Generative adversarial networks with physical evaluators for spray simulation of pintle injector-Reference-Cited by-同舟云学术

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

Published:2021-07-01 Issue:7 Volume:11 Page:075007
ISSN:2158-3226
Container-title:AIP Advances
language:en
Short-container-title:AIP Advances

Author:

Ma Hao¹^ORCID,Zhang Botao²,Zhang Chi³^ORCID,Haidn Oskar J.¹

Affiliation:

1. Department of Aerospace and Geodesy, Technical University of Munich, 85748 Garching, Germany

2. Key Laboratory for Liquid Rocket Engine Technology, Xi’an Aerospace Propulsion Institute, 710100 Xi’an, China

3. Department of Mechanical Engineering, Technical University of Munich, 85748 Garching, Germany

Funder

China Scholarship Council

Deutsche Forschungsgemeinschaft

Publisher

AIP Publishing

Subject

General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0056549

Reference40 articles.

1. Pintle Injectors

2. Liquid-Propellant Rocket Engine Throttling: A Comprehensive Review

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1. A comprehensive deep learning geometric shape optimization framework with field prediction surrogate and reinforcement learning;Physics of Fluids;2024-04-01

2. Flow and spray characteristics of a gas–liquid pintle injector under backpressure environment;Physics of Fluids;2024-04-01

3. A New Modeling Method for Spray Angle Prediction of Liquid–Liquid Pintle Injector;AIAA Journal;2023-11

4. A hybrid safety monitoring framework for industrial FCC disengager coking rate based on FPM, CFD, and ML;Process Safety and Environmental Protection;2023-07

5. Review of atomization and mixing characteristics of pintle injectors;Acta Astronautica;2022-11

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