Uncertainty quantification analysis for simulation of wakes in wind-farms using a stochastic RANS solver, compared with a deep learning approach. (15th May 2023)

• Record Type: Journal Article
• Authors: Parekh, Jigar; Verstappen, R.W.C.P.
• Is Part Of: Computers & fluids. Volume 257(2023)
• Publication Date: 2023-05-15
• Subjects: RANS turbulence modeling -- Wind-farm wakes -- Uncertainty quantification -- Random Reynolds stress tensor field -- Generalized polynomial chaos -- Deep learning -- Computational fluid dynamics -- Turbulence; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2023.105867
• Languages: English
• ISSNs: 0045-7930

Abstract

Abstract: Quantification of uncertainties in Reynolds–Averaged Navier–Stokes (RANS) simulations has gained a considerable interest in turbulence modeling. We present two different approaches for the quantification and propagation of model-form and operational uncertainties in context of wind turbine RANS simulations. The first approach is based on a stochastic RANS solver in OpenFOAM using intrusive polynomial chaos method (Parekh and Verstappen, 2023). Here the uncertainties are propagated through a single (large) simulation for the coupled coefficients of the polynomial expansion. The second approach is a surrogate based uncertainty quantification (SBUQ) method. The surrogate model comprises of a 3D U-Net neural network (trained over a single wind turbine) combined with a wake superposition model in order to the prediction of flow field in an array of wind turbines. The above-mentioned approaches are applied for uncertainty quantification analysis in RANS simulations of two turbulent engineering flow problems — (i) a wake past a single wind turbine, and (ii) wake interactions and power losses in an array of wind turbines. The results show that the uncertain RANS solutions from the two approaches are able to reasonably capture the reference high-fidelity solution. We also discuss comparisons between the two approaches including computational cost, applicability, generality etc. The two methods can be further explored and applied to engineering applications where it is critical to compute the turbulent RANS solution in presence of various sources of uncertainties. Highlights: Wind turbine RANS simulations are susceptible to multiple sources of uncertainties. Stochastic RANS is resource efficient and applicable to diverse CFD applications. 3D U-Net surrogate is capable of accurate wind turbine/farm flow field predictions. Surrogate based UQ analysis is reasonably accurate and computationally inexpensive.