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Evaluation of state-specific transport properties using machine learning methods

Vladimir Istomin, Elena Kustova, Semen Lagutin, Ivan Shalamov
In this study, machine learning algorithms are employed
to calculate state-to-state transport coefficients
in nonequilibrium reacting gas flows. The focus is on
the evaluation of thermal conductivity, shear viscosity,
and bulk viscosity coefficients under conditions of strong
coupling between vibrational-chemical kinetics and gas
dynamics. In order to solve a regression problem for
evaluating state-to-state transport coefficients, a specific
software application with user interface is developed,
which allows loading, processing, and saving of data arrays;
configuring model architecture; training and evaluating
models with various optimizers, loss functions,
and metrics; making predictions using trained models.
Using the developed software the multi-layer perceptron
regression model is constructed and trained. The
model is assessed in a binary mixture of molecular and
atomic nitrogen taking into account 48 vibrational states;
the coefficients are computed in the wide temperature
range for the varying mixture composition. Good agreement
of the results with the original transport coefficients
calculated using rigorous but computationally expensive
kinetic theory algorithms is shown. Applying
machine learning techniques yields a significant speedup
of about two orders of magnitude in the computation
of transport coefficients. It is concluded that implementation
of machine learning methods may considerably
reduce the computational efforts required for nonequilibrium
flow simulations.
CYBERNETICS AND PHYSICS, VOL. 12, NO. 1, 2023, 34–41 https://doi.org/10.35470/2226-4116-2023-12-1-34-41
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