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| Modeling and Application of Electromagnetic Field Finite Element Simulation Software MBSE for Electrical Equipment Integrated with AI |
| Jin Liang1,2, Jia Yufang1, Liu Lu1, Guo Shaonan1, Ma Tianci1 |
1. State Key Laboratory of Intelligence Power Distribution and System Equipment Hebei University of Technology Tianjin 300401 China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300401 China |
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Abstract Electromagnetic field finite element simulation software is crucial for electromagnetic design and analysis, particularly in the performance evaluation of complex electrical equipment, which often relies on costly foreign software with high barriers to entry. As the demand for electromagnetic field simulations continues to increase, existing commercial software fails to meet the specific customization needs of various engineering projects. It creates an urgent need to develop autonomous, lightweight, and intelligent simulation tools. This paper introduces the development of a prototype intelligent finite element simulation software, IFEM, which integrates artificial intelligence (AI) technology using a model-based systems engineering (MBSE) approach to create a visual software model.
Firstly, this paper proposes a bidirectional coupling software design method consisting of two key aspects: the bidirectional coupling between software developers and users as well as between numerical simulation and AI. The bidirectional coupling between developers and users aims to enable efficient information exchange, ensuring flexibility and adaptability in the software design process. Meanwhile, the bidirectional coupling between numerical simulation and AI retains the accuracy of numerical simulation while leveraging the efficiency of AI.
A four-layer architectural design process for the electromagnetic finite element simulation software is constructed, including operational analysis, system analysis, logical architecture, and software architecture. A graphical model represents the complex software structure, significantly enhancing transparency and improving collaboration among developers. During the development of the IFEM prototype, the bidirectional coupling of numerical simulation and AI is implemented using Python programming language and the PyCharm development environment, unifying the development and debugging of finite element and AI modules. It has the advantages of reduced programming workload, rich and user-friendly algorithm libraries, easy prototyping, and seamless AI integration.
Finally, a solver based on the node finite element method and Physics-Informed Neural Networks (PINN) is used to calculate the potential distribution between two-dimensional electrodes. Post-processing generates the electric field strength discrete values, field distribution maps, and contour lines. A comparison with COMSOL results shows that the mean absolute percentage errors (MAPE) for the potential and electric field strength are2.735×10-11 and 4.185×10-11, respectively. Additionally, an improved LinkNet model, integrated with the node finite element method, is used to optimize the magnetic field distribution of a solenoid. Compared to COMSOL, the MAPE for the 2D constant magnetic field vector potential and magnetic flux density magnitudes are0.054% and 2.889%, respectively. By replacing the finite element model with the improved LinkNet model, the Pareto front for solenoid optimization under two objectives is obtained. After training, the improved LinkNet model generates 250 samples in 7.5 seconds, whereas the finite element method takes 5 minutes and 43 seconds to generate the same number of samples.
Through case validation, IFEM demonstrates excellent computational accuracy and efficiency in solving problems such as 2D electrostatic fields and solenoid magnetic field optimization. The software also shows strong integration and scalability with AI algorithms. By adopting MBSE-based modeling, the transparency of the software design process and communication efficiency within the team are greatly improved, allowing for effective management and resolution of potential issues early in development. This approach has significantly enhanced the development efficiency of the electromagnetic field finite element simulation software. This paper provides effective methodologies for AI integration within specialized software development. Future research will focus on the software’s scalability and applicability enhancement, exploring complex engineering scenarios’ electromagnetic field simulation and optimization.
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Received: 02 July 2024
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2025, Vol. 40 
