Classifying Regions of High Model Error Within a Data-Driven RANS Closure

Classifying Regions of High Model Error Within a Data-Driven RANS Closure: Application to Wind Turbine Wakes

Steiner, J. (TU Delft Wind Energy)
Viré, A.C. (TU Delft Wind Energy)
Dwight, R.P. (TU Delft Aerodynamics)

2022

Data-driven Reynolds-averaged Navier–Stokes (RANS) turbulence closures are increasing seen as a viable alternative to general-purpose RANS closures, when LES reference data is available—also in wind-energy. Parsimonious closures with few, simple terms have advantages in terms of stability, interpret-ability, and execution speed. However experience suggests that closure model corrections need be made only in limited regions—e.g. in the near-wake of wind turbines and not in the majority of the flow. A parsimonious model therefore must find a middle ground between precise corrections in the wake, and zero corrections elsewhere. We attempt to resolve this impasse by introducing a classifier to identify regions needing correction, and only fit and apply our model correction there. We observe that such classifier-based models are significantly simpler (with fewer terms) than models without a classifier, and have similar accuracy, but are more prone to instability. We apply our framework to three flows consisting of multiple wind-turbines in neutral conditions with interacting wakes.

http://resolver.tudelft.nl/uuid:abe6130b-c239-4b6a-8d19-d8255a9dfb7b

https://doi.org/10.1007/s10494-022-00346-6

1386-6184

Flow, Turbulence and Combustion, 109 (3), 545-570

Institutional Repository

journal article

© 2022 J. Steiner, A.C. Viré, R.P. Dwight