Generative adversarial networks with physical evaluators for spray simulation of pintle injector

Due to the adjustable geometry, pintle injectors are especially suitable for liquid rocket engines, which require a widely throttleable range. However, applying the conventional computational fluid dynamics approaches to simulate the complex spray phenomenon in the whole range still remains a great challenge. In this paper, a novel deep learning approach used to simulate instantaneous spray fields under continuous operating conditions is explored. Based on one specific type of neural network and the idea of physics constraint, a Generative Adversarial Networks with Physics Evaluators framework is proposed. The geometry design and mass flux information are embedded as inputs. After the adversarial training between the generator and discriminator, the generated field solutions are fed into two physics evaluators. In this framework, a mass conversation evaluator is designed to improve the training robustness and convergence. A spray angle evaluator, which is composed of a down-sampling Convolutional Neural Network and theoretical model, guides the networks to generate the spray solutions more closely according to the injection conditions. The characterization of the simulated spray, including the spray morphology, droplet distribution, and spray angle, is well predicted. This work suggests great potential for prior physics knowledge employment in the simulation of instantaneous flow fields.