Optimal Design Arrangement for Suppressing Supercritical CO2 Heat Transfer Deterioration by Deep Learning and Genetic Algorithm

Supercritical carbon dioxide (CO2) is a promising working fluid for advanced power cycles. However, under high heat flux and low mass flux, its heat transfer performance can deteriorate severely, posing significant risks to system safety and efficiency. Inserting obstacles into flow channels is an effective way to suppress such heat transfer deterioration (HTD). In this study, the body-centered cubic (BCC) lattice structure is taken as an example to investigate the effects of the number and arrangement of BCC units on the flow and heat transfer of supercritical CO2 using numerical simulation, deep learning, and genetic algorithms. The results show that placing a BCC lattice structure upstream of the HTD temperature peak effectively improves local heat transfer, and the deterioration zone is shifted downstream. For a fixed number of BCC units, different spatial arrangements have little impact on pressure drop and only a limited effect on heat transfer enhancement. However, their influence on the suppression of HTD is very significant. Based on the analysis of the optimal arrangement, an approximate optimal method is obtained, in which BCC structures are inserted sequentially at locations 0.5 to 2 tube diameters (D) upstream of each wall temperature peak. A simplified yet effective design strategy is also proposed: the first BCC structure is placed 0.5 to 2 D upstream of the smooth tube’s temperature peak, and the remaining BCC units are then distributed uniformly along the subsequent flow length. In this way, effective suppression of heat transfer deterioration is achieved.