU input parameters is evaluated numerically by central finite differences and combined with neural-network backpropagation. A second-order Thevenin battery model is used as a proof-of-concept, and coupled simulation is implemented through the Functional Mockup Interface (FMI) standard. The framework provides a reusable approach for integrating Modelica physical models with data-driven parameter identification.amfc2026-180-research-on-pinn-data-driven-modeling-method-based-on-modelica-modelKaixuan ZhangYanfang LiuYuan SunHeng WangXudong WangZhidong ZhangWei WuenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/TGUR3V/https://modelica.simtek.cc/amfc2026/talk/TGUR3V/feedback/Modelica Technology & AI (R1001)From Intent to Action: Agentic AI on Validated Modelica LibrariesOral presentation2026-09-21T16:00:00+08:0016:0000:25In this work, we compare a traditional design-ofexperiments (DoE) workflow, defined up-front and then run, against an agentic AI workflow that revises its plan as the data arrives. The use case is a chassis optimization on the standard compact example in the Modelon VehicleDynamics Library. Both workflows produce a recommendation. The DoE arrives at a clean Pareto front and a stiffness change. The agentic path is longer: along the way it flags a controller saturation in the DoE winner, traces a center-ofgravity asymmetry to a load sensitivity the original scope did not cover, and finds a kinematic ceiling outside the four DoE parameters. It ends up changing a different parameter, and that parameter turns out to do more than the static KPIs asked for: dynamic verification shows the bushing change roughly halves the fishhook return lag unloaded. The architecture that makes this possible combines validated physics, Modelon Impact, the Model Context Protocol that connects them to an LLM, and a knowledge base layered by lifetime. We close with what we learned about when each approach is the right tool.amfc2026-184-from-intent-to-action-agentic-ai-on-validated-modelica-librariesJohan AndreassonAjith KumarClément CoïcenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/J78WAX/https://modelica.simtek.cc/amfc2026/talk/J78WAX/feedback/Modelica Technology & AI (R1001)Agentic Model-Based Design with FMI: A Two-Axis Interoperability ArchitectureOral presentation2026-09-21T16:25:00+08:0016:2500:25Model-Based Design has proven effective for developing complex cyber-physical systems, yet its workflows remain largely manual. Additionally, industrial workflows often cross tool and organizational boundaries. This paper presents an agentic MBD framework for such boundary-crossing workflows. The architecture combines the Model Context Protocol (MCP) as the agent-to-tool interface, with FMI 3.0 as a model-to-model interface. We report an initial case study in which an agent builds a battery management system faultprotection model around a supplier-provided battery pack FMU. The case study demonstrates the feasibility of using MCP and FMI as complementary interoperability boundaries for agent-driven MBD workflows and highlights the interface and verification challenges that emerge at those boundaries. The architecture is tool-neutral by design; the present work exercises it through a MATLAB® and Simulink® reference implementation.amfc2026-187-agentic-model-based-design-with-fmi-a-two-axis-interoperability-architectureWeiwu LiHaihua FengenfalsePaper in PDFpresentation slideshttps://modelica.simtek.cc/amfc2026/talk/SEQVXV/https://modelica.simtek.cc/amfc2026/talk/SEQVXV/feedback/FMI & MBSE (R2001)T2S1-Session ChairChair2026-09-21T11:39:00+08:0011:3900:01This session is chaired by:amfc2026-240-t2s1-session-chairDegang Fuenfalsehttps://modelica.simtek.cc/amfc2026/talk/DDQYAD/https://modelica.simtek.cc/amfc2026/talk/DDQYAD/feedback/FMI & MBSE (R2001)FMU-based Training of Physics-Informed Neural Nets for ControlOral presentation2026-09-21T11:40:00+08:0011:4000:25Physics-Informed Neural Nets for Control (PINCs) extend traditional Physics-Informed Neural Networks (PINNs) by incorporating control inputs and variable initial conditions, therefore enabling autoregressive long-horizon simulation by chaining short-horizon predictions, and making them suitable for control-oriented applications. However, the existing literature commonly adopts PINC training procedures where the governing dynamics are embedded in an explicit analytical form, whereas in many engineering workflows, the system models are often available only as Functional Mock-up Units (FMUs). This paper proposes an FMU-based training framework for PINC that uses an FMI 3.0 Model Exchange (ME) FMU directly as the description of system dynamics. The physics-informed residual is evaluated in the forward pass by querying the FMU for state derivatives, while gradient backpropagation is enabled through FMI 3.0 adjoint derivatives integrated into PyTorch via a custom autograd.Function. Case studies on a Van der Pol oscillator and on a mechanical system show that the proposed framework can train PINCs without manually implementing the analytical governing equations and can achieve long-horizon simulation performance comparable to conventional ODE-based training when the required FMI 3.0 features are available.amfc2026-163-fmu-based-training-of-physics-informed-neural-nets-for-controlYiding WangLuca BascettaGianni FerrettienfalsePaper in PDF: FMU-based Training of Physics-Informed Neural Nets for Controlhttps://modelica.simtek.cc/amfc2026/talk/C9HZ8L/https://modelica.simtek.cc/amfc2026/talk/C9HZ8L/feedback/FMI & MBSE (R2001)An AI-Enhanced FMI-Based Model Predictive Control Framework with IoT-Driven Digital Twin Updating for Energy-Efficient HVAC Systems(IUP)Oral presentation2026-09-21T12:05:00+08:0012:0500:25Large-scale HVAC systems in public buildings account for approximately 40% of total building energy consumption. This presentation reports an industrial deployment of an FMI-based Model Predictive Control (MPC) framework with AI-driven adaptive optimization for a university library's central air-conditioning system. A Modelica-based digital twin of the HVAC system was developed using FMI co-simulation architecture (Modelica Association 2026a; b). The digital twin is continuously updated with real-time IoT sensor data, enabling the MPC controller to minimize energy consumption while maintaining thermal comfort. An AI-driven iterative strategy automatically adjusts model parameters and control policies based on operational feedback (Zhang et al. 2026).
Field deployment through integration with the existing Building Automation System (BAS) achieved 15%–20% energy reduction versus conventional control, with 10-minute optimization cycles. The talk will share practical deployment experience, lessons learned from laboratory prototyping to industrial implementation, and open challenges in scaling AI-enhanced MPC for building energy management.amfc2026-166-an-ai-enhanced-fmi-based-model-predictive-control-framework-with-iot-driven-digital-twin-updating-for-energy-efficient-hvac-systems-iupXuelian LeienfalseAbstract in PDF: An AI-Enhanced FMI-Based Model Predictive Control Framework with IoT-Driven Digital Twin Updating for Energy-Efficient HVAC Systems(IUP)https://modelica.simtek.cc/amfc2026/talk/F8Z33G/https://modelica.simtek.cc/amfc2026/talk/F8Z33G/feedback/FMI & MBSE (R2001)T2S2-Session ChairChair2026-09-21T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-245-t2s2-session-chairChristian Bertschenfalsehttps://modelica.simtek.cc/amfc2026/talk/EFKM9L/https://modelica.simtek.cc/amfc2026/talk/EFKM9L/feedback/FMI & MBSE (R2001)OEM/Supplier Model Exchange Case Studies at JAMBE(IUP)Oral presentation2026-09-21T13:50:00+08:0013:5000:25JAMBE, the Japan Automotive Model-Based Engineering Center (https://www.jambe.jp), is an organization which aims to promote model-based engineering for the Japanese automotive industry. Automobile manufacturers (OEMs), component suppliers, and IT vendors are working together on three main activities: "promoting model-based engineering methodology," "promoting model reuse and exchange between OEMs and suppliers," and "solving common issues in model exchange." The latest membership count is 210 companies and organizations (as of January 30, 2026).
JAMBE is organizing joint pilot projects to validate the infrastructure, as well as the common rules and processes, for the secure and seamless exchange of simulation models between companies. In this paper, we will introduce two of these projects.amfc2026-170-oem-supplier-model-exchange-case-studies-at-jambe-iupDai ArakiYoshihiko NishiTatsuya MasuhisaYoshito MoriiYasushi NoguchienThese two JAMBE pilot projects have confirmed the practicality of a method for exchanging models while protecting confidential information. The methods proven effective in these pilot projects are currently being implemented in the actual design and development processes of JAMBE participating companies.falseAbstract in PDF: OEM/Supplier Model Exchange Case Studies at JAMBE(IUP)https://modelica.simtek.cc/amfc2026/talk/RKSPKH/https://modelica.simtek.cc/amfc2026/talk/RKSPKH/feedback/FMI & MBSE (R2001)Accelerating Virtual Validation using FMI-based Test Automation: From Component Testing to Digital Twins(IUP)Oral presentation2026-09-21T14:15:00+08:0014:1500:25The presentation demonstrates the workflow using ecu.test from tracetronic on representative automotive validation scenarios. Practical lessons learned from industrial deployment are shared, including reproducibility, interoperability, supplier integration and the reduction of late integration effort. The result is reduced dependence on costly HiL capacity, a consolidated tool landscape, lower integration effort and earlier software defect detection. Beyond the concrete use case, the contribution shows how FMI, its Layered Standards and SSP mature into a foundation for continuous, hardware-independent validation across the entire V-model.amfc2026-174-accelerating-virtual-validation-using-fmi-based-test-automation-from-component-testing-to-digital-twins-iupMatthias BertholdenfalseAbstract in PDF: Accelerating Virtual Validation using FMI-based Test Automation: From Component Testing to Digital Twinshttps://modelica.simtek.cc/amfc2026/talk/KSFRE7/https://modelica.simtek.cc/amfc2026/talk/KSFRE7/feedback/FMI & MBSE (R2001)Simulation and Verification of Marine Power System Based on Data, Models, and Mock-upsOral presentation2026-09-21T14:40:00+08:0014:4000:25Conventional simulation methods relying on single models or empirical formulas fail to support intelligent, full-lifecycle simulation and verification for complex systems such as marine power systems. Multi-domain modeling languages like Modelica, while powerful, remain at the tool level and cannot resolve bottlenecks in data fusion, high-confidence simulation, or closed-loop verification. To overcome these limitations, this paper proposes a system simulation methodology based on data, models, and mock-ups. The methodology integrates fulllifecycle multi-source data into simulation, employs simulation models to construct a digital mock-up, and achieves tight integration and closed-loop iteration among data, models, and the physical mock-up among data, models, and the physical mock-up, thereby establishing high-confidence system-level simulation across design, testing, service, and maintenance. A digital twin of a marine power system is developed using Modelica with real-time multi-dimensional data interaction. Experimental validation under four typical operating conditions (shutdown, 0kW, 30kW, 60kW) shows that simulated key parameters (steam pressure, turbine speed, output power) agree closely with measured values, with all relative errors controlled within 8%. These results demonstrate the feasibility, practicality, and engineering applicability of the proposed methodology, confirming that integrating data, models, and mock-ups effectively supports intelligent full-lifecycle operation and maintenance of complex systems and provides an implementable pathway for the intelligent transformation of systems engineering.amfc2026-178-simulation-and-verification-of-marine-power-system-based-on-data-models-and-mock-upsZiyi