Toward a GPU-Accelerated Immersed Boundary Method for Wind Forecasting Over Complex Terrain

Abstract

A short-term wind power forecasting capability can be a valuable tool in the renewable energy industry to address load-balancing issues that arise from intermittent wind fields. Although numerical weather prediction models have been used to forecast winds, their applicability to micro-scale atmospheric boundary layer flows and ability to predict wind speeds at turbine hub height with a desired accuracy is not clear. To address this issue, we develop a multi-GPU parallel flow solver to forecast winds over complex terrain at the micro-scale, where computational domain size can range from meters to several kilometers. In the solver, we adopt the immersed boundary method and the Lagrangian dynamic large-eddy simulation model and extend them to atmospheric flows. The computations are accelerated on GPU clusters with a dual-level parallel implementation that interleaves MPI with CUDA. We evaluate the flow solver components against test problems and obtain preliminary results of flow over Bolund Hill, a coastal hill in Denmark.