[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 …
Mechanisms and modeling of bubble dynamic behaviors and mass transfer under gravity: a review
S Yan, X Wang, L Zhu, X Zhang, Z Luo - Chemical Engineering Science, 2023 - Elsevier
Bubbly flow is a prototypical two-phase flow problem. The dynamic behavior of bubbles
within a reactor is intrinsically linked to their transport, distribution, and mass transfer, all of …
within a reactor is intrinsically linked to their transport, distribution, and mass transfer, all of …
Applications of machine learning to the analysis of engine in-cylinder flow and thermal process: A review and outlook
F Zhao, DLS Hung - Applied Thermal Engineering, 2023 - Elsevier
To adequately elucidate the complex in-cylinder flow structures and its underlying effects on
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
Multiscale graph neural network autoencoders for interpretable scientific machine learning
The goal of this work is to address two limitations in autoencoder-based models: latent
space interpretability and compatibility with unstructured meshes. This is accomplished here …
space interpretability and compatibility with unstructured meshes. This is accomplished here …
Knowledge distillation in wide neural networks: Risk bound, data efficiency and imperfect teacher
Abstract Knowledge distillation is a strategy of training a student network with guide of the
soft output from a teacher network. It has been a successful method of model compression …
soft output from a teacher network. It has been a successful method of model compression …
[HTML][HTML] Velocity reconstruction in puffing pool fires with physics-informed neural networks
Pool fires are canonical representations of many accidental fires which can exhibit an
unstable unsteady behavior, known as puffing, which involves a strong coupling between …
unstable unsteady behavior, known as puffing, which involves a strong coupling between …
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 …
Adversarial sampling of unknown and high-dimensional conditional distributions
Many engineering problems require the prediction of realization-to-realization variability or a
refined description of modeled quantities. In that case, it is necessary to sample elements …
refined description of modeled quantities. In that case, it is necessary to sample elements …
Data-driven reduction and decomposition with time-axis clustering
A new approach for modal decomposition through re-interpretation of unsteady dynamics,
termed time-axis clustering, is developed in this work and is demonstrated on an …
termed time-axis clustering, is developed in this work and is demonstrated on an …