Linköping Electronic Conference Proceedings
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Variable Structure System Simulation via Predefined Acausal Components
Get PDFThis article outlines a new approach of the experimental open-source modeling and simulation system Modia to simulate systems where the number of variables and equations can be changed after compilation and also during simulation, without having to re-generate and rec-ompile the code. Details are given for heat transfer in an insulated rod, where the discretisation of the rod is completely hidden from the symbolic engine. It is discussed how this approach could also be used in a future version of Modelica and/or FMI. Furthermore, this feature is also used in various variants to speed up collision handling in 3D mechanical systems. For example, by rigidly fixing an object after it has been gripped, with or without calculating the elastic response, and thereby dynamically changing the number of degrees of freedom
Simulation Study of Flow Instability in Parallel Multi-Channel Systems Based on Modelica
Get PDFIn parallel channels of a nuclear reactor core, flow instability can cause a significant decrease in critical heat flux (CHF) or mechanical oscillation of the fuel components, endangering the normal operation of the reactor. The NUMAP software, developed based on the two-fluid six-equation theory and using the Modelica language, is a multi-domain unified modeling and simulation platform for nuclear power plants. In this paper, a parallel dual-channel system model was constructed based on the NUMAP software, referencing a high-temperature and high-pressure steam-water two-phase thermohydraulic experimental device, to simulate flow instability phenomena. The comparison with experimental data validated the transient analysis ability of the NUMAP software for flow instability phenomena. Based on this, the flow instability boundary of a parallel multi-channel system was calculated under the same operating conditions. When the number of parallel channels was 2, 3, and 4, the calculated flow instability boundary error did not exceed ±5%, verifying that a parallel dual-channel structure can be used to obtain the flow instability boundary when there are multiple parallel heating channels
5th Generation District Heating and Cooling Modelica Models for Prosumer Interaction Analysis
Get PDF5th Generation District Heating and Cooling (5GDHC) provides a promising pathway for decarbonising the thermal sector. To quantifying the synergies between heating, cooling, and electricity, complex thermofluid models are required. Modelica offers a potential solution for developing such models but there is a scarcity of accessible and usable models. This paper addresses this gap by presenting a comprehensive set of Modelica models for key elements of 5GDHC systems: prosumers, balancing units, and hydraulic interfaces. The models prioritise usability by facilitating the utilisation of Func-tional Mock-up Interface and Power Hardware-in-the-Loop (PHIL) methodologies. Component design, rele-vant controls and the applicability of PHIL setups are discussed. A theoretical case exemplifies hardware min-imisation, using only heat exchangers to investigate prosumer behaviour. The paper concludes with a discus-sion on the potential use of these models, opportunities for improvement, and the need for further research and experimental investigations in understanding 5GDHC systems
Open-Source Models for Sand-Based Thermal Energy Storage in Heating Applications
Get PDFThis paper presents a new open-source modeling package in the Modelica language for particle-based silica-sand thermal energy storage (TES) in heating applications, available at https://github.com/sbslab/modelica-sand. Silica sand is an abundant, low-cost, and efficient storage medium for concentrated solar power and electricity generation. Although uncommon today, solid particle TES could benefit building and district heating systems, particularly as building electrification and renewable energy penetration increases. To enable heating system design and evaluation with sand TES, this work developed and open-source released Modelica models from base classes through complete systems with both physical equipment and controls. This paper first presents the new models. Then, we demonstrate their application with a heating plant that supplies steam for district heating, while also providing power-to-heat grid services by storing excesses renewable electricity as thermal energy
An Open-Source Benchmark of IEEE Test Cases for Easily Testing a New Approach for Steady State Calculations in Power Systems
Get PDFPower systems modeling and simulation are essential to conduct studies on the electrical transmission system and ensure its security. For this purpose, RTE, the French Transmission System Operator (TSO), is developing Dynaωo, a hybrid Modelica/C++ open-source suite of simulation tools for power systems. Most power systems models for Dynaωo are developed in the Modelica language using the Dynaωo Modelica library. This paper presents a full Modelica standard electrical power system benchmark implemented using the Dynaωo library. The IEEE 14-bus system benchmark is modeled here for steady-state calculation, with an approach that replaces the static load flow. Two test cases are simulated using the OpenModelica environment showing differences in the final steady-state result. We show flexibility in modeling with this library where different system behaviors can be observed and where models with different levels of details can be replaced depending on the application: steady-state calculation, long-term stability, or short-term stability
Race Car Cooling System Model for Real Time use in a Driving Simulator
Get PDFPowertrain performance optimization is one of main targets in racecar and road hypercar development. A key activity needed for both endothermal and electric powertrains is the cooling system sizing through simulation to make sure that the temperature limits are not exceeded in the most aggressive conditions minimizing or avoiding power derating. This article describes the implementation of a 1D cooling system simulation model integrated with a vehicle multibody model to be used real time in the Dallara dynamic driving simulator with human driver. This activity is the result of a collaboration between Dallara which uses the model implemented to develop and optimize the cooling system architecture of its vehicles, and Claytex which develops the libraries used to generate these simulation models
