Computational Design Optimization and Reliability Assessment of Thermal Systems

Abstract

Transition to renewable energy solutions, while managing surging energy demand, calls for novel thermal designs. These designs, especially with the advent of additive manufacturing, are becoming increasingly complex and computationally driven. Optimization and reliability analysis of such complex designs typically require several evaluations of the quantities of interest. This is usually accomplished by querying computational models often involving expensive numerical methods like Finite Element Analysis and Computational Fluid Dynamics. To alleviate the computational cost of these design routines, surrogate models can be employed in place of the original model, as they are cheaper to evaluate. In addition, several models, both computational and experimental, are often available to describe a system of interest. These models have varying evaluation costs and fidelities. In general, an expensive high-fidelity model describes the system with the accuracy required for the task at hand, while lower-fidelity models are less accurate but computationally cheaper. In such situations, multi-fidelity procedures can combine information from different levels of fidelity to accelerate the optimization and reliability routines. In this work, these two concepts (i.e., surrogate modeling and multi-fidelity) are employed for optimization and reliability analysis of concentrated solar receiver tubes and heat exchangers.