Solution approaches to inverse heat transfer problems with and without phase changes: A state-of-the-art review

Abstract

Heat transfer problems (HTPs) with and without phase change are encountered in many areas of science and engineering. Some HTPs cannot be solved straightforwardly since certain heat transfer parameters are unknown. As a result, the need to find solutions to inverse HTPs arises. A number of approaches, methods, and algorithms for the solution of inverse HTPs have been developed and published in the past. Nonetheless, even the most recent handbooks dealing with inverse HTPs do not provide a comprehensive overview of the developments and advancements in this area (in particular the applications and comparisons of the methods). The present state-of-the-art review aims at filling this information gap and it also presents an overview of the most recent research works. Four classes of distinct methods and algorithms are addressed in detail; conventional (usually iterative and gradient-based) algorithms, nature-inspired meta-heuristic algorithms, techniques utilising artificial neural networks and machine learning, and algorithms based on fuzzy logic. The results obtained with the use of these methods are assessed and compared to each other. The intended contributions of the present review are twofold. Firstly, the review presents a comprehensive overview of the latest advancements and developments in the field of inverse HTPs, including cutting-edge research works. Secondly, it critically evaluates and compares the performance of different methods and algorithms, providing practical insights to researchers for the selection of suitable approaches to solve their specific inverse HTPs.