Topology optimization of heat exchangers: A review

• Published in: Energy (Oxford) Vol. 252; p. 124053
• Main Authors: Fawaz, Ahmad, Hua, Yuchao, Le Corre, Steven, Fan, Yilin, Luo, Lingai
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2022; Elsevier BV; Elsevier
• Subjects: Additive manufacturing; Algorithms; Conjugate heat transfer; Engineering Sciences; Flow distribution; Fluids mechanics; Heat exchangers; Heat transfer; Machine Learning; Mechanical engineering; Mechanics; Model reduction; Optimization techniques; Parameterization; Reviews; Statistics; Thermics; Topology; Topology optimization; Topology optimization; AI; Conjugate heat transfer; Heat exchangers; Additive manufacturing; Machine learning
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2022.124053

The critical importance of heat exchangers (HXs) on energy systems has been widely recognized, which can largely determine the overall efficiency. With the rise of the topology optimization (TO) and additive manufacturing techniques, it is anticipated that the TO may became a leading optimization technique in designing HX structures for the heat transfer intensification. Various algorithms for TO of HXs are dispersed in the literature, while a comprehensive and comparative review on their features, advantages, disadvantages, and limitations, is still lacking. Therefore, this paper aims at filling the literature gap by providing a comprehensive state-of-the art review on the TO for HXs over the past decades, so as to indicate the most promising technology roadmap. Each stage of the TO, i.e. the design parametrization, the heat transfer modeling, the optimization, and the final realization, is analyzed carefully in the corresponding section, highlighting the major pros, cons and challenges. Our statistics demonstrate that the current TO, though well-developed and fast improved, still have numerous limitations in handling the industrial HXs that hold the complicate structures and flow patterns. Eventually, three emerging schemes, i.e. machine learning, model order reduction, and moving morphable components, aimed to improve the efficiency of TO are also discussed. •Recent researches on TO of heat exchangers are reviewed following its workflow.•Current TO is found to be not powerful enough to handle industrial heat exchangers.•Various TO algorithms are compared to indicate the promising tech roadmap.•Emerging schemes to improve TO's efficiency are discussed.