[HTML][HTML] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …
chemical phenomena in time and length scales, including complex chemical reactions and …
Recent advances and effectiveness of machine learning models for fluid dynamics in the built environment
Indoor environmental quality is crucial for human health and comfort, necessitating precise
and efficient computational methods to optimise indoor climate parameters. Recent …
and efficient computational methods to optimise indoor climate parameters. Recent …
A critical review of physical models in high temperature multiphase fluid dynamics: Turbulent transport and particle-wall interactions
N Jain, A Le Moine… - Applied …, 2021 - asmedigitalcollection.asme.org
This review article examines the last decade of studies investigating solid, molten, and liquid
particle interactions with one another and with walls in heterogeneous multiphase flows …
particle interactions with one another and with walls in heterogeneous multiphase flows …
Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields
A deep-learning based approach is developed for efficient evaluation of thermophysical
properties in numerical simulation of complex real-fluid flows. The work enables a significant …
properties in numerical simulation of complex real-fluid flows. The work enables a significant …
Efficient premixed turbulent combustion simulations using flamelet manifold neural networks: A priori and a posteriori assessment
Flamelet-based reduced manifold tabulation is very useful to save computing time compared
to simulations of turbulent flames with detailed kinetics. However, conventional tabulation …
to simulations of turbulent flames with detailed kinetics. However, conventional tabulation …
Field inversion for data-augmented RANS modelling in turbomachinery flows
Turbulence modelling in turbomachinery flows remains a challenge, especially when
transition and separation phenomena occur. Recently, several research efforts have been …
transition and separation phenomena occur. Recently, several research efforts have been …
FluxNet: a physics-informed learning-based Riemann solver for transcritical flows with non-ideal thermodynamics
Traditional Riemann solvers fall into two broad categories: exact solvers, which require
multiple iterations to achieve high accuracy, and approximate linearized solvers, which …
multiple iterations to achieve high accuracy, and approximate linearized solvers, which …
Application of machine learning in low-order manifold representation of chemistry in turbulent flames
The Uniform Conditional State (UCS) and the Multidimensional Flamelet Manifold (MFM)
models are methods for the tabulation of chemistry in simulations of turbulent flames. The …
models are methods for the tabulation of chemistry in simulations of turbulent flames. The …
Gradient-harmonizing-based deep learning for thermophysical properties of carbon dioxide
C Ni, X Wang, H Liu, K Zhang, X Zheng… - Journal of Thermophysics …, 2023 - arc.aiaa.org
Carbon dioxide presents many unique advantages for cooling and power cycles under
supercritical or near-critical conditions, where the characterization of thermophysical …
supercritical or near-critical conditions, where the characterization of thermophysical …
[HTML][HTML] Application of dense neural networks for manifold-based modeling of flame-wall interactions
J Bissantz, J Karpowski, M Steinhausen, Y Luo… - Applications in Energy …, 2023 - Elsevier
Artifical neural networks (ANNs) are universal approximators capable of learning any
correlation between arbitrary input data with corresponding outputs, which can also be …
correlation between arbitrary input data with corresponding outputs, which can also be …