title: Neural network approach in modelling vibrational kinetics of carbon dioxide
metadata.name: dc.contributor.author
metadata.value: Gorikhovskii, Viacheslav I.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kustova, Elena V.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2022-12-27T16:08:21Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2022-12-27T16:08:21Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Gorikhovskii V. I., Kustova E.V. Neural network approach in modelling vibrational kinetics of carbon dioxide. Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy, 2022, vol. 9 (67), issue 4, pp. 665–678. https://doi.org/10.21638/spbu01.2022.409 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu01.2022.409
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/38724
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The study is devoted to modeling nonequilibrium vibrational kinetics of carbon dioxide
  taking into account complex mechanisms of relaxation and intermode energy exchanges.
  The possibilities of using machine learning methods to improve the performance of numerical
  simulation of non-equilibrium carbon dioxide flows are studied. Various strategies for
  increasing the efficiency of the hybrid four-temperature model of CO2 kinetics are considered.
  The neural network approach proposed by the authors to calculate the rate of
  vibrational relaxation in each mode turned out to be the most promising. For the problem
  of spatially homogeneous relaxation, estimates of the error and computational costs of the
  developed algorithm are carried out, and its high accuracy and efficiency are demonstrated.
  For the first time, the carbon dioxide flow behind a plane shock wave was simulated in a full state-to-state approximation. A comparison with the results obtained in the framework of
  the hybrid four-temperature approach is carried out, and the equivalence of the approaches
  is shown. This makes it possible to recommend developed multitemperature approximations
  as the main tool for solving problems of nonequilibrium kinetics and gas dynamics.
  The hybrid four-temperature approach using the neural network method for calculating
  relaxation terms showed the acceleration of numerical simulation in time by more than
  an order of magnitude, while maintaining accuracy. This technique can be recommended
  for solving complex multidimensional problems of nonequilibrium gas dynamics, including
  state-to-state chemical reactions.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The work is supported by St Petersburg State University (project ID: 93022273). The authors
  thank A. A.Kosareva for providing the code for calculating the kinetics of CO2 in a three-temperature
  approximation with which the described neural network approach was tested for the first time.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Mathematics. Mechanics. Astronomy;Volume 9 (67); Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: vibrational relaxation rate
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: state-to-state and multi-temperature kinetics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: artificial neural network
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: carbon dioxide
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Neural network approach in modelling vibrational kinetics of carbon dioxide
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 665-678.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/38724/1/665-678.pdf
resources.format: Adobe PDF
resources.size: 587,46 kB