ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers
Solving for detailed chemical kinetics remains one of the major bottlenecks for
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
A methodology for estimating hypersonic engine performance by coupling supersonic reactive flow simulations with machine learning techniques
We propose a methodology used to estimate the performance of hypersonic engines by
coupling some machine learning methods with a generated CFD database and one …
coupling some machine learning methods with a generated CFD database and one …
An adaptive time-integration scheme for stiff chemistry based on computational singular perturbation and artificial neural networks
We leverage the computational singular perturbation (CSP) theory to develop an adaptive
time-integration scheme for stiff chemistry based on a local, projection-based, reduced order …
time-integration scheme for stiff chemistry based on a local, projection-based, reduced order …
Reduced-order modeling of supersonic fuel–air mixing in a multi-strut injection scramjet engine using machine learning techniques
Dual-mode ramjet/scramjet engines promise extended flight speed range and are the
commonly preferred air-breathing propulsion system from within the family of hypersonic …
commonly preferred air-breathing propulsion system from within the family of hypersonic …
Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion
Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ
a projection of the thermochemical state onto a low-dimensional manifold within state space …
a projection of the thermochemical state onto a low-dimensional manifold within state space …
[HTML][HTML] Assessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasma-Assisted Ignition to Turbulent Flame Propagation
Combustion involves the study of multiphysics phenomena that includes fluid and chemical
kinetics, chemical reactions and complex nonlinear processes across various time and …
kinetics, chemical reactions and complex nonlinear processes across various time and …
CRK-PINN: A physics-informed neural network for solving combustion reaction kinetics ordinary differential equations
S Zhang, C Zhang, B Wang - Combustion and Flame, 2024 - Elsevier
Recently, artificial neural networks (ANNs) have been frequently embedded in
computational fluid dynamics (CFD) solvers as surrogate tools for solving chemical reaction …
computational fluid dynamics (CFD) solvers as surrogate tools for solving chemical reaction …
Stiffness-reduced neural ODE models for data-driven reduced-order modeling of combustion chemical kinetics
HE Dikeman, H Zhang, S Yang - AIAA SCITECH 2022 Forum, 2022 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2022-0226. vid A novel methodology for
data-driven reduced-order modeling of stiff ODE systems was developed. A combination of a …
data-driven reduced-order modeling of stiff ODE systems was developed. A combination of a …
Deep neural network based unsteady flamelet progress variable approach in a supersonic combustor
View Video Presentation: https://doi. org/10.2514/6.2022-2073. vid Higher dimensional
flamelet manifolds are essential in capturing the coupled effects of pressure gradients and …
flamelet manifolds are essential in capturing the coupled effects of pressure gradients and …
基于深度学习建表的宽域发动机火焰面燃烧模型构建与验证
于江飞， 连城阅， 汤涛， 唐卓， 汪洪波， 孙明波 - 力学学报, 2023 - lxxb.cstam.org.cn
以新型宽域发动机为动力的未来新一代飞行器的研发迫切需要CFD 方法来进行高效高精度的
辅助设计. 文章把传统的火焰面/进度变量燃烧模型与深度学习和神经网络方法相结合 …
辅助设计. 文章把传统的火焰面/进度变量燃烧模型与深度学习和神经网络方法相结合 …