Using LSTM Predictions for RANS Simulations
Date
2024-11-19
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Repositorio Arxiv
Abstract
This study constitutes the second phase of a research endeavor aimed at evaluating the feasibility
of employing Long Short-Term Memory (LSTM) neural networks as a replacement for Reynolds-
Averaged Navier-Stokes (RANS) turbulence models.
In the initial phase of this investigation (titled Modeling Turbulent Flows with LSTM Neural
Networks, arXiv:2307.13784v1 [physics.flu-dyn] 25 Jul 2023), the application of an LSTM-based
recurrent neural network (RNN) as an alternative to traditional RANS models was demonstrated.
LSTM models were used to predict shear Reynolds stresses in both developed and developing tur-
bulent channel flows, and these predictions were propagated through RANS simulations to obtain
mean flow fields of turbulent flows. A comparative analysis was conducted, juxtaposing the LSTM
results from computational fluid dynamics (CFD) simulations with outcomes from the κ − ϵ model
and data from direct numerical simulations (DNS). These initial findings indicated promising per-
formance of the LSTM approach.
This second phase delves further into the challenges encountered and presents robust solutions.
Additionally, new results are provided, demonstrating the efficacy of the LSTM model in predicting
turbulent behavior in perturbed flows. While the overall study serves as a proof-of-concept for
the application of LSTM networks in RANS turbulence modeling, this phase offers compelling
evidence of its potential in handling more complex flow scenarios.
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Keywords
Rans, Simulations
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