Abstract. The objective of this paper is to compare field data from a scanning lidar mounted on a turbine to control-oriented wind turbine wake models. The measurements were taken from the turbine nacelle looking downstream at the turbine wake. This field campaign was used to validate control-oriented tools used for wind plant control and optimization. The National Wind Technology Center in Golden, CO, conducted a demonstration of wake steering on a utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment set points, while a lidar mounted on the nacelle scanned five downstream distances. Primarily, this paper examines measurements taken at 2.35 diameters downstream of the turbine. The lidar measurements were combined with turbine data and measurements of the inflow made by a highly instrumented meteorological mast on-site. This paper presents a quantitative analysis of the lidar data compared to the control-oriented wake models used under different atmospheric conditions and turbine operation. These results show that good agreement is obtained between the lidar data and the models under these different conditions.

Abstract.Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream 5 turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. 10This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.

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Monte Carlo (MC) sampling is the standard approach for uncertainty propagation in problems with high-dimensional stochastic inputs. Various acceleration techniques have been developed to overcome the slow convergence of MC estimates, such as multilevel Monte Carlo (MLMC). MLMC uses successive approximations computed on levels, models with different levels of accuracy, and computational cost to reduce the estimator variance. MLMC analytically determines the number of samples required on each level to achieve a given accuracy at minimal cost. We propose an extension of the original MLMC theoretical framework for modern, heterogeneous computer architectures in which accelerators (GPUs) are available and, therefore, samples can be distributed on both different levels and different compute units (CPUs and GPUs). We derive the optimal sample allocation for the proposed MLMC extension by solving a convex optimization problem. We apply the MLMC extension to a stochastically heated channel flow to provide insight for a study on the design of concentrated solar energy receivers. We demonstrate for the stochastically heated channel flow that the proposed MLMC extension leads to considerable total cost reduction (up to 86%) compared to MLMC using only GPUs.