Generative design of high-power thermochemical energy storage reactors via multiphysics-driven topology optimization

The mismatch between poor heat transfer and slow reaction kinetics critically restricts the power density of thermochemical energy storage (TCES) systems. To overcome this bottleneck, this study proposes a multiphysics-driven topology optimization (TO) framework for closed TCES reactors, aiming to maximize reaction rates by actively balancing heat and mass transfer. Unlike conventional heuristic designs, the TO method enables the generative design of reactor structures that adaptively evolve according to specific thermochemical materials and operating conditions. High-fidelity numerical validations demonstrate that the topology-optimized reactors significantly outperform conventional straight-fin designs. Specifically, compared to the conventional straight-fin baseline, the optimized structures achieve a ∼ 40% reduction in reaction completion time and a > 60% increase in peak thermal power, attributed to the formation of hierarchical heat-conducting networks that effectively track the reaction front. Furthermore, a comprehensive parametric study reveals the competitive mechanism between thermal diffusion and mass transport: increasing heat transfer intensity promotes structural simplification, whereas fast-reacting materials drive the evolution of complex, dendrite-like features. This work establishes an integrated material–structure–operation design paradigm, providing theoretical guidance for developing next-generation high-power TCES systems.