Profile design and optimization of liquid rocket engine nozzle based on machine learning
Li Chenpei, Zhou Chenchu, Gao Yushan, Hu Haifeng
(Xi′an Aerospace Propulsion Institute, Xi′an 710100, China)
Abstract： The nozzle is an important part of the liquid rocket engine to provide the thrust. The structure of the nozzle profile could directly affect the flow of combustion gas in the nozzle, and then impact on the performance of the engine. In this paper, Bspline curve is used to construct the paraboloid profile of the nozzle. Based on the Computational Fluid Dynamics (CFD) flow field of sample set, the nozzle performance is evaluated with specific impulse as the optimal variable. The results show that the optimized nozzle profile obtained by the surrogate model is consistent with that by the characteristic line method, and the maximum error is 0.28%. In this work, the internal profile design and optimization is realized via the surrogate model and mesh auto deformation method, and the optimization efficiency is improved.
Key words : internal profile; specific impulse; machine learning; mesh auto deformation method