ZouLuotao XieRuirui ZengYangzeng XuGuobing ChenZichun YangenfalsePaper in PDF: Simulation and Verification of Marine Power System Based on Data, Models, and Mock-upshttps://modelica.simtek.cc/amfc2026/talk/A7F8VL/https://modelica.simtek.cc/amfc2026/talk/A7F8VL/feedback/FMI & MBSE (R2001)T2S3-Session ChairChair2026-09-21T15:34:00+08:0015:3400:01This session is chaired by:amfc2026-250-t2s3-session-chairDai Arakienfalsehttps://modelica.simtek.cc/amfc2026/talk/78GH9N/https://modelica.simtek.cc/amfc2026/talk/78GH9N/feedback/FMI & MBSE (R2001)An FMI-Based Multiscale Workflow for Multi-Scenario Local Accident Assessment in LBE Reactor Rod BundlesOral presentation2026-09-21T15:35:00+08:0015:3500:25System-level thermal-hydraulic models provide bulk quantities but cannot directly resolve rod-wise temperatures or hotspot migration during local accidents. This study develops an FMI-based multiscale workflow that couples a Modelica primary-loop FMU with an OpenFOAMderived reduced-order model of a 3×3 lead-bismuth eutectic rod bundle through a Python driver. Four scenarios are evaluated: baseline operation, single-rod local overpower, and local overpower combined with mild or moderate loss of flow. The coupled model predicts bundleaverage outlet temperature, nine rod temperatures, hotspot temperature, and limiting-rod location. The hotspot-tooutlet temperature difference increases from 0.803 K in the baseline case to 18.274 K under local overpower with moderate flow reduction, while the hotspot migrates to the south-east rod. OpenFOAM calculations at 15 s and 40 s identify the same limiting rod. All four 60 s scenarios require 0.319 s of online execution time, demonstrating faster-than-real-time local accident assessment.amfc2026-181-an-fmi-based-multiscale-workflow-for-multi-scenario-local-accident-assessment-in-lbe-reactor-rod-bundlesXinheng ZhaoGuangliang ChenMinjun PengXiang ZhangenfalsePaper in PDF: An FMI-Based Multiscale Workflow for Multi-Scenario Local Accident Assessment in LBE Reactor Rod Bundleshttps://modelica.simtek.cc/amfc2026/talk/KYXWXP/https://modelica.simtek.cc/amfc2026/talk/KYXWXP/feedback/FMI & MBSE (R2001)High-Pressure Liquid–Gas Ejector Models for System Simulation in ModelicaOral presentation2026-09-21T16:00:00+08:0016:0000:25System simulation of high-pressure liquid–gas ejectors requires compact pressure–flow representations. This work compares two reduced-order models of the same fixed-geometry water–methane ejector against a 21-point CFD pressure-response database: an effective-area onedimensional model and an explicit quadratic surrogate derived from the CFD data. With leave-one-out errors normalized by the global CFD response range, the surrogate gives NRMSEs of 1.19%, 0.80%, and 0.71% for primary flow, secondary flow, and entrainment ratio; the 1D model gives 1.31%, 5.49%, and 5.52%. Both models are implemented in Modelica and exported through FMI 2.0. The surrogate CS FMU matches the native Modelica scheduling trajectory to machine precision. Coupled to a first-order back-pressure actuator, the surrogate supports inverse entrainment-ratio scheduling with settling times of 7.0 and 7.6 s. The two formulations therefore offer complementary choices for reusable system simulation.amfc2026-185-high-pressure-liquid-gas-ejector-models-for-system-simulation-in-modelicaJinglong ZhouJie ChengenfalsePaper in PDF: High-Pressure Liquid–Gas Ejector Models for System Simulation in Modelicahttps://modelica.simtek.cc/amfc2026/talk/PPUWW9/https://modelica.simtek.cc/amfc2026/talk/PPUWW9/feedback/FMI & MBSE (R2001)An FMI Co-Simulation Framework for Day-ahead Optimization of District Cooling Systems Based on ModelicaOral presentation2026-09-21T16:25:00+08:0016:2500:25The high penetration of renewable energy increases grid volatility, necessitating flexible demand-side resources such as District Cooling Systems (DCS) with thermal storage. However, the multi-timescale coupling of equipment, pipelines, and buildings makes it difficult to integrate high-fidelity physical models with optimization. This paper proposes an FMI-based co-simulation framework for day-ahead DCS optimization. A fullchain Modelica model is exported as an FMI 2.0 CoSimulation FMU and coupled with MATLAB optimizers. The main contribution is a standardized FMU-in-the-loop workflow for day-ahead mixed-integer nonlinear programming (MINLP) scheduling with discrete unit commitment, together with a systematic quantification of the accuracy-runtime trade-off induced by FMI communication settings. Sensitivity analysis identifies a practical configuration for the investigated case, while a comparison of the genetic algorithm (GA) and surrogateopt reveals the solution-quality and computation-time trade-off. Compared with storagepriority control, the cost-optimization scenario reduces operating costs by 0.97%, the energy-optimization scenario reduces electricity consumption by 4.58%, and the carbon-optimization scenario reduces CO2 emissions by 4.20%. The physical feasibility of the generated schedules is verified by dynamic FMU simulation.amfc2026-188-an-fmi-co-simulation-framework-for-day-ahead-optimization-of-district-cooling-systems-based-on-modelicaWeidong MaFeng ShaXiang LiRui GaoenfalsePaper in PDF: An FMI Co-Simulation Framework for Day-ahead Optimization of District Cooling Systems Based on Modelicahttps://modelica.simtek.cc/amfc2026/talk/NELTYC/https://modelica.simtek.cc/amfc2026/talk/NELTYC/feedback/Energy (R2002)T3S1-Session ChairChair2026-09-21T11:39:00+08:0011:3900:01This session is chaired by:amfc2026-241-t3s1-session-chairGuangliang Chenenfalsehttps://modelica.simtek.cc/amfc2026/talk/ZBUU77/https://modelica.simtek.cc/amfc2026/talk/ZBUU77/feedback/Energy (R2002)Application of Modelica in Developing a Nuclear Power Transient Identification Module(IUP)Oral presentation2026-09-21T11:40:00+08:0011:4000:20Nuclear power plants must identify and analyze diverse transient events from long-term (often decades of) historical operating data to support unit life-extension and decommissioning assessments. Traditionally, this work relied on analysts manually labeling events against empirical rules, labor-intensive, slow, and prone to subjective misjudgment.
Our approach. We reformulated transient-determination rules as algorithmic logic and implemented them in MUUSE, our in-house Modelica-based graphical simulation and modeling platform. Leveraging Modelica's mature libraries, hierarchical modeling paradigm, and standardized encapsulation, we built, tested, and optimized the logic using standard and custom libraries, realizing identification, classification, key-event-information extraction, and alarming. To integrate with heterogeneous platforms, the logic was packaged as a standalone FMU for unified scheduling by the algorithm scheduling platform. Because the logic is authored in MUUSE on Windows yet must run on a Linux-based algorithm scheduling platform, the FMU is repackaged for the Linux host.
Takeaways. The deployment markedly improves transient-determination efficiency and consistency of results, demonstrating how Modelica and FMI enable industrial-scale operational-data mining. Cross-platform validation shows consistent trajectory agreement between the Windows-host and Linux-host FMU across representative transients, demonstrating that the open FMI sources mechanism enables reproducible, cross-platform deployment of Modelica models on domestic Linux stacks. The talk will present the modeling methodology, FMU encapsulation workflow, and lessons learned from deployment.
**Keywords:** Modelica; FMU; transient identification; operational data mining; nuclear poweramfc2026-164-application-of-modelica-in-developing-a-nuclear-power-transient-identification-module-iupKuolei FengWei QienfalseAbstracthttps://modelica.simtek.cc/amfc2026/talk/NAA3GM/https://modelica.simtek.cc/amfc2026/talk/NAA3GM/feedback/Energy (R2002)Application of MBSE in the Design of a Small Modular Reactor Intermediate Circuit SystemOral presentation2026-09-21T12:05:00+08:0012:0500:25In this application, based on the CGN_NHR1.0 model library, an overall simulation model of the intermediate loop of the low-temperature heating reactor was built using a visual drag-and-drop modeling method. The multi-domain coupling and collaborative simulation
verification were carried out using the MBSE method. The typical working condition selected was the step reduction of rated full power (100% FP) to 70% FP load disturbance. Oriented by the SysML functional requirements, a joint simulation framework for reactor physics, thermal-hydraulic, and control systems was constructed. The simulation conditions were divided into two stages: the first 100 seconds of the system maintained a steady state operation, and at the 100-second moment, a 70% FP step load reduction disturbance was applied. The simulation results showed that the various thermal-hydraulic parameters of the intermediate loop could return to stability within 450 seconds after the disturbance occurred, and the dynamic response performance of the system control was better than the design-expected indicators. Research has confirmed that the SysML-Modelica joint modeling approach is applicable to the design and analysis of complex nuclear energy systems. The "requirement driven - functional mapping - dynamic verification" closed-loop verification mode constructed in this study can provide reliable theoretical and methodological support for the digital design and development of nuclear energy equipment.amfc2026-167-application-of-mbse-in-the-design-of-a-small-modular-reactor-intermediate-circuit-systemMingliang ChenCuiting PengSixin LiuenfalseAbstractPersonal PhotoSlideshttps://modelica.simtek.cc/amfc2026/talk/FWVNUG/https://modelica.simtek.cc/amfc2026/talk/FWVNUG/feedback/Energy (R2002)T3S2-Session ChairChair2026-09-21T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-246-t3s2-session-chairXiaomeng Dongenfalsehttps://modelica.simtek.cc/amfc2026/talk/GHRQVN/https://modelica.simtek.cc/amfc2026/talk/GHRQVN/feedback/Energy (R2002)A Modelica-based Fluid Library for Small Modular Reactors: Design and VerificationOral presentation2026-09-21T13:50:00+08:0013:5000:25While Modelica has become a standard for multi-domain modeling in various complex engineering fields, professional and systematic model libraries specifically tailored for the nuclear power sector remain scarce. To bridge this gap, this paper presents the development of a specialized Modelica fluid component library for Small Modular Reactor (SMR) systems. Serving as a critical module within the broader CGN_NHR library, this framework bridges the gap between fundamental fluid mechanisms, equipment-level modeling, and system-level assembly. We detail the library’s architecture, design philosophy, and the mathematical formulation of its core components. To verify its validity and reliability, a systemlevel model was constructed and benchmarked against the industry-standard RELAP5 model. Comparative results confirm that the proposed library offers high efficiency and accuracy, establishing a robust technical foundation for the rapid design, simulation, and verification of nuclear power systems.amfc2026-171-a-modelica-based-fluid-library-for-small-modular-reactors-design-and-verificationSixin LiuHao HongHong LvenfalsePaper in PDF: A Modelica-based Fluid Library for Small Modular Reactors: Design and Verification照片https://modelica.simtek.cc/amfc2026/talk/BDKE8N/https://modelica.simtek.cc/amfc2026/talk/BDKE8N/feedback/Energy (R2002)Research on Dynamic Simulation of Nuclear Power Steam Generator Based on ModelicaOral presentation2026-09-21T14:15:00+08:0014:1500:25As the pivotal equipment connecting the primary and secondary loops of pressurized water reactor nuclear power plants, the dynamic thermo-hydraulic characteristics of the nuclear steam generator are directly related to the safe and stable operation of the unit. However, traditional modeling approaches suffer from poor versatility and low efficiency, making it difficult to meet the R&D demands for diverse application scenarios and iterative optimization. Addressing these bottlenecks, this paper