Designing latent heat thermal energy storage (LHTES) systems is computationally expensive due to the reliance on slow computational fluid dynamics (CFD) simulations. This study overcomes this bottleneck by developing a hybrid physics-informed neural network (PINN) framework. This PINN, governed by a 0D lumped-capacitance physical model, was trained on a sparse dataset of only 15 validated conjugated heat transfer (CFD) simulations. The resulting digital twin demonstrated exceptional fidelity, achieving a coefficient of determination ( R 2 ) greater than 0.999 against the ground truth data. This validated, instantaneous surrogate model was then coupled with a non-dominated sorting genetic algorithm II (NSGA-II) to perform a comprehensive multi-objective design optimization (MODO). The optimization autonomously navigated the fundamental thermo-hydraulic trade-off by simultaneously maximizing total discharged heat ( Q tot ) and average power ( P avg ) while minimizing pumping power ( W pump ). The balanced optimal designs on the global Pareto front matched the thermal performance of the best baseline (Flat-22), while reducing pumping power. This study demonstrates a powerful PINN-driven framework that transforms the LHTES design process from slow, manual evaluation to a rapid, autonomous exploration of the entire continuous design space, enabling the discovery of holistically optimized solutions. • Hybrid PINN framework accurately models LHTES using experiment and CFD results • Physics-regularized learning ensures consistency in sparse data regimes • 0D lumped model enables instantaneous prediction of transient dynamics • Multi-objective optimization reveals global thermo-hydraulic trade-offs • Optimized configurations balance thermal power and pumping energy costs
Han et al. (Tue,) studied this question.