[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 …
Flame image processing and classification using a pre-trained VGG16 model in combustion diagnosis
Z Omiotek, A Kotyra - Sensors, 2021 - mdpi.com
Nowadays, despite a negative impact on the natural environment, coal combustion is still a
significant energy source. One way to minimize the adverse side effects is sophisticated …
significant energy source. One way to minimize the adverse side effects is sophisticated …
A generative adversarial network (GAN) approach to creating synthetic flame images from experimental data
Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of
reacting flows; however, they tend to generate massive data-sets, rendering conventional …
reacting flows; however, they tend to generate massive data-sets, rendering conventional …
[HTML][HTML] 3d convolutional selective autoencoder for instability detection in combustion systems
T Gangopadhyay, V Ramanan, A Akintayo, PK Boor… - Energy and AI, 2021 - Elsevier
While analytical solutions of critical (phase) transitions in dynamical systems are abundant
for simple nonlinear systems, such analysis remains intractable for real-life dynamical …
for simple nonlinear systems, such analysis remains intractable for real-life dynamical …
Learning thermoacoustic interactions in combustors using a physics-informed neural network
Many gas turbine and rocket engines exhibit unwanted combustion instability at the
experimental testing phase. Instability leads to large amplitude pressure oscillations and …
experimental testing phase. Instability leads to large amplitude pressure oscillations and …
Deep learning algorithms for detecting combustion instabilities
T Gangopadhyay, A Locurto, JB Michael… - Dynamics and Control of …, 2020 - Springer
Combustion instabilities are prevalent in a variety of systems including gas turbine engines.
In this regard, the introduction of active control opens the potential for new paradigms in …
In this regard, the introduction of active control opens the potential for new paradigms in …
Combustion process monitoring based on flame intensity time series
The pulverised coal and its mixture with biomass are one of the most popular fuels in
industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the …
industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the …
Interpretable deep learning for monitoring combustion instability
Transitions from stable to unstable states occurring in dynamical systems can be sudden
leading to catastrophic failure and huge revenue loss. For detecting these transitions during …
leading to catastrophic failure and huge revenue loss. For detecting these transitions during …
[PDF][PDF] An explainable framework using deep attention models for sequential data in combustion systems
T Gangopadhyay, SY Tan… - … Learning and the …, 2019 - ml4physicalsciences.github.io
The explanations provided by a classification framework on sequential data can provide
insights to improve scientific understanding in different problems of physical sciences. We …
insights to improve scientific understanding in different problems of physical sciences. We …
Data-Driven Strategy for Enhanced Subgrid Modeling of Reaction-Rate Using Linear-Eddy Model for Large Eddy Simulation
Large eddy simulation (LES) of turbulent combustion requires closure of various subgrid-
scale (SGS) terms such as mixing, transport, and chemical reactions. In the present study …
scale (SGS) terms such as mixing, transport, and chemical reactions. In the present study …