Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022 - Elsevier
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 …

Emerging trends in numerical simulations of combustion systems

V Raman, M Hassanaly - Proceedings of the Combustion Institute, 2019 - Elsevier
Numerical simulations have played a vital role in the design of modern combustion systems.
Over the last two decades, the focus of research has been on the development of the large …

Deep recurrent optical flow learning for particle image velocimetry data

C Lagemann, K Lagemann, S Mukherjee… - Nature Machine …, 2021 - nature.com
A wide range of problems in applied physics and engineering involve learning physical
displacement fields from data. In this paper we propose a deep neural network-based …

Deep learning for presumed probability density function models

MTH de Frahan, S Yellapantula, R King, MS Day… - Combustion and …, 2019 - Elsevier
In this work, we use machine learning (ML) techniques to develop presumed probability
density function (PDF) models for large eddy simulations (LES) of reacting flows. The joint …

Effect of multiscalar subfilter PDF models in LES of turbulent flames with inhomogeneous inlets

BA Perry, ME Mueller - Proceedings of the Combustion Institute, 2019 - Elsevier
To apply reduced-order manifold combustion models in Large Eddy Simulations (LES) of
systems with multiple inlets, it is necessary to incorporate more than one mixture fraction. As …

Investigation of the derivation and consistency of the quasi-two-dimensional flamelet models for non-premixed flames

P Yu, R Kurose, H Watanabe - Physics of Fluids, 2023 - pubs.aip.org
Three non-premixed quasi-two-dimensional flamelet (Q2DF) models can be derived via
integrating one-dimensional flamelet libraries, which are generated by premixing the third …

[HTML][HTML] Transported and presumed probability density function modeling of the Sandia flames with flamelet generated manifold chemistry

V Jaganath, M Stoellinger - Physics of Fluids, 2021 - pubs.aip.org
The first modeling results for Sandia flames D, E, and F using the flamelet generated
manifold reduced chemistry model with a transported probability density function (TPDF) …

Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures

K Ferguson, BE Morgan - Journal of Fluid Mechanics, 2024 - cambridge.org
We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI)
problem with a configuration based on the experiments of Suchandra & Ranjan (J. Fluid …

A computationally efficient turnkey approach to turbulent combustion modeling: From elusive fantasy to impending reality

ME Mueller - AIAA Scitech 2019 Forum, 2019 - arc.aiaa.org
The fundamental shortcoming of all current approaches to turbulent combustion modeling is
the reliance on extensive a priori knowledge of a combustion system to be simulated. In …

Cascades of turbulent kinetic energy and multicomponent scalars in a momentum-scalar coupling turbulence driven by multiple mechanisms under homogeneous …

W Zhao - Physical Review Fluids, 2024 - APS
Momentum-scalar coupling turbulence, a phenomenon observed in both natural and
engineering contexts, involves the intricate interaction between multicomponent scalars and …