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Merging Shot Data and Measuring Player Importance to Team’s Offensive Flow
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Chemical 2.0 (Free open-source Modelica library)
Full text linkFree open-source Modelica library called Chemical 2.0(https://github.com/MarekMatejak/Chemical) providesexpressions between chemical substances and processes.These robust and unified definitions allow users to choosewhether define processes or substances in their dynamic(electro-)chemical models. Propagation of substancedefinition and chemical solution through connectedcomponents simplify configuration. Chemical pathways canstart even with unknown substances. Chemical kinetics wasrewritten.The possibilities and performance of chemical pathwaysmodeling are increased using a new type of connectors basedon inertial electro-chemical potential. Chemical processescan be directly connected without need to add unsignificantstates. Parameterization of chemical reactions is alsostreamlined, e.g. using forward rate and dissociationcoefficient
Enhancing Collocation-Based Dynamic Optimization through Adaptive Mesh Refinement
Full text linkDirect collocation-based dynamic optimization plays animportant role in the optimization of equation-basedmodels. With this approach, continuous problems aretranscribed into sparse nonlinear programs (NLPs) that canbe solved efficiently. The open-source Modelica environmentOpenModelica provides an implementation using Radau IIAcollocation, but has major limitations, such as the lack ofparameter optimization, no adaptive mesh refinement, and nosupport for higher-order integration schemes. This paperpresents (1) a comprehensive reimplementation thataddresses these limitations and (2) a novel -method meshrefinement algorithm. Implemented in the custom Python /C++ optimization framework GDOPT, the approach demonstratessignificant performance improvements, solving typicalproblems 2 to 3 times faster than OpenModelica underequivalent conditions. Using the proposed mesh refinementalgorithm, the framework correctly identifies non-smoothregions and increases resolution accordingly, requiringonly a small increase in computation time. Theimplementation lays the foundation for a future integrationinto the OpenModelica toolchain
Comparing the Predictive Event Handling Algorithm LookAhead to Rollback and Early Return
Full text linkLookAhead is a lightweight algorithm that improves eventhandling in co-simulation by predicting events andadjusting the communication step size beforehand. Itoperates without requiring subsystem event handlingcapabilities. This paper compares LookAhead with otherevent handling methods, namely Rollback and Early Return,from the perspective of performance and applicability.Results from the presented example show that LookAheadperforms on par with iterative co-simulation methods and isparticularly well-suited for handling shared state events
Enhancing Large-Scale Power Systems Simulations through Functional Mockup Unit-based Grid-Forming Inverter Models
Full text linkNew York State (NYS) faces significant challenges inmeeting the Climate Act’s bold goals of 70% renewableenergy generation by 2030 and total decarbonization of theelectric grid by 2040. Extensive simulations are requiredto assess the impact of numerous inverter-based resources(IBRs) deployed to the large-scale NYS power grid, aimingto evaluate their dynamic behavior and mitigate anynegative interactions with their control schemes. However,the modeling efforts required are huge and thecomputational burden of large-scale simulations isextensive, and often limited by the capabilities of domain-specific tools. This work addresses these limitations bydeveloping a Functional Mockup Unit (FMU) of Grid- Forming(GFM) Inverters for IBR control and integrating them withan electromechanical phasor-domain power system solver. Theproposed FMU facilitates the simulation and parametricstudies needed to analyze large- scale IBR usage withsignificantly improved manual modeling and computationalefforts. The paper details the process of developing andFMU model for GFM IBRs, including all relevant controlloops implemented in the Modelica language and FMUintegrated in OPAL-RT’s ePHASORSIM software. Our FMU modelsare used to successfully deploy and study the impacts of upto 6, 200+ MVA from IBRs on the 5000-bus NYS transmissionsystem
Physics-Based Dynamic Modeling of Solar-Powered Off-Grid Cold Storage for Perishables Using Modelica: A Case Study – Xingalool, Somalia