proposes a modular modeling and dynamic simulation method for nuclear steam generators based on the Modelica language. First, based on a hierarchical modeling approach, component-level models covering plena, pipes, heat structures, steam-water separators, and feedwater plena are constructed to achieve free configuration and visual simulation modeling. Second, according to the operating principles and structure of the nuclear steam generator, a complete dynamic simulation model is assembled using these component-level models. Finally, the established model is validated against the RELAP5 code to verify the effectiveness and reliability of the proposed method. The results indicate that the Modelica-based steam generator simulation model combines high accuracy with high efficiency, providing significant technical support for the rapid design and verification of nuclear power systems.amfc2026-175-research-on-dynamic-simulation-of-nuclear-power-steam-generator-based-on-modelicaHao HongSixin LiuHong LvenfalsePaper in PDF: Research on Dynamic Simulation of Nuclear Power Steam Generator Based on Modelicapersonal photohttps://modelica.simtek.cc/amfc2026/talk/BGZSJF/https://modelica.simtek.cc/amfc2026/talk/BGZSJF/feedback/Energy (R2002)Modelica-based Modeling and Simulation of an SPWR Nuclear Steam Supply SystemOral presentation2026-09-21T14:40:00+08:0014:4000:25Small pressurized water reactors (SPWRs) have emerged as one of the most promising nuclear energy technologies, as they can simultaneously meet current decarbonization goals and the demands of flexible load operation. The nuclear steam supply system (NSSS) is the heart of an SPWR. Traditional text-based modeling approaches lack sufficient modularity, depth, and visualization capabilities. Modelica, which features equation-based, non-causal, object-oriented, and hierarchical modeling, enables rapid, visual, and reusable simulation of nuclear energy systems. Leveraging these advantages, this paper developed a modular, hierarchical dynamic simulation model library to support the rapid modeling and optimal design of SPWRs. Based on the system topology, an SPWR NSSS simulation model was quickly constructed using the developed library, and a corresponding control system was also developed. The accuracy of the modeling method is verified through typical load-change transients.amfc2026-179-modelica-based-modeling-and-simulation-of-an-spwr-nuclear-steam-supply-system/media/amfc2026/submissions/BBYNW8/image_Inj5pc8.webpQingfeng JiangPengfei WangentruePaper in PDF: Modelica-based Modeling and Simulation of an SPWR Nuclear Steam Supply Systemhttps://modelica.simtek.cc/amfc2026/talk/BBYNW8/https://modelica.simtek.cc/amfc2026/talk/BBYNW8/feedback/Energy (R2002)T3S3-Session ChairChair2026-09-21T15:34:00+08:0015:3400:01This session is chaired by:amfc2026-251-t3s3-session-chairXiaomeng Dongenfalsehttps://modelica.simtek.cc/amfc2026/talk/FYMH97/https://modelica.simtek.cc/amfc2026/talk/FYMH97/feedback/Energy (R2002)Modelica-Based Parametric Analysis of Steady-State and Transient Thermal-Hydraulic Characteristics of PCHEs in SCO2 Brayton CyclesOral presentation2026-09-21T15:35:00+08:0015:3500:25Printed circuit heat exchangers (PCHEs) are key components in supercritical carbon dioxide (S-CO2) Brayton cycles because of their compact structure and high heat transfer capability. However, existing studies have mainly focused on steady-state structural optimization or isolated component analysis, while the influence of geometric parameters on thermal-hydraulic responses under system-level transient disturbances remains insufficiently understood. In this study, a Modelica-based dynamic simulation framework is developed for the system-level analysis of recuperators in an S-CO2 Brayton cycle. A one-dimensional dynamic PCHE model is established and validated against benchmark data, with a maximum relative error of 0.87%. A continuously varying heat sink temperature ranging from 22 ° C to 26 ° C is introduced as a representative offdesign disturbance. The effects of channel diameter and channel length on heat transfer coefficient and pressure drop are investigated under both design reference and transient operating conditions. The results show that smaller channels and longer flow paths enhance heat transfer performance but introduce larger pressure-drop penalties under the design reference condition. Under transient heat sink temperature disturbances, smalldiameter and long-channel configurations exhibit stronger thermal-hydraulic response variations than under steadystate conditions. These results indicate that parameter trends obtained under steady-state conditions may not directly represent transient thermal-hydraulic behavior under off-design operation. The proposed framework provides an efficient approach for preliminary parametric assessment and system-level transient analysis of recuperators in S-CO2 Brayton cycles.amfc2026-182-modelica-based-parametric-analysis-of-steady-state-and-transient-thermal-hydraulic-characteristics-of-pches-in-sco2-brayton-cyclesHao XuYifan XuYuandong ZhangMinjun PengGenglei XiaenfalsePDF: Modelica-Based Parametric Analysis of Steady-State and Transient Thermal-Hydraulic Characteristics of PCHEs in SCO2 Brayton Cycleshttps://modelica.simtek.cc/amfc2026/talk/QEWZYQ/https://modelica.simtek.cc/amfc2026/talk/QEWZYQ/feedback/Energy (R2002)Analysis of Charging and Discharging Control Schemes for S-CO₂ Reactor Systems Based On ModelicaOral presentation2026-09-21T16:00:00+08:0016:0000:25To be comeamfc2026-186-analysis-of-charging-and-discharging-control-schemes-for-s-co-reactor-systems-based-on-modelicaYuchen NiuYuandong ZhangMinjun PengGenglei XiaChenyang Wangenfalsehttps://modelica.simtek.cc/amfc2026/talk/KUG88X/https://modelica.simtek.cc/amfc2026/talk/KUG88X/feedback/Energy (R2002)Modelica-Based Dynamic Analysis of an HTGR Combined Cycle System under Negative Reactivity Disturbance: Comparison of Constant-Pressure and Sliding-Pressure OperationOral presentation2026-09-21T16:25:00+08:0016:2500:25Based on cascade energy utilization, a hightemperature gas-cooled reactor (HTGR) combined cycle offers an efficient energy conversion scheme, but its complex system configuration, strong parameter coupling, and dynamic response mechanisms require systematic analysis. In this paper, a dynamic model of an HTGR combined cycle system is developed in Modelica and validated by comparison with reference results. A negative reactivity disturbance of -300 pcm is then introduced to investigate the system transient response, with further comparison between constant-pressure and sliding-pressure operation. The results show that the steam generator heat-transfer rate decreases by 10.8% under sliding-pressure operation, less than the 15.1% decrease under constant-pressure operation, indicating better heat-transfer matching. However, steam turbine power decreases by 15.8%, compared with 3.8% under constant-pressure operation. Therefore, constantpressure operation is more suitable for maintaining main steam pressure and steam turbine power, whereas sliding-pressure operation is more appropriate for partial-load heat-transfer matching and load following.amfc2026-189-modelica-based-dynamic-analysis-of-an-htgr-combined-cycle-system-under-negative-reactivity-disturbance-comparison-of-constant-pressure-and-sliding-pressure-operationHaojie QuMing DingenfalsePDF: Modelica-Based Dynamic Analysis of an HTGR Combined Cycle System under Negative Reactivity Disturbance: Comparison of Constant-Pressure and Sliding-Pressure Operationhttps://modelica.simtek.cc/amfc2026/talk/X8DEBQ/https://modelica.simtek.cc/amfc2026/talk/X8DEBQ/feedback/(Electric) Mobility & Buildings (R2003)T4S1-Session ChairChair2026-09-21T11:39:00+08:0011:3900:01This session is chaired by:amfc2026-242-t4s1-session-chairYan Zhaoenfalsehttps://modelica.simtek.cc/amfc2026/talk/CMPR8G/https://modelica.simtek.cc/amfc2026/talk/CMPR8G/feedback/(Electric) Mobility & Buildings (R2003)Automobile Model Digital Engineering PracticeOral presentation2026-09-21T11:40:00+08:0011:4000:25With the rapid growth of electrification, intelligence, and customization demands in the automotive industry, parallel development of multiple vehicle models imposes higher requirements on the re-usability, flexibility, and development efficiency of digital engineering models. Based on the practical application of automotive model digital engineering, this paper proposes an overall framework for automotive research and development. By integrating key technologies such as modular hierarchical architecture design, parametric configuration-driven modeling, cross-domain data collaboration management, and automated verification systems, the framework establishes an end-to-end solution spanning requirement analysis, model development, simulation validation, and iterative optimization. Based on a specific vehicle model system development case, this framework achieved a ~50% reduction in system development cycles, ~30% fewer quality issues, and 50% higher model development efficiency. It enables rapid model reuse and facilitates swift derivative design across various power trains (fuel, electric, hybrid) and configurations. The research provides methodological insights for efficient digital engineering systems in automotive industry.amfc2026-223-automobile-model-digital-engineering-practiceenfalsePaper in PDF: Automobile Model Digital Engineering Practicehttps://modelica.simtek.cc/amfc2026/talk/PZM3MA/https://modelica.simtek.cc/amfc2026/talk/PZM3MA/feedback/(Electric) Mobility & Buildings (R2003)22-DOF Full-Coupled Vehicle Dynamics Modeling and Control Based on ModelicaOral presentation2026-09-21T12:05:00+08:0012:0500:25A 22-DOF vehicle dynamics model is developed in Modelica and implemented in MWorks Sysplorer 2025a.The generalized coordinates comprise six sprung-body motions, twelve translational motions of the four unsprung masses, and four wheel-spin motions. The physical plant is formulated as a coupled differential-algebraic equation system: the body, suspension, unsprung mass, wheel, tire, and road components are connected through custom acausal physical connectors, whereas steering and wheel-torque commands are retained only as external causal boundaries. The model is assessed using acceleration, braking, and steering-step datasets from MWorks, a 22-DOF Simulink model, and road tests. The threesource comparison demonstrates consistent overall dynamic trends and close agreement in key performance indicators, particularly in braking-distance prediction and steady steering response, thereby validating the effectiveness of the proposed acausal 22-DOF modeling framework.amfc2026-168-22-dof-full-coupled-vehicle-dynamics-modeling-and-control-based-on-modelicaYufan GuoZehua WuBingtao RenShichun YangenfalsePaper in PDF: 22-DOF Full-Coupled Vehicle Dynamics Modeling and Control Based on Modelicahttps://modelica.simtek.cc/amfc2026/talk/QHWTVG/https://modelica.simtek.cc/amfc2026/talk/QHWTVG/feedback/(Electric) Mobility & Buildings (R2003)T4S2-Session ChairChair2026-09-21T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-247-t4s2-session-chairRui Gaoenfalsehttps://modelica.simtek.cc/amfc2026/talk/UDWNLF/https://modelica.simtek.cc/amfc2026/talk/UDWNLF/feedback/(Electric) Mobility & Buildings (R2003)Modelica-Based Modeling of PMSM Control MethodsOral presentation2026-09-21T13:50:00+08:0013:5000:25The strong coupling, nonlinearity, and multi-physicaldomain nature of permanent magnet synchronous motor (PMSM) drive systems pose significant challenges to unified modeling and control-strategy verification. To address these challenges, this paper proposes a system-level modeling frame-work for PMSM control methods based on the Modelica language. By exploiting Modelica's object-oriented, acausal, and equation-based modeling features, the proposed framework provides modular encapsulation of the motor, inverter, modulation strategy, and thermal network under a unified physical-connection architecture. First, a basic component library is developed, including the d– q current controller, the Clark/Park transformations, and space-vector pulse-width modulation (SVPWM). On this basis, a PMSM vector control model is established, achieving the approximate decoupled control of the d- and q-axis currents. Furthermore, equivalent thermal models are constructed for different cooling configurations to characterize the temperature rise caused by copper, iron, and permanent-magnet losses. Finally, a maximum-torque-per-ampere (MTPA) control model is introduced to improve current utilization and overall system efficiency. Simulation results verify the modularity, scalability, and reusability of the proposed framework, and demonstrate its capability to serve as a unified platform for multi-physical-domain co-simulation and control-strategy verification of PMSM drive systems. Keywords—Permanent magnet synchronous motor (PMSM), Modelica, vector control, maximum torque per ampere (MTPA), multi-physical-domain modeling.amfc2026-172-modelica-based-modeling-of-pmsm-control-methodsYuang YangHongyou ZhongYang QienfalsePaper in PDF: Modelica-Based Modeling of PMSM Control Methodshttps://modelica.simtek.cc/amfc2026/talk/C9UW7A/https://modelica.simtek.cc/amfc2026/talk/C9UW7A/feedback/(Electric) Mobility & Buildings (R2003)Hardware-in-Loop Testing of an Induction Machine Drive SystemOral presentation2026-09-21T14:15:00+08:0014:1500:25Induction motors are highly preferred in electric vehicle (EV) applications due to robustness. This paper presents a comprehensive study on the vector control of an induction motor for EV applications. To achieve precise closed-loop speed and torque regulation, the mechanical angular speed of the rotor is obtained through a quadrature encoder. In addition to this, a magnetic flux estimator is implemented to provide the real-time flux information. The mathematical model and control for a motor drive system are established. To validate the effectiveness of the typical vector control, a real-time Hardware-in-the-Loop (HIL) test is conducted, where the power circuit and control strategies are both simulated by the PLECS RT Box. The HIL results verify the stability, dynamic response, and overall effectiveness of the proposed vector control scheme for EV drivetrains.amfc2026-176-hardware-in-loop-testing-of-an-induction-machine-drive-systemYang QiHongyou ZhongYuang YangenfalsePaper in PDF: Hardware-in-Loop Testing of an Induction Machine Drive Systemhttps://modelica.simtek.cc/amfc2026/talk/XCCF77/https://modelica.simtek.cc/amfc2026/talk/XCCF77/feedback/(Electric) Mobility & Buildings (R2003)T4S3-Session ChairChair2026-09-21T15:34:00+08:0015:3400:01This session is chaired by:amfc2026-252-t4s3-session-chairStefan Wischhusenenfalsehttps://modelica.simtek.cc/amfc2026/talk/CU8JQZ/https://modelica.simtek.cc/amfc2026/talk/CU8JQZ/feedback/(Electric) Mobility & Buildings (R2003)Dynamic Load Calculation and Analysis of Multi-Zone RC Thermal Network Based on ModelicaOral presentation2026-09-21T15:35:00+08:0015:3500:25Traditional air conditioning load calculation methods suffer from overestimated results in steady-state computation, excessive computational burden in unsteady-state calculation, and low efficiency in numerical analysis. Such defects make it difficult to quickly and accurately satisfy the control requirements of multi-zone air conditioning loads under complex dynamic boundaries. To solve these problems, this paper proposes applying the Modelica-based RC thermal network heat transfer model to air conditioning load calculation, and carries out dynamic load simulation and verification with the multi-zone structure in the control cabin of an underwater vehicle as the research object. The results show that the proposed method can realize multi-zone dynamic load solution under dynamic boundaries. The dynamic response accuracy is verified against measured loads under surface and underwater navigation conditions. The average relative error rates of the total load are only 3.17% and 2.90%, respectively, and the maximum error rate is less than 10%. The model has high accuracy and provides an effective approach for multi-zone dynamic load calculation.amfc2026-183-dynamic-load-calculation-and-analysis-of-multi-zone-rc-thermal-network-based-on-modelicaXuyang ZhongQiuyuan ZhuAimin ZhouHongqi ShiXinyu ShuXinhua XuenfalsePaper in PDF: Dynamic Load Calculation and Analysis of Multi-Zone RC Thermal Network Based on Modelicahttps://modelica.simtek.cc/amfc2026/talk/PHKVVC/https://modelica.simtek.cc/amfc2026/talk/PHKVVC/feedback/(Electric) Mobility & Buildings (R2003)Modelling of a Coupled PV and ITES Air Conditioning System Based on MWorks and its Operation Analysis for a Large High-speed Railway StationOral presentation2026-09-21T16:00:00+08:0016:0000:25Large high-speed railway stations are major energy consumers with enormous cooling loads. Integrating distributed renewables such as photovoltaics (PV) offers new prospects for building decarbonization but brings challenges to system dispatch and economic optimization. This study evaluates operational modes of a coupled PV and ice thermal energy storage (ITES) air conditioning system to maximize economic benefits. A physical simulation model of the PV+ITES system including PV module, chiller module, pump module, ice storage tank module etc. was built in MWorks. Three operation modes are considered, and the yearly operation performances were simulated. Quantitative comparison shows Mode 3 (combining PV direct supply and nighttime off-peak ice storage via TOU tariffs) performs optimally, with an annual net operating cost of 2.852 million CNY and 27.53% cost saving vs. Mode 1 (independent PV grid-connection), providing practical references for large transportation hubs.amfc2026-224-modelling-of-a-coupled-pv-and-ites-air-conditioning-system-based-on-mworks-and-its-operation-analysis-for-a-large-high-speed-railway-stationenfalsePaper in PDF: Modelling of a Coupled PV and ITES Air Conditioning System Based on MWorks and its Operation Analysis for a Large High-speed Railway Stationhttps://modelica.simtek.cc/amfc2026/talk/YRSAQP/https://modelica.simtek.cc/amfc2026/talk/YRSAQP/feedback/(Electric) Mobility & Buildings (R2003)Control Strategy for a High-Efficiency Refrigeration Plant Based on Dymola Simulation: A Case Study of a Xiamen Airlines Support FacilityOral presentation2026-09-21T16:25:00+08:0016:2500:25The operational control strategy of a refrigeration plant directly impacts its annual energy efficiency. Conventional control logic, based on static design conditions or time-scheduled fixed parameters, cannot effectively adapt to continuous fluctuations in cooling load and meteorological conditions. This paper employs a model-based Dymola environment to develop a fullsystem model of a high-efficiency refrigeration plant and proposes a dynamic control strategy driven by hourly cooling load and outdoor meteorological parameters. Based on the hourly cooling load profile of a public building in a hot-summer warm-winter climate zone, the proposed strategy is simulated and compared against a conventional baseline. The results indicate that the proposed strategy achieves an energy efficiency ratio (EER) of 6.02 over the cooling season, representing a 19.4% improvement compared to the baseline strategy. The proposed methodology offers a practical reference for control strategy optimization in chiller plants of large public facilities with fluctuating cooling loads, such as airports and shopping malls.amfc2026-190-control-strategy-for-a-high-efficiency-refrigeration-plant-based-on-dymola-simulation-a-case-study-of-a-xiamen-airlines-support-facilityHaoran WuYuhui WangHejiang SunenfalsePaper in PDF: Control Strategy for a High-Efficiency Refrigeration Plant Based on Dymola Simulation: A Case Study of a Xiamen Airlines Support Facilityhttps://modelica.simtek.cc/amfc2026/talk/YPFXCM/https://modelica.simtek.cc/amfc2026/talk/YPFXCM/feedback/Main hall (R1016)15 Years Functional Mock-up Interface – a Journey from FMI 1.0 to 3.0 and the Road AheadKeynote2026-09-22T09:00:00+08:0009:0000:45Over the last fifteen years, the Functional Mock-up Interface (FMI) has evolved by addressing real industrial needs for simulation interoperability. What began as a solution mainly for the automotive industry has now firmly established itself as the de-facto industrial standard for tool-independent model exchange and co-simulation across numerous domains. This keynote will trace the journey of FMI from its initial concepts in version 1.0 to the powerful ecosystem of today. The release of FMI 3.0 marks a pivotal milestone in this journey, enabling major improvements that redefine the boundaries of system simulation. This new version introduces advanced co-simulation capabilities, facilitates the development and deployment of virtual ECUs, and seamlessly integrates FMI models into complex optimization and machine learning workflows. These enhancements empower engineers to tackle new challenges in digitalization and automation with greater efficiency and fidelity. Looking to the road ahead, the FMI standard continues to expand through the development of Layered Standards. These domain-specific extensions, built on top of the core FMI framework, enable further improvements and unlock entirely new use cases for virtual engineering. This talk will provide a comprehensive look at FMI’s past, present, and future, celebrating its role as a cornerstone of modern model-based development.amfc2026-225-15-years-functional-mock-up-interface-a-journey-from-fmi-1-0-to-3-0-and-the-road-aheadChristian BertschenfalseSlideshttps://modelica.simtek.cc/amfc2026/talk/ECHQNF/https://modelica.simtek.cc/amfc2026/talk/ECHQNF/feedback/Main hall (R1016)Xu's Modeling Method: A Foundation for Hydraulic Digital Twins and Intelligent ComponentsKeynote2026-09-22T09:45:00+08:0009:4500:30The Xu’s Modeling Method, originating from foundational research for the HyPneu software at Oklahoma State University in the 1980s, provides an intuitive framework for hydraulic system modeling. Its core premise is to align the modeling process with engineers’ cognitive paradigms. A key breakthrough was its algorithmic solution to Differential-Algebraic Equation (DAE) problems, validated through the PERSIM software developed under China’s “Seventh Five-Year Plan” national project. The method enables model construction directly from hydraulic schematics by applying Kirchhoff’s law at junctions, reducing all components to two fundamental sub-models: hydraulic resistance (throttle) and hydraulic capacitance (cylinder). This yields an implicit state equation (a DAE). The Xu’s Algorithm solves this via a unified dynamic-static simulation approach, using the Newton-Raphson method iteratively to compute static states and dynamic derivatives over time steps. In the Digital Twin era, the method finds new relevance. Its implicit state equation serves as a potential mechanism model for digital twins, contingent on solving the inverse problem—a current research focus that may incorporate Artificial Intelligence. Additionally, the method offers a theoretical basis for creating “Hydraulic Intelligent Components,” guiding the design of simpler, more efficient constructions from first principles. This positions it as an advancement over the classic Hydraulic Resistance Systematic Theory. Practically, the method is being applied to develop dedicated simulation and digital twin software for a hydraulic robot system manufactured in Shenzhen, demonstrating its ongoing industrial applicability.amfc2026-226-xu-s-modeling-method-a-foundation-for-hydraulic-digital-twins-and-intelligent-componentsYangzeng XuenfalseSlideshttps://modelica.simtek.cc/amfc2026/talk/E87ZG7/https://modelica.simtek.cc/amfc2026/talk/E87ZG7/feedback/Main hall (R1016)XRG® Amber – The new modular engineering platform for advanced system modelling, simulation and analysisInvited Talk2026-09-22T10:35:00+08:0010:3500:25With Amber, we are opening a new and promising chapter in XRG’s long list of software developments.
“Simulation for everyone” will now be available as a completely newly designed modern standalone tool integrating known but also new features into a single handy application for advanced system simulation, result analysis, and model optimization.