Hybrid Power Systems Simulation and Optimization Utilizing SSP and FMI
Get PDFCollaborative model-based development of the hybrid power system often requires large-scale co-simulation and system parameter optimization. In this study, we investigate an architecture for parallel processing simulation of SSP (System Structure and Parametrization) and FMI (Functional Mock-up Interface), which enables high-speed computation by multi-core distribution. We combine Bayesian optimization and co-simulation, then we build a collaborative development platform for hybrid power systems design. We report performance experiments using hybrid electric vehicle simulation model published by JAMBE (Japan Automotive Model-Based Engineering center)
Pumped Thermal Energy Storage for Multi-Energy Systems Optimization
Get PDFGrid-scale energy storage systems are essential to support renewables integration and ensure grid flexibility simultaneously. As an alternative to electrochemical batteries, Pumped Thermal Energy Storage is a new storage technology suitable for grid-scale applications. This device stores electric energy as thermal exergy, which can be discharged directly for thermal uses or converted back into power depending on the necessities of the grid. The capability of the proposed energy storage to act as electric and thermal storage fits with the sector coupling necessities of multi-energy systems in which electrical and thermal energy carriers are involved. This paper investigates the effects on optimal grid management of integrating a Brayton Pumped Thermal Energy Storage into a multi-energy system. The case study includes renewable generation from photovoltaic modules and residential and industrial users' electrical and thermal load profiles. The system day-ahead optimization, performed through a Mixed Integer Linear Programming approach, aims to minimize the operational cost computed over a 24-hour horizon. The simulation highlights how the proposed storage technology interacts with the users' requirements during different seasons. The final results highlight that using multi-energy storage (i.e., providing power, heating, and cooling) brings a 5% reduction in operating costs during the year compared to a traditional electric-to-electric storage operation
Visualization of Industrial Production Processes using 3D Simulation Software for Enhanced Decision-Making
Get PDFThis paper explores the use of 3D simulation software for visualizing industrial production processes and its potential to enhance decision-making for improved production efficiency, quality, and profitability. Industrial production processes are complex and involve many variables and factors that can interact in unpredictable ways. Visualization helps to simplify these complex interactions, identify patterns and relationships, and enable more informed decision-making.The research question that guides this paper is: How can the use of 3D simulation software for visualization of industrial production processes improve decision-making and optimize production efficiency, quality, and profitability? This paper will investigate the benefits and challenges of using 3D simulation software for visualizing industrial production processes, including the ability to identify bottlenecks, and optimize the production process. Further, the paper examines the role of visualization in enabling more informed decision-making, including the ability to analyze production data and make data-driven decisions. To illustrate this, an industrial automation case study consisting of a manufacturing industry modelled in a 3D simulation software has been presented.The results of this 3D-simulation model provide insights into the advantages and disadvantages of utilizing 3D simulation software to visualize industrial manufacturing processes. The article further presents the significance of these findings for production managers, engineers, and decision-makers. Thus, the purpose of this study is to help readers understand how using 3D simulation software for visualization of industrial production processes can improve decision-making and optimize production efficiency, quality, and profitability
Data-driven reinforcement learning-based parametrization of a thermal model in induction traction motors
Get PDFMonitoring the temperature of induction traction motors is crucial for the safe and efficient operation of railway propulsion systems. Several thermal models were developed to capture the thermal behaviour of the induction motors. With proper calibrating of the thermal model parameters, they can be used to predict the motor’s temperature. Moreover, calibrated thermal models can be used in simulation to evaluate the motor’s performance under different operating conditions and find the optimal control strategies.Parameterization of the thermal model is usually performed in dedicated labs where the induction motor is operated under predefined operating conditions and calibrating algorithms are then used to find the model’s parameters. With the development of digital tools, including smart sensors, Internet of Things (IoT) devices, software applications, and various data collection platforms, operational data can be collected and used later to calibrate the parameters of the thermal model. Nevertheless, calibrating the model’s parameters from operational data collected from different driving cycles is challenging as the model has to capture the thermal behaviour from all driving cycles’ data.In this paper, a data-driven reinforcement learning-based parametrization method is proposed to calibrate a thermal model in induction traction motors. First, the thermal behaviour of the induction motor is modelled as a thermal equivalent network. Second, a reinforcement learning (RL) agent is designed and trained to calibrate the model parameters using the data collected from multiple driving cycles. The proposed method is validated by numerical simulation results. The results showed that the trained RL agent came up with a policy that adeptly handles diverse driving cycles with different performance characteristics