2022
DOI: 10.1002/er.7879
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Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

Abstract: Summary Despite rapid improvements in the performance of the central processing unit (CPU), the calculation cost of simulating chemically reacting flow using CFD remains infeasible in many cases. The application of the convolutional neural networks (CNNs) specialized in image processing in flow field prediction has been studied, but the need to develop a neural network design fitted for CFD has recently emerged. In this study, a neural network model introducing the finite volume method (FVM) with unique networ… Show more

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Cited by 9 publications
(3 citation statements)
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References 44 publications
(75 reference statements)
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“…Although past research has demonstrated the potential of DL in accelerating predictions of flow fields, most of them are short-term forecasts within two hundred steps, which is far from industrial applications. For instance, the FVMN model proposed by Jeon et al can predict ten future oil fields based on two. The model developed by Bazai can predict 170 steps but requires 18,000 frames for training.…”
Section: Resultsmentioning
confidence: 99%
“…Although past research has demonstrated the potential of DL in accelerating predictions of flow fields, most of them are short-term forecasts within two hundred steps, which is far from industrial applications. For instance, the FVMN model proposed by Jeon et al can predict ten future oil fields based on two. The model developed by Bazai can predict 170 steps but requires 18,000 frames for training.…”
Section: Resultsmentioning
confidence: 99%
“…Ayodeji and Amidu [ 52 ] developed a surrogate model based on a deep feedforward neural network to predict the turbulent eddy viscosity in RANS simulations; the results closely matched those of an actual turbulent model. Jeon and Lee [ 53 ] constructed a neuron-network-based model to simulate the principles of the finite volume method (FVM) in fluid dynamics. The performance was evaluated using unsteady reacting flow datasets and showed good agreement with reference data at one-tenth of the computational cost.…”
Section: Application Of Ai To Nuclear Reactor Design Optimizationmentioning
confidence: 99%
“…2019; Kochkov et al. 2021; Jeon, Lee & Kim 2022), develop improved turbulence closures (e.g. Ling, Kurzawski & Templeton 2016; Wang, Wu & Xiao 2017; Beck, Flad & Munz 2019; Duraisamy, Iaccarino & Xiao 2019), reconstruct turbulent flow fields from spatially limited data (e.g.…”
Section: Introductionmentioning
confidence: 99%