In the vendor session, we will demonstrate selected features and give an outlook for the planned modules, which will also involve surrogate model creation.amfc2026-227-xrg-amber-the-new-modular-engineering-platform-for-advanced-system-modelling-simulation-and-analysisStefan Wischhusenenfalsehttps://modelica.simtek.cc/amfc2026/talk/AKVSLH/https://modelica.simtek.cc/amfc2026/talk/AKVSLH/feedback/Modelica Technology & AI (R1001)Dassault Systemes-REAL-TIME INTERACTIVESYSTEM ENGINEERINGEXPERIENCEInvited Talk2026-09-22T10:35:00+08:0010:3500:25Dassault Systèmes is the PLM world leader and a key player of the MBSE (Model Based System Engineering) implementation in the industry. This presentation introduces the Proof of Concept (PoC) project, developed through collaboration between the University of Adelaide (UoA) and Dassault Systèmes, introduces a Virtual Twin Submarine Drone simulator. Following the Model-Based Systems Engineering (MBSE) methodology aligned with the V-cycle development process, the framework spans from stakeholder requirements and specifications to system and subsystem definition. System operational analysis is captured in CATIA Magic, while dynamic multiphysics modeling is executed in Dymola using Modelica. Rather than relying on predefined animations, the drone's underwater movements are driven by real simulation data, enabling real-time interactive control, scenario testing, and pilot training. Integrated with 3DEXCITE for photorealistic 3D rendering, this immersive simulator allows stakeholders to interactively explore and validate design concepts before physical prototyping. By combining systems engineering with virtual rendering, this methodology reduces costly physical testing and de-risks development early in the design cycle.amfc2026-261-dassault-systemes-real-time-interactivesystem-engineeringexperienceDaphne WUenfalsehttps://modelica.simtek.cc/amfc2026/talk/TGDR73/https://modelica.simtek.cc/amfc2026/talk/TGDR73/feedback/Modelica Technology & AI (R1001)T1S4-Session ChairChair2026-09-22T11:04:00+08:0011:0400:01This session is chaired by: Prof. Liu Yanfang.
She is the professor at the school of transportation sciencen and technology of Beihang University. She received her Ph.D. in Aerospace Manufacturing Engineering from Beihang University. Her research focuses on intelligent connected vehicles and their applications. She has hosted multiple national level projects such as the National Natural Science Foundation of China, and published more than 109 academic papers. She received multiple scientific and technological awards, such as the First Prize for Technological Invention in the Mechanical Industry, the Special Prize for Technological Progress in the Chinese Mechanical Industry, and the First Prize for Science and Technology in the Chinese Automotive Industry.amfc2026-253-t1s4-session-chair/media/amfc2026/submissions/FMKRG7/image_Hpouup6.webpYanfang Liuenfalsehttps://modelica.simtek.cc/amfc2026/talk/FMKRG7/https://modelica.simtek.cc/amfc2026/talk/FMKRG7/feedback/Modelica Technology & AI (R1001)Theoretical Reconstruction of Xu's Digital Twin Modeling Method and Its Integration with the Modelica EnvironmentOral presentation2026-09-22T11:05:00+08:0011:0500:25Digital twin technology is central to Cyber-Physical Systems, yet purely data-driven approaches demand high computational resources and expensive validation. Xu's Modular Modeling Method for Performance Parameters of Large-scale Hydraulic Systems ("Xu's Modeling Method")—a physical modeling technology from the 1980s implemented in the PERSIM software—shares structural similarities with Modelica, though their algorithms differ. We extend Xu's method to mechanismbased digital twin modeling, creating a grey-box approach that combines physical models with data. Using Modelica, we implement XAP (Xu's Algorithm Package), unifying simulation and digital twin modeling. A key contribution is an inverse operation framework that recovers physical fault indicators from measured step-response data. Numerical validation shows that identifiable fault parameters—single, dual, and crosschamber multi-parameter combinations—are recovered with errors below 1%, and the identifiability limit of joint estimation within a chamber pair is characterized. The resulting grey-box method offers interpretable, efficient digital twin modeling for motion control systems.amfc2026-191-theoretical-reconstruction-of-xu-s-digital-twin-modeling-method-and-its-integration-with-the-modelica-environmentLei XiangliQuan LongXu YangzengWang QixinLi ZepengenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/PTKHHT/https://modelica.simtek.cc/amfc2026/talk/PTKHHT/feedback/Modelica Technology & AI (R1001)Linearizing Position Constraints in Rigid Multibody Dynamics Using Direction Cosine Coordinates: Method and Modelica ModelsOral presentation2026-09-22T11:30:00+08:0011:3000:25This paper introduces a formulation for Modelica MultiBody models in which angular coordinates in closed-loop systems are replaced by direction cosine coordinates, and the orthonormality violations of the introduced direction cosine coordinates are controlled by Baumgarte-type correction terms at the derivative level. Thus, the position constraints are transformed into linear systems, while the acceleration-level and velocity-level equations remain linear. In favorable cases, the Jacobian becomes constant and dynamic switching of dummy states at the position level is no longer required. Numerical experiments indicate good stability and modest speed improvements.amfc2026-195-linearizing-position-constraints-in-rigid-multibody-dynamics-using-direction-cosine-coordinates-method-and-modelica-modelsYan ZhaoLiping ChenenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/9VGK97/https://modelica.simtek.cc/amfc2026/talk/9VGK97/feedback/Modelica Technology & AI (R1001)Learning the Missing Physics Locally with Modelica and Wolfram LanguageOral presentation2026-09-22T11:55:00+08:0011:5500:25This paper introduces State-Invariant Hybrid Models (SIHM), a subset of hybrid physics-machine-learning models in which the state vector remains defined by the first-principles Modelica model, while learned components contribute missing physics locally. We qualitatively contrast global residual learning at system level with local residual learning at component level, arguing that local learning better matches the acausal modeling paradigm of Modelica. A tool-agnostic workflow is proposed in which a replaceable dummy component marks the suspected location of missing physics, while compiler-derived structural information identifies the connected states and variables. The workflow is then illustrated in the Wolfram ecosystem on two models to demonstrate the approach's modularity. The results indicate that local SIHM components recover targeted missing effects while preserving the state selection of the original model.amfc2026-199-learning-the-missing-physics-locally-with-modelica-and-wolfram-languageClément CoïcAnkit NaikenfalsePaper in PDFhttps://modelica.simtek.cc/amfc2026/talk/XB79KT/https://modelica.simtek.cc/amfc2026/talk/XB79KT/feedback/Modelica Technology & AI (R1001)T1S5-Session ChairChair2026-09-22T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-254-t1s5-session-chairDirk Zimmerenfalsehttps://modelica.simtek.cc/amfc2026/talk/8UZKSP/https://modelica.simtek.cc/amfc2026/talk/8UZKSP/feedback/Modelica Technology & AI (R1001)Reconstruction of Complex Thermodynamic Equations of State in Modelica and Its Application in Chemical Process SimulationOral presentation2026-09-22T13:50:00+08:0013:5000:25The transition to intelligent process industries requires high-fidelity digital twins capable of dynamic transient simulation. While Modelica excels in multi-physical modeling, standard fluid libraries often struggle with complex thermodynamic phase equilibria, frequently
encountering Jacobian singularities during multicomponent property calculations. To address this bottleneck, this study proposes a novel native Modelica media modeling strategy that fundamentally reconstructs complex thermodynamic models, including Peng-
Robinson (PR) and SRK equations of state, alongside activity coefficient models like NRTL. By optimizing the mathematical expressions of core non-linear equations and introducing an improved initialization mechanism, the proposed framework effectively mitigates numerical oscillations at phase boundaries. Validated through a dynamic simulation of a synthetic natural gas production process, the system demonstrates significantly enhanced numerical robustness and high accuracy in vapor-liquid equilibrium calculations without initialization failures, providing a reliable foundation for complex process industry digital twins.amfc2026-203-reconstruction-of-complex-thermodynamic-equations-of-state-in-modelica-and-its-application-in-chemical-process-simulationXiangcheng WanJiawei TanYingru Zhaoenfalsehttps://modelica.simtek.cc/amfc2026/talk/AYKSCK/https://modelica.simtek.cc/amfc2026/talk/AYKSCK/feedback/Modelica Technology & AI (R1001)Closed-Form Local Stability of a Pressure-Phase-Coordinate Control-Volume ModelOral presentation2026-09-22T14:15:00+08:0014:1500:25This paper studies the local stability of a pressure–phasecoordinate control-volume model for one-dimensional compressible flow. Using pressure and a generalized phase coordinate as the state variables, the model retains the essential effects of compressibility, phase change, flow restriction, and wall heat transfer while remaining simple enough for closed-form analysis. Starting from the mass and energy balances, explicit state equations are derived and linearized about an equilibrium operating point. The analysis yields a closed-form factorization of the Jacobian determinant, showing that saddle versus non-saddle behavior is governed by a single phase-coordinate sensitivity term in the energy balance. In strictly single-phase regions, and in the bulk of the two-phase region under the present approximation, the model is non-saddle. By contrast, sharp heat-transfer variation near phase boundaries can drive direct stable-to-saddle transitions. In a higherpressure regime, the analysis also identifies conditions under which a Hopf candidate may arise. These results provide a simple and physically interpretable framework for understanding how heat transfer influences local stability in two-phase flow models.amfc2026-207-closed-form-local-stability-of-a-pressure-phase-coordinate-control-volume-modelHongtao QiaoChristopher LaughmanVedang DeshpandeScott BortoffenfalsePaper in PDF: Closed-Form Local Stability of a Pressure-Phase-Coordinate Control-Volume Modelhttps://modelica.simtek.cc/amfc2026/talk/RZPHTK/https://modelica.simtek.cc/amfc2026/talk/RZPHTK/feedback/Modelica Technology & AI (R1001)A Brief Methodological Reconsideration of Modelica-Based Hydraulic System Modeling and Xu's MethodOral presentation2026-09-22T14:40:00+08:0014:4000:25This paper briefly reviews the engineering development of Modelica-based hydraulic system modeling. On this basis, it reconsiders the issue of reasonable decomposition in component-based hydraulic modeling and discusses the methodological relevance of Xu's method for the development of Modelica hydraulic model libraries. Finegrained component decomposition facilitates rapid assembly and reuse, especially for users without extensive hydraulic modeling expertise. However, excessive subdivision may also introduce duplicated pressure states, hidden volumes, artificial fast time constants, increased numerical stiffness, and ambiguous physical semantics. Through order-of-magnitude examples involving pilotoperated relief valves, pressure compensators in multiway valves, short manifold passages, hydraulic cylinder chambers, and accumulator groups, the paper illustrates that the appropriate component boundary depends on the modeling objective, the dominant physical mechanism, the available parameters, and the quantities to be validated.amfc2026-211-a-brief-methodological-reconsideration-of-modelica-based-hydraulic-system-modeling-and-xu-s-methodBo WangXu YangzengenfalsePaper in PDF: A Brief Methodological Reconsideration of Modelica-Based Hydraulic System Modeling and Xu's