  Due to the adjustable geometry, pintle injectors are specially suitable for
the liquid rocket engines which require a widely throttleable range. While
applying the conventional computational fluid dynamics approaches to simulate
the complex spray phenomena in the whole range still remains to be 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 networks and the idea of physics
constraint, a Generative Adversarial Networks with Physics Evaluators (GAN-PE)
framework is proposed. The geometry design and mass flux information are
embedded as the inputs. After the adversarial training between the generator
and discriminator, the generated field solutions are fed into the two physics
evaluators. In this framework, mass conversation evaluator is designed to
improve the training robustness and convergence. And the spray angle evaluator,
which is composed of a down-sampling CNN and theoretical model, guides the
networks generating the spray solutions more according with the injection
conditions. The characterization of the simulated spray, including the spray
morphology, droplet distribution and spray angle, is well predicted. The work
suggests a great potential of the prior physics knowledge employment in the
simulation of instantaneous flow fields.
