FAST.Farm development and validation of structural load prediction against large eddy simulations

涡轮机 唤醒 塔楼 风力发电 环境科学 海洋工程 弯矩 大涡模拟 上游(联网) 工程类 结构工程 气象学 湍流 航空航天工程 物理 电信 电气工程
作者
Kelsey Shaler,Jason Jonkman
出处
期刊:Wind Energy [Wiley]
卷期号:24 (5): 428-449 被引量:45
标识
DOI:10.1002/we.2581
摘要

Abstract FAST.Farm is a mid‐fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA‐OpenFAST results for three laterally‐aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA‐OpenFAST structural response for the non‐waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower‐top and tower‐base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.
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