Volume 2B: Turbomachinery 2017
DOI: 10.1115/gt2017-63497
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Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation

Abstract: The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By in… Show more

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Cited by 37 publications
(37 citation statements)
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“…The EARSMs developed using GEP also have the added advantage that they do not require any high-fidelity data or supporting machine-learning based framework for their implementation into and use in CFD codes. GEP was used to conduct a priori analysis for a high pressure turbine vane, which showed significant improvement as compared to the baseline cases [18]. However, no a posteriori analysis (RANS calculation) was conducted in that work.…”
Section: Introductionmentioning
confidence: 99%
“…The EARSMs developed using GEP also have the added advantage that they do not require any high-fidelity data or supporting machine-learning based framework for their implementation into and use in CFD codes. GEP was used to conduct a priori analysis for a high pressure turbine vane, which showed significant improvement as compared to the baseline cases [18]. However, no a posteriori analysis (RANS calculation) was conducted in that work.…”
Section: Introductionmentioning
confidence: 99%
“…The result is a dissipation rate consistent with the LES fields and the RANS transport equations. For more information on this process see earlier work on turbine blade flows [9].…”
Section: Methodology For Reynolds Stress Closurementioning
confidence: 99%
“…Recently, a comparison of neural networks and GEP to regress non-linear stress-strain relationships showed very similar predictive performance, yet the GEP optimization was at a fraction of the cost [8]. Further, GEP was applied to high-pressure turbines and showed improvement over the linear model [9]. These studies looked primarily at the stress-strain relationship, yet researchers are also exploring other model components.…”
Section: Introductionmentioning
confidence: 99%
“…Using the eddy viscosity calculated via a least square optimized approach [29], shear stress profiles, calculated based on LES data, from the three wake regions and different phases have been shown in Fig. 12 for the 1B2U case.…”
Section: Comparison Of Modelsmentioning
confidence: 99%
“…The algorithm not only improved the Reynolds stress prediction, but also improved the mean flow features for a few canonical cases [28]. GEP was applied for the first time on a turbomachinery component when an a priori study was conducted for high pressure turbines [29]. The EARSMs developed brought about a 32% reduction in error over the baseline case in an a priori sense.…”
Section: Introductionmentioning
confidence: 99%