Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
This work addresses a domain-specific problem for aerospace engineering by reducing computational costs in nozzle optimization, though it is incremental as it builds on existing neural network methods.
The paper tackled the challenge of optimizing fluidic injection parameters for a single expansion ramp nozzle across multiple operating conditions by using a pretrained neural network to replace CFD simulations, achieving a 1.14% improvement in the average thrust coefficient.
Fluidic injection provides a promising solution to improve the performance of overexpanded single expansion ramp nozzle (SERN) during vehicle acceleration. However, determining the injection parameters for the best overall performance under multiple nozzle operating conditions is still a challenge. The gradient-based optimization method requires gradients of injection parameters at each design point, leading to high computational costs if traditional computational fluid dynamic (CFD) simulations are adopted. This paper uses a pretrained neural network model to replace CFD during optimization to quickly calculate the nozzle flow field at multiple design points. Considering the physical characteristics of the nozzle flow field, a prior-based prediction strategy is adopted to enhance the model's transferability. In addition, the back-propagation algorithm of the neural network is adopted to quickly evaluate the gradients by calling the computation process only once, thereby greatly reducing the gradient computation time compared to the finite differential method. As a test case, the average nozzle thrust coefficient of a SERN at seven design points is optimized. An improvement in the thrust coefficient of 1.14% is achieved, and the time cost is greatly reduced compared with the traditional optimization methods, even when the time to establish the database for training is considered.