A deep learning framework for supersonic turbulent combustion

2024/09/13 by Tong Zhao, Chong Wu, Runtong Zhu +4 · 12 citations
Chemistry · Engineering · #Aerodynamics and Acoustics in Jet Flows #Aeronautics #Aerospace engineering #Chemistry #Combustion #Combustion and flame dynamics #Computational Fluid Dynamics and Aerodynamics #Computer science #Engineering #Environmental science #Mechanics #Physics #Supersonic speed #Turbulence

paper · doi:10.1016/j.actaastro.2024.09.027

published in Acta Astronautica 225, 524-537 (Elsevier BV)

openalex publication_date 2024/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Citations

• Influences of H2O mass fraction and chemical kinetics mechanism on the turbulent diffusion combustion of H2–O2 in supersonic flows
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Wall heat transfer prediction in CH4/O2 and H2/O2 rocket thrust chambers using a non-adiabatic flamelet model
• CFD design capabilities for next generation high-speed aircraft
• Neural network approach to solve gas dynamics problems with chemical transformations
• Approach to combustion calculation using neural network
• Prediction model of temperature field in dual-mode combustors based on wall pressure
• Study on the thermal decomposition of an ionic liquid propellant [EMIm][BF4]