Methodhttps://modelica.simtek.cc/amfc2026/talk/AN7YGT/https://modelica.simtek.cc/amfc2026/talk/AN7YGT/feedback/FMI & MBSE (R2001)Toshiba-Engineering Future Complex Systems through Multi-Domain Co-Simulation and Data-Driven EngineeringInvited Talk2026-09-22T10:35:00+08:0010:3500:25Silver sponsoramfc2026-228-toshiba-engineering-future-complex-systems-through-multi-domain-co-simulation-and-data-driven-engineeringDai ArakiJieshen Daienfalsehttps://modelica.simtek.cc/amfc2026/talk/BBFVZH/https://modelica.simtek.cc/amfc2026/talk/BBFVZH/feedback/FMI & MBSE (R2001)T2S4-Session ChairChair2026-09-22T11:04:00+08:0011:0400:01This session is chaired by:amfc2026-255-t2s4-session-chairXinghai Gaoenfalsehttps://modelica.simtek.cc/amfc2026/talk/XZZE8C/https://modelica.simtek.cc/amfc2026/talk/XZZE8C/feedback/FMI & MBSE (R2001)An Equation-Oriented Modeling Framework for Thermodynamic Cycle Systems toward MBSE: A Case Study on Supercritical CO₂ Brayton CyclesOral presentation2026-09-22T11:05:00+08:0011:0500:25Complex thermodynamic cycle systems play a critical role in advanced energy applications, where modeling and simulation constitute a fundamental component of Model-Based Systems Engineering (MBSE). Conventional sequential modular approaches rely heavily on predefined calculation sequences and empirical parameter settings, leading to limited adaptability for complex cycle topologies with strong coupling, flow splitting, and multiple recuperation processes. This study proposes an equation-oriented (EO) modeling framework for thermodynamic cycle systems, in which the entire cycle is formulated as a coupled nonlinear algebraic equation system and solved simultaneously. Standardized algebraic component models are established for compressors, turbines, and recuperators. An ε–NTU-based heat exchanger formulation is adopted to avoid the predefined minimum temperature difference commonly required in conventional approaches. In addition, a bi-level solution strategy combining outerlayer parameter scanning and inner-layer Newton iterations is introduced to handle additional design degrees of freedom. The proposed framework is validated using supercritical CO₂ simple recuperated and recompression Brayton cycles. Simulation results show that the predicted cycle efficiencies agree well with published reference data, with deviations below 1% under all investigated operating conditions. The framework demonstrates stable convergence behavior and strong adaptability to complex thermodynamic topologies. The proposed approach exhibits acausal, modular, and topology-decoupled characteristics consistent with the declarative modeling philosophy of Modelica. It provides a unified and extensible solution framework for MBSEoriented modeling and simulation of complex thermodynamic energy systems.amfc2026-192-an-equation-oriented-modeling-framework-for-thermodynamic-cycle-systems-toward-mbse-a-case-study-on-supercritical-co-brayton-cyclesXinyu BaiGenglei XiaTao ZhouGuanghui JiaoenfalsePaper in PDF: An Equation-Oriented Modeling Framework for Thermodynamic Cycle Systems toward MBSE: A Case Study on Supercritical CO₂ Brayton Cycleshttps://modelica.simtek.cc/amfc2026/talk/AGJPFK/https://modelica.simtek.cc/amfc2026/talk/AGJPFK/feedback/FMI & MBSE (R2001)A Three-Level MBSE Logical Design Framework and Requirements-Traceability Validation for ICF Ultrafast Imaging Diagnostics InstrumentsOral presentation2026-09-22T11:30:00+08:0011:3000:25The ultrafast evolution of the physical process in the laser driven inertial confinement (ICF) fusion requires ultrafast imaging diagnostic instruments. The traditional commercial instruments are not able to meet the ICF experiment requirement. It is a technical challenge to develop the diagnostic instruments with an extreme capbility. This work proposes a three-layer MBSE logical design framework for an ultrafast framing imager. The framework comprises an operational analysis layer, a system design layer, and a component design layer. Using SysML as the unified modeling language, it progressively decomposes the experimental objective of acquiring picosecond-scale plasma-evolution images into system indicators, functional nodes, logical components, and module-level parameters. Methodologically, the study establishes a bidirectional requirements-traceability mechanism and introduces a parameter-budget model for temporal-resolution allocation. Taking system temporal resolution <= 80 ps as the representative case, a complete decomposition chain is constructed from the user requirement down to triggersynchronization jitter, gating-pulse jitter, and MCP response time. Process-based comparative analysis further confirms the effectiveness of the framework in requirement allocation, change-impact localization, and cross-disciplinary collaboration. The case study shows that, at the same problem scale, the MBSE approach reduces the time required for requirement-to-module allocation from approximately 6.4 h to 2.1 h, and shortens the impact-localization time for tightening the target from 0 ps to 50 ps from 47 min to 11 min, while significantly reducing omissions. Model statistics indicate that the case includes 5 user requirements, 8 system indicators, 17 functional nodes, 12 key interfaces, and 46 traceability links, demonstrating not only conceptual value but also maintainability at a practical engineering scale. The proposed method therefore provides a useful foundation for the systematic design, rapid iteration, and subsequent automated verification of laser-fusion diagnostic equipment.amfc2026-196-a-three-level-mbse-logical-design-framework-and-requirements-traceability-validation-for-icf-ultrafast-imaging-diagnostics-instrumentsLiqiong XiaJinhua ZhengYouchen LiHuabing DuLin HuJianhua ZhengChaoxin ChenXing ZhangenfalsePaper in PDF: A Three-Level MBSE Logical Design Framework and Requirements-Traceability Validation for ICF Ultrafast Imaging Diagnostics Instrumentshttps://modelica.simtek.cc/amfc2026/talk/LSJCYV/https://modelica.simtek.cc/amfc2026/talk/LSJCYV/feedback/FMI & MBSE (R2001)Research on Multi-disciplinary Unified Modeling and Simulation Technology of Hualong One SystemOral presentation2026-09-22T11:55:00+08:0011:5500:25Nuclear power systems are complex multi-disciplinary systems involving thermal hydraulics, fluid mechanics, control engineering and electrical engineering. Comprehensive performance analysis and optimization of such systems require coupled multi-disciplinary modeling and simulation. Nevertheless, co-simulation via interfaces between separate single-discipline simulation software brings numerous inherent drawbacks. Hualong One is China’s domestically developed third-generation pressurized water reactor. To accelerate system modeling and simulation efficiency, this paper independently develops the unified multi-disciplinary modeling library CGN_HPR V1.0 based on Modelica and the hierarchical modeling method, with the domestic MWorks simulation platform as the core development environment. Adopting a four-tier architecture consisting of basic, component, equipment and system levels, the library supports modular, parameterized and visual modeling for streamlined simulation analysis, and addresses the technical bottleneck of insufficient deep coupling in traditional multi-disciplinary simulation workflows. Typical operating conditions of Hualong One are modeled and simulated using the proposed library, which preliminarily verifies the validity of the Modelica-based system simulation model. The results demonstrate that the Modelica-enabled simulation framework enables seamless integration of cross-disciplinary subsystems, mitigates the difficulties in multi-disciplinary coupled simulation for nuclear power plants, and improves overall system analysis and design capacity, providing key technical support for unit safety assessment and operational optimization.amfc2026-200-research-on-multi-disciplinary-unified-modeling-and-simulation-technology-of-hualong-one-systemCuiting PengFuhong LuoHong LvenfalsePaper in PDF: Research on Multi-disciplinary Unified Modeling and Simulation Technology of Hualong One System照片https://modelica.simtek.cc/amfc2026/talk/9DQNQJ/https://modelica.simtek.cc/amfc2026/talk/9DQNQJ/feedback/FMI & MBSE (R2001)T2S5-Session ChairChair2026-09-22T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-258-t2s5-session-chairChristian Bertschenfalsehttps://modelica.simtek.cc/amfc2026/talk/A3T8CG/https://modelica.simtek.cc/amfc2026/talk/A3T8CG/feedback/FMI & MBSE (R2001)An FMI-Based Fast Thermal Prediction FMU for Real-Time Digital Twins of Data Center Server RoomsOral presentation2026-09-22T13:50:00+08:0013:5000:25Data center server rooms require thermal prediction components that can be called by digital twins and system simulation platforms under changing IT loads and cooling conditions. CFD provides detailed spatial information but is too expensive for step-wise online simulation. Machine-learning surrogates are efficient, but offline models often lack state management, rolling prediction, and physical safeguards for continuous simulation. This paper presents an FMI(Functional Mock-up Interface)compatible fast thermal prediction FMU (Functional Mock-Up Unit) for data center server rooms. The FMU embeds a physics-guided graph neural thermal predictor and exposes standardized FMI inputs and outputs for supply-air temperature, fan state, IT load, historical thermal states, and predicted temperatures. Each simulation step performs input validation, historywindow update, model inference, output correction, and FMI variable write-back. A CRAH M1-08 shutdown replay case shows an overall RMSE of 0.284 ° C and an average step time of 0.0656 s, supporting near-real-time use in thermal digital twins.amfc2026-204-an-fmi-based-fast-thermal-prediction-fmu-for-real-time-digital-twins-of-data-center-server-roomsHaobo GuoSiyuan YangChuangkang YangXiangmiao HaoJie WuWenquan TaoenfalsePaper in PDF: An FMI-Based Fast Thermal Prediction FMU for Real-Time Digital Twins of Data Center Server Roomshttps://modelica.simtek.cc/amfc2026/talk/GVDZN9/https://modelica.simtek.cc/amfc2026/talk/GVDZN9/feedback/FMI & MBSE (R2001)A Modelica-FMI digital twin for reinforcement learning-based NOx emission control of boilers under deep peak shavingOral presentation2026-09-22T14:15:00+08:0014:1500:25Deep peak-shaving in coal-fired boilers causes non-linear dynamics, rendering traditional PID control insufficient for NOx emission management. This paper proposes a Modelica-based digital twin framework for a 600 MW boiler. The CFD-PINN model is coupled with a physicsbased steam-side dynamic model and an adaptive correction layer driven by DCS (Distributed Control System) data to ensure high-precision synchronization under operating condition drift. An intelligent reinforcement learning agent using the Recurrent Proximal Policy Optimization (Recurrent PPO) algorithm is developed to inject residual bias signals into the main loop, optimizing NOx emissions without altering the original architecture. Validated in seven scenarios spanning 300–600 MW, this strategy reduces the 95thpercentile NOx by 19%–23% and emission fluctuations by 32.7%–43.2% across four dynamic scenarios relative to both manually tuned and gain-scheduled PID baselines, while maintaining combustion efficiency. This approach provides a deployable, low-risk pathway for achieving stricter NOx compliance under deep peak shaving operation.amfc2026-208-a-modelica-fmi-digital-twin-for-reinforcement-learning-based-nox-emission-control-of-boilers-under-deep-peak-shavingQing DuanShengshan BiXuyao TangDongyang ZhouAiguo XuenfalsePaper in PDF: A Modelica-FMI digital twin for reinforcement learning-based NOx emission control of boilers under deep peak shavinghttps://modelica.simtek.cc/amfc2026/talk/GKSRCG/https://modelica.simtek.cc/amfc2026/talk/GKSRCG/feedback/FMI & MBSE (R2001)IFAC WC 2026 Benchmark Problem on Thermal Management Strategy Design of Connected Battery Electric VehiclesInvited Talk2026-09-22T14:40:00+08:0014:4000:25Invited talk: This benchmark proposes a thermal management problem for the battery electric vehicles (BEVs) considering both the energy consumption minimization, the temperature controls of electric devices and comfortability of cabin. The vehicle is assumed running
in a connected environment with real-time vehicle-to-everything (V2X) data available. The benchmark problem formulation is given and a simulation platform of BEV with V2X data is provided for the challengers. The purpose of this benchmark problem is to provide a platform for students and early-career researchers to tackle the challenges of on-board thermal management strategies in electric vehicles, and to exchange cutting-edge research results in automotive system control and optimization.amfc2026-231-ifac-wc-2026-benchmark-problem-on-thermal-management-strategy-design-of-connected-battery-electric-vehiclesFuguo Xuenfalsehttps://modelica.simtek.cc/amfc2026/talk/XXRVYH/https://modelica.simtek.cc/amfc2026/talk/XXRVYH/feedback/Energy (R2002)CATARC-Application Practice of New Energy Vehicle Thermal Management Simulation Based on CautosimInvited Talk2026-09-22T10:35:00+08:0010:3500:25Silver sponsoramfc2026-229-catarc-application-practice-of-new-energy-vehicle-thermal-management-simulation-based-on-cautosimBo JinenfalseSlideshttps://modelica.simtek.cc/amfc2026/talk/W7WPAS/https://modelica.simtek.cc/amfc2026/talk/W7WPAS/feedback/Energy (R2002)T3S4-Session ChairChair2026-09-22T11:04:00+08:0011:0400:01This session is chaired by:amfc2026-256-t3s4-session-chairLi Sunenfalsehttps://modelica.simtek.cc/amfc2026/talk/37YUKB/https://modelica.simtek.cc/amfc2026/talk/37YUKB/feedback/Energy (R2002)Dynamic Modelling of a PEM Electrolyser and Metal Hydride Hydrogen Storage SystemOral presentation2026-09-22T11:05:00+08:0011:0500:25Renewable deployment requires hydrogen storage decoupling production from demand, and metal hydride systems offer a safe, low-pressure, high-density option. Within the EU REMEDHYS project, a solid-state storage system based on an intermetallic alloy produced from recycled European metals is coupled to a 2 MW proton exchange membrane electrolyser. This paper presents a modular, equation-based Modelica model of the balance of plant of both subsystems, comprising eleven control loops, measured pressure-composition isotherms and a pseudo-twodimensional discretisation of the hydride bed. A 2 MW to 1 MW load step, a full absorption-desorption cycle, a sensitivity analysis and a coupled scenario feeding twenty cassettes are simulated. The hydrogen delivered per cycle falls from 4281 to 3292 g as the delivery pressure rises from 1 to 5 bar and doubles between 50 and 70 ◦C. Coupled to the electrolyser, a cassette charges in 6.6 h and stops discharging at a state of charge of 0.24, limited by the alloy equilibrium pressure.amfc2026-193-dynamic-modelling-of-a-pem-electrolyser-and-metal-hydride-hydrogen-storage-system/media/amfc2026/submissions/NVL3MM/image_VHp8C1E.webpMichele BologneseMatteo TestiPaolo PirasGiulia BonamigoErika Michela DematteisPaola RizziMarcello BariccoenfalsePaper in PDF: Dynamic Modelling of a PEM Electrolyser and Metal Hydride Hydrogen Storage Systemhttps://modelica.simtek.cc/amfc2026/talk/NVL3MM/https://modelica.simtek.cc/amfc2026/talk/NVL3MM/feedback/Energy (R2002)Waste to Hydrogen system dynamic modellingOral presentation2026-09-22T11:30:00+08:0011:3000:25Waste gasification is a promising thermochemical route for converting municipal solid waste and biomass into syngas for clean hydrogen production. This paper presents a Modelica-based dynamic model of the complete Balance of Plant (BoP) of the Hy2Rome Important Project of Common European Interest (IPCEI), focusing on three key contributions: (i) the integration of the gasifier Reduced Order Model (ROM), exported as a Functional Mock-up Unit (FMU), into the Modelica environment; (ii) the dynamic model of the Temperature Swing Adsorption (TSA) unit; and (iii) the dynamic model of the Pressure Swing Adsorption (PSA) system for final hydrogen purification. All BoP components were calibrated against Heat and Material (H&M) balance data, achieving acceptable relative errors. The PSA model implements a four-vessel, eight-step cycle delivering hydrogen at a nominal purity of 99.99 %, maintained during the adsorption steps at cyclic steady state. The TSA model operates a two-vessel thermal swing cycle between 40°C and 270°C with a PIDcontrolled state machine.amfc2026-197-waste-to-hydrogen-system-dynamic-modellingMichele BologneseEmanuele MartinelliMichele UrbaniMatteo TestiAnnarita SalladiniAlessia BorgognaMaria Rosaria PepeenfalsePaper in PDF: Waste to Hydrogen system dynamic modellinghttps://modelica.simtek.cc/amfc2026/talk/DWQUJR/https://modelica.simtek.cc/amfc2026/talk/DWQUJR/feedback/Energy (R2002)Development of an FMI-Oriented One-Dimensional Transient Heat Transfer Model for Separated Heat PipesOral presentation2026-09-22T11:55:00+08:0011:5500:25This paper develops an FMI-oriented one-dimensional transient thermal-hydraulic model for separated heat pipes used in passive residual heat-removal systems. The heat pipe is represented by modular control volumes for the evaporator, vapor riser, condenser, downcomer, wick, wall, vapor chamber, and external water tank. The model combines a nodal thermal-resistance network with a Knudsen-number startup criterion, vapor pressure-drop closure, Clausius-Clapeyron temperature-pressure coupling, phase-change source terms, and capillary/gravity-assisted return-flow closure. The resulting ordinary differential equations are solved using a three-stage Gauss-Legendre implicit Runge-Kutta method. To address system-level reuse, the physics, solver, parameters, inputs, outputs, and state-update routines are organized according to FMI Co-Simulation semantics, enabling later FMU wrapping without changing the core solver. Validation against small-scale and large-scale heat-pipe data shows temperature and flow-rate deviations of approximately 10% or less, supporting fast integration in Modelica-based accident-transient simulations.amfc2026-201-development-of-an-fmi-oriented-one-dimensional-transient-heat-transfer-model-for-separated-heat-pipesPeng JuXinchen WangYujie WangLei PengXinxin LienfalsePaper in PDF: Development of an FMI-Oriented One-Dimensional Transient Heat Transfer Model for Separated Heat Pipeshttps://modelica.simtek.cc/amfc2026/talk/3FL3P9/https://modelica.simtek.cc/amfc2026/talk/3FL3P9/feedback/Energy (R2002)T3S5-Session ChairChair2026-09-22T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-259-t3s5-session-chairXinhua Xuenfalsehttps://modelica.simtek.cc/amfc2026/talk/VJ7GLQ/https://modelica.simtek.cc/amfc2026/talk/VJ7GLQ/feedback/Energy (R2002)Research on Modeling and Simulation of Integrated Energy System Platform Based on OpenModelicaOral presentation2026-09-22T13:50:00+08:0013:5000:25Integrated energy-system models become difficult to reuse when electrical signals, thermal-fluid states, supervisory logic, and evaluation variables are coupled through inconsistent interfaces. This paper presents an OpenModelica platform with unit-explicit cross-domain connectors, graphically encapsulated component models, and a reusable library for renewable generation, conversion, and storage. The thermal loop employs Modelica.Fluid ports and a temperature-dependent SolarSalt medium, while the wind and photovoltaic components expose available, delivered, and curtailed power to support source-level curtailment feedback from the energy-management system. A DASSL-based workflow is applied to a full-year Dunhuang case and complementary parameter-variation cases. The annual simulation maintains complete park electric-load supply, achieves a heat-load satisfaction rate of 90.16%, and yields an electric LCOE of 0.307 CNY/kWh. Statevariable and sensitivity analyses indicate that renewablesource and battery sizing govern electrical absorption and economics, whereas winter thermal-storage availability and heat-side capacity constrain thermal reliability. The platform therefore links reusable Modelica implementation with long-horizon system evaluation without requiring topology-specific reformulation.amfc2026-205-research-on-modeling-and-simulation-of-integrated-energy-system-platform-based-on-openmodelicaYuan YaoXiaomeng DongHong LvHao HongWenji LienfalsePaper in PDF: Research on Modeling and Simulation of Integrated Energy System Platform Based on OpenModelicahttps://modelica.simtek.cc/amfc2026/talk/PH3GHZ/https://modelica.simtek.cc/amfc2026/talk/PH3GHZ/feedback/Energy (R2002)Modelica-Based Performance Analysis of an Organic Rankine Cycle under Marine Hot- and Cold-Source VariationsOral presentation2026-09-22T14:15:00+08:0014:1500:25Fluctuations in offshore low-temperature heat sources and cold seawater alter the heat input, heat rejection, and condensing pressure of organic Rankine cycle (ORC) systems, making reliable off-design assessment important for marine power conversion. This study investigates a closed R134a ORC subjected to hot- and cold-source temperature and flow variations. A variable-condensingpressure model is developed in Modelica. The evaporator and condenser are discretized using the finite-volume method, and receiver pressure is determined from coupled mass and energy balances; the working-fluid mass flow is maintained at 0.50 kg/s. Steady-state matrices are used to quantify pressure, phase state, net power, and thermal efficiency. At 363.15 K and 2.50 kg/s on the hot side and 288.15 K and 1.53 kg/s on the cold side, the model gives high/low pressures of 2.496/0.793 MPa, net power of 4.557 kW, thermal efficiency of 4.66%, and receiver level of 0.501. Across the normal cold-source-temperature cases, condensing pressure and net power vary by 29.7 kPa and 0.185 kW, respectively; the corresponding ranges over the cold-source-flow matrix are 35.5 kPa and 0.224 kW. The 359.15 and 360.15 K hot-source cases lead to wet expansion, whereas the 278.15 and 283.15 K cold-source cases cross the pump-inlet phase boundary. Refined calculations over the 89-90 °C hot-source interval show increases of 0.128 kW in net power and 1.532 kW in evaporator heat input, with thermal efficiency increasing from 4.606% to 4.664%. These results quantify the distinct effects of heat-input and heat-rejection variations under variable condensing pressure and identify the associated phase boundaries.amfc2026-209-modelica-based-performance-analysis-of-an-organic-rankine-cycle-under-marine-hot-and-cold-source-variationsHongfei ZhuXiao YanMing DingenfalsePaper in PDF: Modelica-Based Performance Analysis of an Organic Rankine Cycle under Marine Hot- and Cold-Source Variationshttps://modelica.simtek.cc/amfc2026/talk/Y7ZQLD/https://modelica.simtek.cc/amfc2026/talk/Y7ZQLD/feedback/Energy (R2002)Modelica-Based Simulation of Hydrodynamics and Thermomechanical Stress in Water-Wall Tubes of a 1000 MW Ultra-Supercritical BoilerOral presentation2026-09-22T14:40:00+08:0014:4000:25Under low-load conditions, the water wall of ultrasupercritical boilers often faces severe challenges such as flow distribution imbalance, phase change point migration, and hydrodynamic multiplicity, which can easily induce tube wall overheating and even tube rupture, directly threatening the safety of heating surfaces. To address this, this paper constructs a thermalhydraulic-stress multi-physics coupled simulation model for a 1000 MW ultra-supercritical boiler water wall using the Modelica language. It analyzed three typical operating conditions, BMCR, 75% THA, and 50% THA, to investigate the coupled distribution of hydrodynamic characteristics and stress-strain. The results show that mechanical stress decreases linearly with load reduction, thermal stress peaks due to enhanced phase-change heat transfer, and strain distribution is dominated by thermal expansion. The equivalent stress is controlled by mechanical stress, exhibiting a trend of first decreasing and then increasing with height. The conclusions indicate that the essential causes of increased local failure risk are the coupling effect of flow distribution imbalance and non-uniform heat load under low load. This study provides a theoretical basis for optimization design.amfc2026-212-modelica-based-simulation-of-hydrodynamics-and-thermomechanical-stress-in-water-wall-tubes-of-a-1000-mw-ultra-supercritical-boilerTianyu ChuCan FangJiali HuangWei ZhangJunhua ChenDong GuoYi LuenfalsePaper in PDF: Modelica-Based Simulation of