Advances in AI-Based Optimization for Multiphysics Fluid Dynamics in Product Design Engineering

Abstract

The integration of Artificial Intelligence (AI) with multiphysics fluid dynamics modeling has emerged as a transformative approach in product design engineering. As industries demand faster, more efficient, and cost-effective solutions, traditional Computational Fluid Dynamics (CFD) methods—while powerful—are often limited by high computational costs, long simulation times, and the complexity of solving coupled multiphysics phenomena. AI-based optimization addresses these challenges by enhancing predictive accuracy, accelerating simulation workflows, and enabling real-time design iteration across a range of fluid dynamics applications, including aerodynamics, thermal management, and fluid-structure interaction. Recent advances have focused on the use of machine learning algorithms, particularly deep learning, surrogate modeling, and reinforcement learning, to approximate complex flow behavior and reduce reliance on full-scale numerical simulations. These models learn from large datasets generated by high-fidelity simulations or experimental data to predict outcomes under new conditions with remarkable speed and accuracy. Hybrid approaches combining physics-informed neural networks (PINNs) with CFD solvers enable adherence to governing physical laws while leveraging AI’s pattern recognition capabilities, offering a new paradigm for solving Navier-Stokes and other coupled partial differential equations in multiphysics environments. In product design engineering, AI-driven optimization frameworks are increasingly employed to automate geometry generation, refine mesh quality, minimize drag, optimize heat transfer, and manage multiphase flow systems. These tools enable engineers to explore vast design spaces, identify optimal solutions rapidly, and adapt to changing performance constraints. Furthermore, the use of AI in uncertainty quantification and sensitivity analysis contributes to more robust and resilient product development cycles. This paper reviews current advancements in AI-integrated multiphysics fluid dynamics, highlighting applications in automotive, aerospace, energy systems, and biomedical device design. It also identifies limitations in current methodologies, including data scarcity, model generalization, and integration complexity. The future of AI in this domain lies in the convergence of explainable AI, edge computing, and autonomous simulation systems that can continuously learn and adapt, driving innovation in next-generation product development.