Generative Adversarial Networks with Physical Evaluators for Spray Simulation of Pintle Injector
arXiv:2101.01217 · doi:10.1063/5.0056549
Abstract
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.
24 pages, 9 figures, 2 tables
References in corpus (6)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Conditional Generative Adversarial Nets
- A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
- Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
- DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks
- A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks