Topology optimization for conjugate heat transfer: Latest methodology advances, effects of design parameters and materials properties, and extended application scenarios

This review synthesizes recent advances in topology optimization (TO) for conjugate heat transfer (CHT) problems involving coupled fluid flow and thermal transport, and highlights their growing role in the innovative design of fluidic, thermal, and reactive devices. The paper first outlines the theoretical and computational framework of TO-CHT, updating the latest developments in design parameterization, flow and heat transfer modeling, and optimization algorithms. The review then provides a unique and systematic synthesis of how key factors such as design parameters, boundary conditions, and materials properties influence TO outcomes. Based on these foundations, comprehensive overview of TO applications across various thermal and energy devices is provided, including heat sinks, heat exchangers, chemical reactors, latent heat thermal energy storage systems, and many others. The reviewed research trends clearly demonstrate that studies of TO-CHT have grown at an exceptionally rapid pace in recent years, owing to its unique ability to generate and propose non-conventional nature-inspired device designs. There is a noticeable shift toward addressing increasing complexity in both physical phenomena and geometric configurations, considering more and more irregular 3D design domains, non-uniform and unsteady heat loads, multiple materials, complex flow patterns, multi-physics interactions, and dynamic/cyclic operations. Despite significant progress, current TO-CHT still face challenges related to computational efficiency, simulation accuracy, design robustness, operational constraints, and manufacturability, sometimes resulting in only incremental or marginal performance gains, or impractical use. Experimental validation also remains limited, which hinders thorough and convincing proof of concept. Future methodological efforts should better leverage artificial intelligence (AI)-assisted tools to reduce computational cost through multi-level modeling and to enhance optimization robustness via gradient-free optimizers. On the simulation side, higher-fidelity and more complex multiphysics models should be employed to more accurately capture the underlying real-world phenomena. In parallel, experimental validation at multiple scales should be strengthened using high-resolution flow and thermal diagnostics, providing valuable feedback to further improve simulation accuracy and predictive capability. On the implementation side, manufacturability must be improved by integrating cost, fabrication, and operational constraints into the TO-CHT framework, supported by advances in additive manufacturing (AM).