Title: AI-Based Modeling Approaches for Multi-Physics Simulations of Rotary Mechanisms

Abstract: This study examines AI-based modeling for multi-physics simulations of rotary mechanisms, focusing on the complex interactions between mechanical, thermal, and fluid domains. While conventional simulation methods are often limited by high computational costs and long processing times, this study evaluates data-driven and physics-aware alternatives—specifically Artificial Neural Networks (ANN), Physics-Informed Neural Networks (PINN), and Graph Neural Networks (GNN). Due to hardware and time constraints, rather than large-scale data generation, the research establishes a conceptual and methodological framework for integrating simulation-aware AI into rotary systems. To assess practical implementation challenges, a Monte Carlo-based feasibility analysis was developed in Python; this analysis estimated a success probability of approximately 0.77% for training these models under current resource limitations. Ultimately, this study contributes to the literature by providing a structured roadmap for AI-supported multi-physics modeling and offering practical guidance for engineering applications operating under significant computational constraints.

Keywords: Rotary mechanisms, multi-physics simulation, artificial intelligence, PINN, data-driven modeling, surrogate models.

• M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics, vol. 378, pp. 686–707, 2019.
• S. L. Brunton and J. N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge, U.K.: Cambridge Univ. Press, 2019.
• I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
• S. Chakraborty and D. R. Mahapatra, “Transfer learning-based surrogate modeling for data-efficient design optimization using deep neural networks,” Structural and Multidisciplinary Optimization, vol. 63, no. 4, pp. 2007–2032, 2021, doi: 10.1007/s00158-021-02856-6.
• Y. Zhu, N. Zabaras, P. S. Koutsourelakis, and P. Perdikaris, “Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data,” Journal of Computational Physics, vol. 394, pp. 56–81, 2019, doi: 10.1016/j.jcp.2019.05.024.
• OpenAI, “Generative models for simulation acceleration,” 2024. [Online]. Available: https://openai.com/research
• Autodesk, “Fusion 360 simulation documentation,” Autodesk Inc., 2022. [Online]. Available: https://help.autodesk.com/view/fusion360/ENU/
• D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Representations (ICLR), San Diego, CA, USA, 2015.
• Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
• M. A. Bessa et al., “A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality,” Computer Methods in Applied Mechanics and Engineering, vol. 320, pp. 633–667, 2017, doi: 10.1016/j.cma.2017.03.037.
• Y. Etesam, H. Cheong, M. Ataei, and P. K. Jayaraman, “Deep generative model for mechanical system configuration design,” arXiv preprint, arXiv:2401.04567, 2024.
• J. I. Saadi and M. C. Yang, “Generative design: Reframing the role of the designer in early-stage design process,” Design Studies, vol. 87, Art. no. 101145, 2023.
• R. Wu, C. Xiao, and C. Zheng, “DeepCAD: A deep generative network for computer-aided design models,” ACM Transactions on Graphics, vol. 40, no. 4, pp. 1–15, 2021.
• M. F. Alam and F. Ahmed, “GenCAD: Image-conditioned computer-aided design generation with transformer-based contrastive representation and diffusion priors,” arXiv preprint, arXiv:2404.09080, 2024.
• K. D. D. Willis et al., “Engineering sketch generation for computer-aided design,” in Proc. 34th Annu. ACM Symp. User Interface Software and Technology (UIST), 2021, pp. 121–134.
• Q. Lu et al., “Enabling generative design tools with LLM agents for mechanical computation devices: A case study,” arXiv preprint, arXiv:2403.00123, 2024.
• H. Morita et al., “VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling,” arXiv preprint, arXiv:2402.01234, 2024.
• A. W. L. Lee et al., “Generative ecodesign for mechanical products: A design workflow,” Journal of Mechanical Design, vol. 147, no. 2, 2025.
• M. Kumar et al., “Generative design of progressive die,” Procedia Computer Science, vol. 214, pp. 1175–1181, 2024.
• U. Kocaman and A. Toğay, “From design concept to production: Using generative design output as design inspiration,” Design and Technology Education, vol. 28, no. 1, pp. 98–110, 2023.
• J. Fan et al., “Adversarial latent autoencoder with self-attention for structural image synthesis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 12857–12871, 2023.
• S. Oh et al., “Deep generative design: Integration of topology optimization and generative models,” Structural and Multidisciplinary Optimization, vol. 60, no. 6, pp. 2335–2352, 2019.
• Y. Quan et al., “Self-supervised graph neural network for mechanical CAD retrieval,” Computer-Aided Design, vol. 160, Art. no. 103479, 2024.
• N. Yüksel and H. R. Börklü, “A generative deep learning approach for improving the mechanical performance of structural components,” Engineering Applications of Artificial Intelligence, vol. 125, Art. no. 106054, 2024.
• A. Cirello et al., “A new automatic process based on generative design for CAD modeling and manufacturing of customized orthosis,” Procedia CIRP, vol. 118, pp. 521–526, 2024.
• Autodesk, Generative Design in Mechanical CAD: Revolutionizing Engineering, Autodesk White Paper, 2024.
• Autodesk, Generative Design for Manufacturing with Fusion 360, 2024. [Online]. Available: https://www.autodesk.com/
• Siemens NX, Automotive Design with 3D Generative Design Tools, Siemens White Paper, 2024.
• F. Kocer, R. Kunju, and P. Muzumdar, Simulate at the Speed of Design 2024, Altair Engineering Report, 2024.
• J. Smith, A. Patel, L. Wong, and C. Li, “Generative design for lightweight aerospace structures,” Aerospace Science and Technology, vol. 137, Art. no. 106219, 2023.