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Maniaci, D. C.

Publications and source records attributed to Maniaci, D. C..

Offshore Wind Energy Validation Experiment Hierarchy

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

17 WIND ENERGY↗

Benchmarks for Model Validation based on LiDAR Wake Measurements

Technology development and design decisions in wind energy are often based on results from simulations performed for individual wind turbines or entire wind plants. It is therefore critical to ensure that the models being used for research and industry applications in wind energy be thoroughly validated against measurements. A full-system validation of wind plant simulations must consider the atmospheric inflow, the response of the wind turbines, and their wakes. This task is complicated by the lack of freely available, quality-controlled, high-quality measurements. Here, such measurements are used to offer a validation exercise that can be used to assess the accuracy of models of any fidelity level. When it comes to real-world measurements, the dataset considered herein is simple in terms of terrain but exhibits pronounced diurnal cycles. Instead of a full-scale wind plant, we consider an individual research-scale, utility wind turbine instrumented for power and loads measurements. Three benchmarks are defined, with increasing levels of complexity: near neutral, slightly unstable, and very stable atmospheric stratification. Through comparisons between observations and simulations, the benchmarks provide complementary information about the model performance and its ability to reproduce mean and dynamic wake characteristics. This article describes the measurements and methodology used to define these benchmarks and provides the information required to perform simulations and conduct the model-measurement comparison. The objective is to provide a robust wake model validation exercise open to anyone, which will serve to minimize uncertainty in model validation practices related to varying methodologies across simulation tools and users.

17 WIND ENERGY↗

Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques

Wind lidar technology is being extensively used in utility-scale wind turbine wake measurements because of its capabilities to capture the dominant structures of interest in the near-wake region. However, wind lidar devices provide only line-of-sight (LOS) measurements, and retrieval of horizontal wind speed from the LOS measurements of a single lidar in the wind turbine wake could introduce errors due to the assumptions used in the retrieval process. With that in mind, the goals of this paper are to estimate the errors associated with the retrieval processes and provide guidelines for best practices. To achieve these goals, virtual lidar samples are obtained with large-eddy simulations (LES) by sampling the flow field with a model of the Technical University of Denmark (DTU) SpinnerLidar. LES is chosen to simulate the neutral and unstable atmospheric boundary layer (ABL), a benchmark case of the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) Task 31. The precursor ABL simulation is validated with the reference data provided by the IEA Wind Task 31 benchmark case. The data from the precursor simulation is used to drive a simulation of a wind turbine submerged in the ABL. A weighting function is applied to the LES data to mimic the real DTU SpinnerLidar measurements. In the end, the virtual lidar data are processed and compared with the original LES data to estimate the errors. Results show that lidar captures mean ABL profile without any noticeable error, whereas turbulence intensity is underpredicted ~15% at hub height for the neutral case. Volume averaging by the lidar technology has a significant effect in the shear layer of a wind turbine wake.

17 WIND ENERGY↗