Incorporating basic calibrations in existing machine-learned turbulence modeling
This work aims to incorporate basic calibrations of Reynolds-averaged Navier-Stokes (RANS) models as part of machine learning (ML) frameworks. The ML frameworks considered are tensor-basis neural network (TBNN), physics-informed machine learning (PIML), and field inversion & machine learning (FI...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
06.11.2023
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Subjects | |
Online Access | Get full text |
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Summary: | This work aims to incorporate basic calibrations of Reynolds-averaged
Navier-Stokes (RANS) models as part of machine learning (ML) frameworks. The ML
frameworks considered are tensor-basis neural network (TBNN), physics-informed
machine learning (PIML), and field inversion & machine learning (FIML) in J.
Fluid Mech., 2016, 807, 155-166, Phys. Rev. Fluids, 2017, 2(3), 034603 and J.
Comp. Phys., 2016, 305, 758-774, and the baseline RANS models are the
one-equation Spalart-Allmaras model, the two-equation $k$-$\omega$ model, and
the seven-equation Reynolds stress transport models. ML frameworks are trained
against plane channel flow and shear-layer flow data. We compare the ML
frameworks and study whether the machine-learned augmentations are detrimental
outside the training set. The findings are summarized as follows. The
augmentations due to TBNN are detrimental. PIML leads to augmentations that are
beneficial inside the training dataset but detrimental outside it. These
results are not affected by the baseline RANS model. FIML's augmentations to
the two eddy viscosity models, where an inner-layer treatment already exists,
are largely neutral. Its augmentation to the seven-equation model, where an
inner-layer treatment does not exist, improves the mean flow prediction in a
channel. Furthermore, these FIML augmentations are mostly non-detrimental
outside the training dataset. In addition to reporting these results, the paper
offers physical explanations of the results. Last, we note that the conclusions
drawn here are confined to the ML frameworks and the flows considered in this
study. More detailed comparative studies and validation & verification studies
are needed to account for developments in recent years. |
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DOI: | 10.48550/arxiv.2311.03133 |