Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
[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 …
Highly accurate heat release rate marker detection in NH3–CH4 cofiring through machine learning and domain knowledge-based selection integration
Ammonia emerges as a promising substitute for traditional fuels, offering a potential
reduction in fossil fuel consumption and the associated emissions. Given its weak reactivity …
reduction in fossil fuel consumption and the associated emissions. Given its weak reactivity …
Data-assisted combustion simulations with dynamic submodel assignment using random forests
This investigation outlines a data-assisted approach that employs random forest classifiers
for local and dynamic submodel assignment in turbulent-combustion simulations. This …
for local and dynamic submodel assignment in turbulent-combustion simulations. This …
Progress and prospects of artificial intelligence development and applications in supersonic flow and combustion
J Le, M Yang, M Guo, Y Tian, H Zhang - Progress in Aerospace Sciences, 2024 - Elsevier
Due to the significant improvement in computing power and the rapid advancement of data
processing technologies, artificial intelligence (AI) has introduced new tools and …
processing technologies, artificial intelligence (AI) has introduced new tools and …
Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows
In the past decades, Deep Learning (DL) frameworks have demonstrated excellent
performance in modeling nonlinear interactions and are a promising technique to move …
performance in modeling nonlinear interactions and are a promising technique to move …
Modeling of sub-grid conditional mixing statistics in turbulent sprays using machine learning methods
Deep artificial neural networks (ANNs) are used for modeling sub-grid scale mixing
quantities such as the filtered density function (FDF) of the mixture fraction and the …
quantities such as the filtered density function (FDF) of the mixture fraction and the …
Data-driven prediction of flame temperature and pollutant emission in distributed combustion
The flame temperature and pollutant emission (of NO and CO) characteristics in distributed
combustion were examined using data-driven artificial neural network (ANN) approach …
combustion were examined using data-driven artificial neural network (ANN) approach …
Data-driven discovery of heat release rate markers for premixed NH3/H2/air flames using physics-informed machine learning
The spatial distribution of heat release rate (HRR) is important for flame front identification.
However, direct measurement of HRR is impossible using the current experimental …
However, direct measurement of HRR is impossible using the current experimental …