Integration of multi-objective reliability-based design optimization into thermal energy management: Application on phase change material-based heat sinks. (September 2021) - Debich, B.; Yaich, A.; Dammak, K.; El Hami, A.; Gafsi, W.; Walha, L.; Haddar, M.
Abstract: Phase change material-based heat sinks are widely used in several industrial applications such as in the field of industrial automation and mechatronics. Nowadays, several researchers aim to obtain an optimal design of these systems in order to ameliorate its performances. Nevertheless, uncertainties are not considered for the majority of these studies. Furthermore, it has been confirmed that deterministic multi-objective optimization (DMOO) may lead to an unreliable design. In this study, a new methodology that leads to perform the Multi-Objective Reliability-Based Design Optimization (MORBDO) for thermal management of a passive cooling system is proposed. It consists in coupling the Finite Element Model (FEM), MORBDO procedures and surrogate approaches. Kriging predictor is used to construct metamodels and then validated using Cross-Validation (CV) and error predictions. A numerical investigation is carried out to study the different DMOO and MORBDO approaches. In this study, a 3D PCM-based round pin-fin heat sink is detailed. The aim of this problem is to minimize two objective functions: the cost (total volume V ) and the final cooling time t f by considering both thermal and physical constraints. For this purpose, geometrical uncertain parameters are considered such as length L, height H and ϕ pin diameter of the heat sink. This study leads to develop a well-distributed reliable Pareto solutions by combining the Robust Hybrid Method (RHM) and the Constrained Non-dominated Sorting Genetic Algorithm (C-NSGA-II). Then, the efficiency of the proposed MORBDO-RHM approach for PCM-based heat sink is verified. Highlights: Numerical investigation of 3D transient thermal management of PCM-based heat sink. A new methodology to perform the MORBDO for PCM-based thermal management. sink. The FE model, surrogate model techniques and MORBDO methods are coupled. The surrogate models are constructed and validated using kriging predictor. A novel method MORBDO-RHM based on C-NSGA-II is developed.