A preconditioned multigrid for simulation of atmospheric flow and wind turbine wakes. (20th November 2015)

• Record Type: Journal Article
• Authors: Jafari, Samira; Basol, Altug M.; Chokani, Ndaona; Abhari, Reza S.
• Is Part Of: Computers & fluids. Volume 122(2015)
• Page Start: 111
• Page End: 122
• Publication Date: 2015-11-20
• Subjects: Preconditioning -- High Reynolds number Flow -- Wind flow simulations -- Compressible solvers; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2015.08.022
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: The paper details the implementation of the preconditioning. Several modifications suited for high Reynolds-number flow are suggested. The use of simplified inlet/outlet boundary condition is assessed. The formulation of a novel immersed wind turbine model is presented. Results are compared with available full-scale wind measurements. Absract: This paper demonstrates a low Mach number preconditioning formulation for high Reynolds number, low Mach number atmospheric flows. The preconditioning is implemented together with multigrid approach into a multistage solver in order to provide an efficient and robust all purpose solver to be used for the simulation of high Reynolds number viscous flows. Several modification and scaling of the preconditioned parameter are introduced to ensure robust use of the scheme for this particular flow regime in presence of stagnation points and seperation regions. The simulations results of standard cases of atmospheric flow over hilly terrain with attached and separated flow are presented to demonstrate the superior convergence, accuracy and robustness of the developed preconditioned scheme. An immersed wind turbine model is also developed and embedded into the preconditioned solver which allows for the simulations of far wake behind wind turbines in wind farms. The use of the immersed model reduces the stringent grid requirements of full RANS simulations around wind turbines and allows one to efficiently predict the wake evolution within wind farms.