Abstract
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.
Highlights
The field solutions are generated by G, and the other three parts are employed to guarantee that the outputs catch the spray morphology and obey the operating conditions
The Generative Adversarial Networks (GANs) is the base of the proposed network framework; the G captures the real spray data distribution, which corresponds to the operation conditions; and the D estimates the probability that a condition–sample pair came from the training data rather than G
The present evaluator is composed of two parts: one is the theoretical model of the spray angle and the other is a Convolutional Neural Network (CNN) encoder to estimate the spray angles from the generated field solutions
Summary
Due to their wider throttling range and greater combustion stability, pintle injectors are especially suitable for liquid rocket engines that require deep, fast, and safe throttling, such as the descent propulsion system in the Apollo program and the reusable Merlin engine of SpaceX.. In the previous spray simulations of pintle injectors, the changes were only considered under discrete condition combinations over a limited number of select operating points.. Able to directly obtain field solutions that obey physical laws and operating conditions.. Able to directly obtain field solutions that obey physical laws and operating conditions.25 In these works, Partial Differential Equations (PDEs) were employed in the loss function to explicitly constrain the network training.. In the state-of-the-art neural network methods, Generative Adversarial Networks (GANs) proposed by Goodfellow et al. are efficient to generate the instantaneous flow fields.. By introducing mass conversation and spray angle models as the two evaluators, this framework has a better training convergence and predictive accuracy. The trained model is able to simulate the macroscopic morphology and characterization of the instantaneous flow fields under different conditions.
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