Hydrodynamics and Thermomechanical Stress in Water-Wall Tubes of a 1000 MW Ultra-Supercritical Boilerhttps://modelica.simtek.cc/amfc2026/talk/QHLCKE/https://modelica.simtek.cc/amfc2026/talk/QHLCKE/feedback/(Electric) Mobility & Buildings (R2003)TLK-Thermo - Engineering Services and Software for Thermal SystemsInvited Talk2026-09-22T10:35:00+08:0010:3500:25Comprehensive overview of TLK-Thermo's engineering services, simulation software, and advanced measurement capabilities.amfc2026-230-tlk-thermo-engineering-services-and-software-for-thermal-systemsChristian Schulzeenfalsehttps://modelica.simtek.cc/amfc2026/talk/KVALSR/https://modelica.simtek.cc/amfc2026/talk/KVALSR/feedback/(Electric) Mobility & Buildings (R2003)T4S4-Session ChairChair2026-09-22T11:04:00+08:0011:0400:01This session is chaired by:amfc2026-257-t4s4-session-chairZhixu Chenenfalsehttps://modelica.simtek.cc/amfc2026/talk/PLXGXH/https://modelica.simtek.cc/amfc2026/talk/PLXGXH/feedback/(Electric) Mobility & Buildings (R2003)Coordinated Pressure Control Strategy Integrating Feedforward Compensation and Integral Freezing for Biosafety Laboratories under Transient Door Operation DisturbancesOral presentation2026-09-22T11:05:00+08:0011:0500:25Safe operation of biosafety laboratories requires directional airflow and stable pressure gradients to contain pathogenic aerosols. However, door operations between the core and buffer rooms frequently disrupt this balance, inducing transient pressure disturbances. Conventional PI control often responds inadequately to such rapid transients, which can lead to airflow reversal and compromise biosafety. This study develops a Modelicabased two-zone transient differential pressure regulation model for the core and buffer rooms and proposes a coordinated control strategy that integrates feedforward compensation with an integral-freezing mechanism. During door opening, the feedforward mechanism adjusts the exhaust airflow, rapidly increasing the doorway air mass flow rate to 0.12–0.14 kg/s. This establishes a directional airflow barrier (velocity ≥ 0.2 m/s), thereby reducing the risk of leakage during personnel transit. By freezing the PI integral term during the door-open phase and applying feedforward exhaust compensation upon closure, the proposed strategy restricts the maximum pressure undershoot during door closure to −17 Pa (against the −15 Pa design setpoint), compared to −24 Pa under conventional PI control. The pressure recovery time is also reduced from 48.3 s to 13.6 s. These findings provide a simulation-based basis for improving the dynamic containment of high-level biosafety laboratories under transient disturbances.amfc2026-194-coordinated-pressure-control-strategy-integrating-feedforward-compensation-and-integral-freezing-for-biosafety-laboratories-under-transient-door-operation-disturbancesYuhui WangHejiang SunenfalsePaper in PDF: Coordinated Pressure Control Strategy Integrating Feedforward Compensation and Integral Freezing for Biosafety Laboratories under Transient Door Operation Disturbanceshttps://modelica.simtek.cc/amfc2026/talk/FDENKU/https://modelica.simtek.cc/amfc2026/talk/FDENKU/feedback/(Electric) Mobility & Buildings (R2003)Simulation and Experimental Verification of Temperature Rise in an Oil-Cooled Motor Based on AmesimOral presentation2026-09-22T11:30:00+08:0011:3000:25With the development of electric drive systems for electric vehicles toward high power density and integration, electric drive assemblies integrating the drive motor, motor controller, and reducer have attracted lots of concerns. Under the combined effect of multiple heat sources within a compact space, the system heat dissipation of an electric drive system remains an open challenge, so it is necessary to analyze the temperature rise characteristics inside the motor. This paper examines in detail the distribution of internal losses in the motor and calculates the thermal resistances of the motor components. Then, a thermal–hydraulic coupled simulation platform on the Amesim platform for predicting the temperature rise of oil-cooled motors is developed based on the lumped-parameter thermal network method. A bench test is established to verify the accuracy of the simulation model. Experimental results show that the maximum error is 5.53%. This study provides a reference for the thermal analysis of motors in electric drive assemblies.amfc2026-198-simulation-and-experimental-verification-of-temperature-rise-in-an-oil-cooled-motor-based-on-amesimCunhao ZhangShuo ChengYanfang LiuPeng DongShuhan WangXiangyang XuenfalsePaper in PDF: Simulation and Experimental Verification of Temperature Rise in an Oil-Cooled Motor Based on Amesimhttps://modelica.simtek.cc/amfc2026/talk/A7YAYU/https://modelica.simtek.cc/amfc2026/talk/A7YAYU/feedback/(Electric) Mobility & Buildings (R2003)Optimal Droop Control for SOFC/battery/PV Hybrid System Based on Multi-objective Genetic AlgorithmOral presentation2026-09-22T11:55:00+08:0011:5500:25SOFC/battery/PV hybrid energy system is widely used to generate electricity. However, it is challenging to manage output power of each devices while regulating the bus voltage within a safe range. To this end, this paper proposes an optimal droop control strategy for the hybrid system, which consists of solid oxide fuel cell (SOFC), lithium-ion battery and photovoltaic (PV) array. First, a group of nonlinear and implicit differential equations including different sources and controllers is developed. Then, multi-objective genetic algorithm is adopted to minimize the output power error and the electricity loss simultaneously, deriving the optimal droop control parameters as well as the feedback PI parameters. Finally, the optimal solution is chosen from the Pareto front. Numerical experiments demonstrate that the control performance of the proposed method is better than the single objective method while the electricity loss can be reduced by 96.26%..amfc2026-202-optimal-droop-control-for-sofc-battery-pv-hybrid-system-based-on-multi-objective-genetic-algorithmZongrun WangLi SunenfalseAbstract: Optimal Droop Control for SOFC/battery/PV Hybrid System Based on Multi-objective Genetic Algorithmhttps://modelica.simtek.cc/amfc2026/talk/TNTZZ7/https://modelica.simtek.cc/amfc2026/talk/TNTZZ7/feedback/(Electric) Mobility & Buildings (R2003)T4S5-Session ChairChair2026-09-22T13:49:00+08:0013:4900:01This session is chaired by:amfc2026-260-t4s5-session-chairMichele Bologneseenfalsehttps://modelica.simtek.cc/amfc2026/talk/7NCJZD/https://modelica.simtek.cc/amfc2026/talk/7NCJZD/feedback/(Electric) Mobility & Buildings (R2003)Calibration workflow development using FMI-based reduced-order models for NEVs thermal managementOral presentation2026-09-22T13:50:00+08:0013:5000:25New Energy Vehicles (NEVs) drive China's automotive market. However, calibrating thermal management for the battery, motor, and inverter is a major bottleneck, especially as development cycles shrink to 24 months. High-fidelity thermal-fluid models are too slow for repeated calibration and fixed-step real-time execution. This paper presents an end-to-end industrial workflow: 1) Build high-fidelity models using 3D data; 2) Train reduced-order models (ROMs), implemented here as nonintrusive data-driven surrogate models, from simulation data; 3) Export the trained surrogates as FMI-compliant co-simulation FMUs for NI VeriStand real-time testing. The ROM tool proves efficient for repeated calibration runs; in this paper, ROM refers to a non-intrusive datadriven surrogate model exported as an FMI-compliant cosimulation FMU, rather than a projection-based or equation-level model-order-reduction method. The reported speedup is interpreted as repeated-simulation acceleration, with data generation and training treated as one-time amortized costs. For the reported battery validation cases, temperature RMSE was evaluated against experimental or CAE reference data and normalized by the initial temperature of the battery heat-generation operating condition. The calculation results show that the single-cell RMSE is within 5%, the battery-pack RMSE is within 10%, and the absolute temperature error is within 3 K. For a representative 10-module battery-pack cooling case, the 250 s closed-loop simulation time was reduced from 2200 s to 44 s after surrogate-FMU replacement, and the calibrated PI response met the control targets.amfc2026-206-calibration-workflow-development-using-fmi-based-reduced-order-models-for-nevs-thermal-managementRui GaoYuhao XuJunjie ChenJunyang HouWeilin LiDa LiYang QiYue TangenfalsePaper in PDF: Calibration workflow development using FMI-based reduced-order models for NEVs thermal managementhttps://modelica.simtek.cc/amfc2026/talk/P7TTP8/https://modelica.simtek.cc/amfc2026/talk/P7TTP8/feedback/(Electric) Mobility & Buildings (R2003)Thermal Simulation of a Low-Voltage Motor Based on a Thermal Resistance NetworkOral presentation2026-09-22T14:15:00+08:0014:1500:25Low-voltage motors are increasingly used in steering, cooling, pumping, and cabin-control subsystems of newenergy vehicles, and excessive temperature can impair their reliability. This paper develops a compact Modelica thermal-network model for an external-rotor low-voltage permanent-magnet synchronous motor. The model represents the principal heat sources, lumped heat capacities, and conductive, convective, and radiative heat-transfer paths. For the tested mass-produced motor, an end-to-end simulation of 30 min of physical operation completed in approximately 2s. Comparison with measured housing temperature for one operating case yielded a maximum relative discrepancy of 5.9%, occurring at the housing-temperature measurement point. These results indicate that the model is a computationally efficient reduced-order tool for the motor and operating range studied. Because the experimental comparison is limited to one motor type and one measured temperature location, accuracy and parameter transferability must be reassessed before the model is applied to other motor topologies or cooling arrangements.amfc2026-210-thermal-simulation-of-a-low-voltage-motor-based-on-a-thermal-resistance-networkZhixu ChenBing YuHan TanHaiying OuXiaodong Xuentruehttps://modelica.simtek.cc/amfc2026/talk/8YE79S/https://modelica.simtek.cc/amfc2026/talk/8YE79S/feedback/(Electric) Mobility & Buildings (R2003)Model-Based Thermal Management for Electric Vehicles: Modeling, Simulation, and ControlOral presentation2026-09-22T14:40:00+08:0014:4000:25With the continuous growth in global sales and market penetration, electric vehicles are increasingly required to operate under extreme temperatures, high altitudes, and humid or saline conditions, posing substantial challenges to thermal management. To address this challenge, this paper proposes a 1D model-based framework for integrated thermal management system (ITMS), enabling the unified design of modeling, simulation, and control. A vehicle-level thermal model is developed in Modelica to capture the coupled dynamics of the refrigeration cycle, cabin, battery, and motor, and is driven by driving cycles to simulate diverse operating conditions. The functional mock-up interface (FMI) is adopted to bridge the physical simulation model and the co-simulation environment, establishing a control interface that supports multivariable coordination and facilitates the integration of data-driven methodologies, such as reinforcement learning (RL). Within this framework, a RL-based control model is implemented to coordinate the system’s actuators. Under dynamic driving cycles, we evaluate the learned RL policy against a fixed-parameter baseline and a rule-based controller. The results validate the feasibility of the proposed integrated architecture in coupling physicsbased level modeling with adaptive data-driven control, providing a systematic approach for thermal management design in electric vehicles.amfc2026-213-model-based-thermal-management-for-electric-vehicles-modeling-simulation-and-controlChengen LiZhixu ChenLi XieXiaohu WangYuxi LiuYuanhao PiaoenfalsePDF: Model-Based Thermal Management for Electric Vehicles: Modeling, Simulation, and Controlhttps://modelica.simtek.cc/amfc2026/talk/SXPFPM/https://modelica.simtek.cc/amfc2026/talk/SXPFPM/feedback/