Benchmarks for Model Validation based on LiDAR Wake Measurements
Benchmarks for Model Validation based on LiDAR Wake Measurements
复制标题
基于 LiDAR 尾流测量的模型验证基准
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
B. Naughton
中科院分区：
文献类型：
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作者：
P. Doubrawa;M. Debnath;P. Moriarty;E. Branlard;T. Herges;D. Maniaci;B. Naughton
风能的技术开发和设计决策通常基于对单个风力涡轮机或整个风力发电厂进行的模拟结果。因此，确保用于风能研究和工业应用的模型经过测量的彻底验证是至关重要的。风力发电厂模拟的全系统验证必须考虑大气流入、风力涡轮机的响应和它们的尾迹。由于缺乏可免费获得的、有质量控制的、高质量的测量方法，这项任务变得更加复杂。在这里，这样的测量被用来提供一个验证练习，可以用来评估任何保真度水平的模型的准确性。当涉及到现实世界的测量时，这里考虑的数据集在地形方面很简单，但显示出明显的日循环。而不是一个完整的风力发电厂，我们考虑一个单独的研究规模，公用事业风力涡轮机仪器的电力和负荷测量。根据复杂程度的增加，定义了三个基准：接近中性、稍微不稳定和非常稳定的大气分层。通过对观测和模拟的比较，基准提供了关于模型性能及其再现平均和动态尾流特性的能力的补充信息。本文描述了用于定义这些基准的测量和方法，并提供了执行模拟和进行模型测量比较所需的信息。目标是提供一个对任何人开放的稳健的尾流模型验证练习，这将有助于减少模型验证实践中与模拟工具和用户之间不同方法相关的不确定性。
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.