Coaxial-Injector Surrogate Modeling Based on Reynolds-Averaged Navier–Stokes Simulations Using Deep Learning-Reference-Cited by-同舟云学术

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Coaxial-Injector Surrogate Modeling Based on Reynolds-Averaged Navier–Stokes Simulations Using Deep Learning

Published:2022-09 Issue:5 Volume:38 Page:783-798
ISSN:1533-3876
Container-title:Journal of Propulsion and Power
language:en
Short-container-title:Journal of Propulsion and Power

Author:

Krügener M.¹,Zapata Usandivaras J. F.¹,Bauerheim M.¹,Urbano A.¹

Affiliation:

1. ISAE-SUPAERO, University of Toulouse, 31055 Toulouse, France

Funder

Chair for Space Advanced Concepts Laboratory (SaCLaB) resulting from the partnership between Airbus Defence and Space, Ariane Group and ISAE-SUPAERO.

Region Occitanie, MODAR project

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Space and Planetary Science,Mechanical Engineering,Fuel Technology,Aerospace Engineering

Link

https://arc.aiaa.org/doi/pdf/10.2514/1.B38696

Reference43 articles.

1. Exploration of combustion instability triggering using Large Eddy Simulation of a multiple injector liquid rocket engine

2. Large Eddy Simulation of Combustion and Heat Transfer in a Single Element GCH4/GOx Rocket Combustor

3. Large-Eddy Simulation of Supercritical Combustion: Model Validation Against Gaseous H2–O2 Injector

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3. Design-Variable Hypernetworks for Flowfield Emulation and Shape Optimization of Compressor Airfoils;AIAA Journal;2023-12-28

4. Surrogate modeling of parameterized multi-dimensional premixed combustion with physics-informed neural networks for rapid exploration of design space;Combustion and Flame;2023-12

5. Discovering optimal flapping wing kinematics using active deep learning;Journal of Fluid Mechanics;2023-11-07

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