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UID:pretalx-amfc2026-UDWNLF@modelica.simtek.cc
DTSTART;TZID=CST:20260921T134900
DTEND;TZID=CST:20260921T135000
DESCRIPTION:This session is chaired by:
DTSTAMP:20261004T070621Z
LOCATION:(Electric) Mobility & Buildings (R2003)
SUMMARY:T4S2-Session Chair - Rui Gao
URL:https://modelica.simtek.cc/amfc2026/talk/UDWNLF/
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UID:pretalx-amfc2026-NAPY3J@modelica.simtek.cc
DTSTART;TZID=CST:20260921T144000
DTEND;TZID=CST:20260921T150500
DESCRIPTION:Web-based modeling platforms and automated modelchecking work
 flows in CI/CD pipelines require remotely callable Modelica frontend serv
 ices. This paper presents SMC (Simtek Modelica Compiler)\, a service-orien
 ted Modelica frontend implemented in Go. SMC exposes model loading\, struc
 tured queries\, diagnostics\, and flattening through a service API backed
  by a pipeline for parsing\, instantiation\, semantic analysis\, connectio
 n processing\, and flat-model generation. Drawing on the SMC implementati
 on\, we also discuss the engineering implications of Go for single-binary 
 deployment\, concurrent processing\, representation of Modelica language c
 onstructs\, and memory allocation. We demonstrate the prototype through in
 tegration with a web modeling platform. A common set of 606 comparable cla
 sses from the Modelica Standard Library (MSL) 4.0.0 is used both to assess
  frontend-result consistency against the OpenModelica Compiler (OMC) and t
 o measure flattening time\, memory allocation\, and garbage-collection be
 havior. The evaluation covers frontend artifacts and in-process processing
  costs\; backend processing\, code generation\, simulation\, and FMU expor
 t are outside its scope.
DTSTAMP:20261004T070621Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:SMC: A Cloud-Deployable Modelica Frontend Service in Go - Hailong W
 ang\, Qingda Xu\, Enyang Zhang\, Longsheng Sun\, Rui Gao\, Aiguo Xu
URL:https://modelica.simtek.cc/amfc2026/talk/NAPY3J/
END:VEVENT
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UID:pretalx-amfc2026-NELTYC@modelica.simtek.cc
DTSTART;TZID=CST:20260921T162500
DTEND;TZID=CST:20260921T165000
DESCRIPTION:The high penetration of renewable energy increases grid volatil
 ity\, necessitating flexible demand-side resources such as District Coolin
 g Systems (DCS) with thermal storage. However\, the multi-timescale coupli
 ng of equipment\, pipelines\, and buildings makes it difficult to integrat
 e high-fidelity physical models with optimization. This paper proposes an 
 FMI-based co-simulation framework for day-ahead DCS optimization. A fullch
 ain 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) s
 cheduling with discrete unit commitment\, together with a systematic quant
 ification of the accuracy-runtime trade-off induced by FMI communication s
 ettings. 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 red
 uces operating costs by 0.97%\, the energy-optimization scenario reduces e
 lectricity consumption by 4.58%\, and the carbon-optimization scenario red
 uces CO2 emissions by 4.20%. The physical feasibility of the generated sch
 edules is verified by dynamic FMU simulation.
DTSTAMP:20261004T070621Z
LOCATION:FMI & MBSE (R2001)
SUMMARY:An FMI Co-Simulation Framework for Day-ahead Optimization of Distri
 ct Cooling Systems Based on Modelica - Weidong Ma\, Feng Sha\, Xiang Li\, 
 Rui Gao
URL:https://modelica.simtek.cc/amfc2026/talk/NELTYC/
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UID:pretalx-amfc2026-P7TTP8@modelica.simtek.cc
DTSTART;TZID=CST:20260922T135000
DTEND;TZID=CST:20260922T141500
DESCRIPTION:New Energy Vehicles (NEVs) drive China's automotive market. How
 ever\, calibrating thermal management for the battery\, motor\, and invert
 er is a major bottleneck\, especially as development cycles shrink to 24 m
 onths. High-fidelity thermal-fluid models are too slow for repeated calibr
 ation and fixed-step real-time execution. This paper presents an end-to-en
 d industrial workflow: 1) Build high-fidelity models using 3D data\; 2) Tr
 ain reduced-order models (ROMs)\, implemented here as nonintrusive data-dr
 iven surrogate models\, from simulation data\; 3) Export the trained surro
 gates as FMI-compliant co-simulation FMUs for NI VeriStand real-time testi
 ng. 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 e
 quation-level model-order-reduction method. The reported speedup is interp
 reted as repeated-simulation acceleration\, with data generation and train
 ing treated as one-time amortized costs. For the reported battery validati
 on cases\, temperature RMSE was evaluated against experimental or CAE refe
 rence data and normalized by the initial temperature of the battery heat-g
 eneration operating condition. The calculation results show that the singl
 e-cell RMSE is within 5%\, the battery-pack RMSE is within 10%\, and the a
 bsolute temperature error is within 3 K. For a representative 10-module ba
 ttery-pack cooling case\, the 250 s closed-loop simulation time was reduce
 d from 2200 s to 44 s after surrogate-FMU replacement\, and the calibrated
  PI response met the control targets.
DTSTAMP:20261004T070621Z
LOCATION:(Electric) Mobility & Buildings (R2003)
SUMMARY:Calibration workflow development using FMI-based reduced-order mode
 ls for NEVs thermal management - Rui Gao\, Yuhao Xu\, Junjie Chen\, Junyan
 g Hou\, Weilin Li\, Da Li\, Yang Qi\, Yue Tang
URL:https://modelica.simtek.cc/amfc2026/talk/P7TTP8/
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