Structural Optimal Design of Regenerative Cooling with Variable Section Based on Neural Network

Nowadays, there are some problems such as narrow range in parametric contrast and dependence on empirical relations existing in the research of structural optimization about regenerative cooling. According to the characteristics of aero-engine combustors, a new structure of channels adopted regenerative cooling with variable sectional width was proposed. Neural network combined with numerical stimulation results was selected with the aim of RSD (Relative Standard Deviation) of fuel temperature in channel exit, maximum temperature of hot wall and RSD of hot wall temperature. Therefore, the changing rules of targeted function in different slot width sum, slot width ratio and fin height within the full parameter range were predicted. The results demonstrated that when the slot width sum is relatively small, heat transfer will be strengthened by increasing the fin height. However, when the slot width sum became relatively large, heat transfer will be enhanced by reducing the fin height. This explains the reasons why controversial conclusion about the relationship between fin height and heat transfer was presented in several essays. Moreover, there is an optimal range of slot width ratio that can make all these three objective functions lowest. The temperature of hot wall and its non-uniformity decline by increasing slot width sum, and the fall of the fin height leads to the difference of fuel temperature at the outlet of channels deceasing. Multiple structures with better performance in comprehensive flow-heat transfer can be obtained from the range of prediction. After this optimization, the weighted value of three objective functions dropped by 9.09%. © 2021, Editorial Department of Journal of Propulsion Technology. All right reserved.