[HTML][HTML] Predicting the effect of hydrogen enrichment on the flame describing function using machine learning

Y Shen, AS Morgans - International Journal of Hydrogen Energy, 2024 - Elsevier
As a promising strategy to mitigate carbon emissions, hydrogen enrichment of conventional
fuels is gaining increasing interest, but can lead to increased propensity to damaging …

Linear and nonlinear flame response prediction of turbulent flames using neural network models

N Tathawadekar, A Ösün, AJ Eder… - … Journal of Spray …, 2024 - journals.sagepub.com
Modelling the flame response of turbulent flames via data-driven approaches is challenging
due, among others, to the presence of combustion noise. Neural network methods have …

[HTML][HTML] A parsimonious system of ordinary differential equations for the response modeling of turbulent swirled flames

G Doehner, AJ Eder, CF Silva - Combustion and Flame, 2024 - Elsevier
In this work, we present a parsimonious set of ordinary differential equations (ODEs),
describing with good precision and over a wide range of frequencies the linear and …

[HTML][HTML] RF-PINNs: Reactive Flow Physics-Informed Neural Networks for Field Reconstruction of Laminar and Turbulent Flames using Sparse Data

V Yadav, M Casel, A Ghani - Journal of Computational Physics, 2024 - Elsevier
Abstract Physics-Informed Neural Networks (PINNs) have emerged as a promising tool to
model flow fields by embedding physical laws into neural networks and thereby reducing the …

Segmentation of high-speed flow fields using physics-informed clustering

M Ullman, S Barwey, GS Lee, V Raman - Applications in Energy and …, 2023 - Elsevier
The advent of data-based modeling has provided new methods and algorithms for analyzing
the complex flow fields in high-speed combustion applications. These techniques can be …

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Ż Grądz, W Wójcik, K Gromaszek, A Kotyra, S Smailova… - Energies, 2023 - mdpi.com
Coal remains one of the key raw materials used in the energy industry to generate electricity
and heat. As a result, diagnostics of the combustion process is still an important topic of …

[HTML][HTML] Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion

Ż Grądz, W Wójcik, B Imanbek, B Yeraliyeva - Energies, 2025 - mdpi.com
In the European Union, coal consumption in the power industry has been declining over
time. Energy sources such as renewable energy, nuclear energy, and natural gas are being …

Evaluation of Data-Driven Classifiers for an Ignition Forecast of Large Gas Turbines

F Lang, M Savtschenko… - … of Engineering for …, 2025 - asmedigitalcollection.asme.org
Prediction of successful ignition is a challenging task that requires knowledge of the local
thermochemical state in highly turbulent flow conditions. For typical industrial gas turbine …

A Dual-Path neural network model to construct the flame nonlinear thermoacoustic response in the time domain

J Wu, T Wang, J Nan, L Yang, J Li - arXiv preprint arXiv:2409.05885, 2024 - arxiv.org
Traditional numerical simulation methods require substantial computational resources to
accurately determine the complete nonlinear thermoacoustic response of flames to various …

Incomplete to complete multiphysics forecasting: a hybrid approach for learning unknown phenomena

NN Tathawadekar, NAK Doan, CF Silva… - Data-Centric …, 2023 - cambridge.org
Modeling complex dynamical systems with only partial knowledge of their physical
mechanisms is a crucial problem across all scientific and engineering disciplines. Purely …