Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows
Abstract Large Eddy Simulations (LES) of turbulent reacting flows carried out with detailed
kinetic mechanisms have a key role for the discovery of the physical and chemical …
kinetic mechanisms have a key role for the discovery of the physical and chemical …
On the application of principal component transport for compression ignition of lean fuel/air mixtures under engine relevant conditions
Principal component transport-based data-driven reduced-order models (PC-transport
ROM) are being increasingly adopted as a combustion model of turbulent reactive flows to …
ROM) are being increasingly adopted as a combustion model of turbulent reactive flows to …
Artificial intelligence as a catalyst for combustion science and engineering
Combustion and energy conversion play critical roles in all facets of environmental and
technological applications, including the utilization of sustainable energy sources for power …
technological applications, including the utilization of sustainable energy sources for power …
[PDF][PDF] Reduced-order modeling of reacting flows using data-driven approaches
Data-driven modeling of complex dynamical systems is becoming increasingly popular
across various domains of science and engineering. This is thanks to advances in numerical …
across various domains of science and engineering. This is thanks to advances in numerical …
Acceleration of the complex reacting flow simulation with a generalizable neural network based on meta-learning
T Bai, Y Huai, T Liu, S Jia, M You, N Chang - Fuel, 2024 - Elsevier
A chemical decoupled method is proposed in this paper. To accelerate the complex reacting
flow simulation, the chemistry and flow computation is decoupled by a Neural Network (NN) …
flow simulation, the chemistry and flow computation is decoupled by a Neural Network (NN) …
[HTML][HTML] Hierarchical higher-order dynamic mode decomposition for clustering and feature selection
This article introduces a novel, fully data-driven method for forming reduced order models
(ROMs) in complex flow databases that consist of a large number of variables. The algorithm …
(ROMs) in complex flow databases that consist of a large number of variables. The algorithm …
A Bayesian optimization framework for the control of combustion instability of a bluff-body stabilized combustor
Control of combustion instability for a realistic gas-turbine combustor is challenging. This
work aims to establish an efficient numerical framework for optimization to improve the …
work aims to establish an efficient numerical framework for optimization to improve the …
[HTML][HTML] Data-driven identification of precursors of flashback in a lean hydrogen reheat combustor
In this work, we propose a data-driven framework to identify precursors of extreme events in
turbulent reacting flows. Specifically, we tackle the problem of flashback prediction in a lean …
turbulent reacting flows. Specifically, we tackle the problem of flashback prediction in a lean …
[PDF][PDF] Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based Tabulation
This chapter describes the use of machine learning (ML) algorithms with the linear-eddy
mixing (LEM) based tabulation for modeling of subgrid turbulencechemistry interaction. The …
mixing (LEM) based tabulation for modeling of subgrid turbulencechemistry interaction. The …