Full text linkThis paper presents a dynamic, physics-based Modelica modelfor simulating solar-powered, off-grid cold storage systemsused to preserve perishables. A preliminary componentlibrary, developed using the Modelica Standard Library,supports modular modeling of PV-powered chillers, thermalloads, and latent thermal energy storage (LTES), whichmaintains cooling during non-solar hours. The library isapplied to a 200 m³ cold room in Xingalool, Somalia,designed to stay at 5 °C. A case study simulating 500 kg ofcrop loading at 8:00 and unloading at 17:00 demonstratesthe system’s dynamic behavior under realistic solar andambient conditions.** This paper is intended to be presented as a poster
Modelica2Pyomo: a tool to translate Modelica models into Pyomo optimization models
Full text linkTasks involving Modelica models often do not simplyinvestigate the dynamic behavior of a system, but ratherwant to characterize also possible optimal controlstrategies according to suitable criteria. Unfortunately,since Modelica does not support out-of-the-box optimizationfeatures, users are often forced to use other tools tocode again the system model for optimization studies. Forthis reason, the authors present Modelica2Pyomo, anopen-source tool to translate Modelica models into Pyomooptimization programs, leveraging on their flat BaseModelica representation. This work illustrates the mainfeatures of Modelica2Pyomo, including automatic variablesand constraints normalization, expressions manipulation andinitialization via Modelica simulation results. Todemonstrate the capabilities of this framework, twoexamples are showcased, including an industrial relevantopen-loop optimal control problem of a solid-oxide fuelcell
Safe and Efficient Control of a Brayton Cycle Heat Pump Using Reinforcement Learning
Full text linkDecarbonizing industrial process heating will increasinglydepend on high-temperature heat pumps. In particular,Brayton cycle heat pumps, which can reach temperaturesabove 250 °C, are viewed as a promising technology.However, ensuring safe operation and optimal controlremains challenging. This study presents an experimentallyvalidated dynamic model of a Brayton cycle heat pump, asystem with multiple control inputs for regulating itsthermal output. Using this model as a training environment,several control concepts integrating Reinforcement Learning(RL) and traditional PI controllers were implemented toachieve desired heat supply at target temperatures. Domainrandomization was employed to improve the controllerrobustness against model uncertainties in preparation fordeployment on the physical system. The results demonstratethat RL controllers can not only achieve the desiredset-point temperature under varying loads while maintainingrequired safety margins, but also discovered a novel, moreenergy-efficient operational strategy
A New Multi-Agent Simulator Framework Using Hopsan and Unreal 5.3
Full text linkUnmanned Aircraft Systems (UAS) research increasingly requires high-performance, multi-agent simulation environments that integrate realistic dynamics with immersive visualization. This paper introduces a distributed co-simulation framework that seamlessly combines the dynamic modeling capabilities of Hopsan with the advanced rendering and interaction tools of Unreal Engine 5.3. Communication between the two platforms is achieved through a lightweight, User Datagram Protocol (UDP) based plugin, which supports bi-directional real-time data exchange and is complemented by a USB Raw Input plugin to integrate human-in-the-loop joystick control. The proposed framework was validated across progressively complex scenarios. First, a single F‑16 aircraft data model was imported from Hopsan, encompassing waypoint-guided dynamics, atmospheric effects, actuation, and geoid-based altitude calibration. Its state reinforced by Hopsan was visualized in Unreal in real-time. Second, the platform was extended to support two independent F‑16 agents, each communicating via dedicated UDP ports, thereby demonstrating modular, scalable, multi-agent operation. Third, we introduced a Human Machine Interface (HMI) scenario, where one aircraft was piloted manually via joystick input, while the second autonomously followed the same waypoint sequence. This validated the framework’s capacity to handle human interaction in a multi-agent context. Results evidence synchronized simulation at real-time performance, accurate environmental interactions (terrain, wind, collision), and reliable human-in-the-loop control. The framework’s architecture promotes modularity, scalability, and deployment flexibility across multiple machines. Future enhancements will explore tighter coupling with Unreal’s environmental physics, adoption of fluid dynamics, and scaling to larger agent ensembles. By integrating open-source dynamical modeling with high-fidelity graphical simulation, this platform offers a robust foundation for UAS mission planning, operator training, and AI-driven control validation