MULTILEVEL UNCERTAINTY QUANTIFICATION OF A WIND TURBINE LARGE EDDY SIMULATION MODEL
MULTILEVEL UNCERTAINTY QUANTIFICATION OF A WIND TURBINE LARGE EDDY SIMULATION MODEL
复制标题
风电机组大涡模拟模型的多级不确定性量化
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Eldred
中科院分区：
文献类型：
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作者：
D. Maniaci;A. Frankel;G. Geraci;M. Blaylock;M. Eldred
风能本质上是随机的;与风能应用相关的空气动力学量和载荷的预测涉及对许多不同情况下许多尺度上的一系列物理相互作用进行建模。这些预测需要一定范围的模型保真度，因为包括大气和风力涡轮机尾流物理相互作用的预测模型可能需要数周时间才能在机构高性能计算系统上求解。为了量化多模型预测风能数量的不确定性，桑迪亚国家实验室的研究人员采用了多级多保真度方法。使用NREL 5 MW转子在具有尾流相互作用的大气边界层中的模拟完成了演示研究。用两个不同保真度的模型模拟了气流;一个致动器线风力发电厂大涡尺度模型，Nalu，使用几种网格分辨率与较低保真度模型OpenFAST相结合。在流动条件和致动器的力量通过使用蒙特卡罗抽样模型传播，以估计在尾流和转子上的力的速度缺陷。粗网格模拟与较低保真度的流动模型一起沿着被利用来减少估计器的方差，并且由此产生的多级多保真度策略与标准蒙特卡罗方法相比在估计器效率方面表现出实质性的改进。D. C. Maniaci，A. L. Frankel，G. Geraci，M. L. Blaylock和M. S.埃尔德雷德
Wind energy is stochastic in nature; the prediction of aerodynamic quantities and loads relevant to wind energy applications involves modeling the interaction of a range of physics over many scales for many different cases. These predictions require a range of model fidelity, as predictive models that include the interaction of atmospheric and wind turbine wake physics can take weeks to solve on institutional high performance computing systems. In order to quantify the uncertainty in predictions of wind energy quantities with multiple models, researchers at Sandia National Laboratories have applied Multilevel-Multifidelity methods. A demonstration study was completed using simulations of a NREL 5MW rotor in an atmospheric boundary layer with wake interaction. The flow was simulated with two models of disparate fidelity; an actuator line wind plant large-eddy scale model, Nalu, using several mesh resolutions in combination with a lower fidelity model, OpenFAST. Uncertainties in the flow conditions and actuator forces were propagated through the model using Monte Carlo sampling to estimate the velocity defect in the wake and forces on the rotor. Coarse-mesh simulations were leveraged along with the lower-fidelity flow model to reduce the variance of the estimator, and the resulting Multilevel-Multifidelity strategy demonstrated a substantial improvement in estimator efficiency compared to the standard Monte Carlo method. D. C. Maniaci, A. L. Frankel, G. Geraci, M. L. Blaylock, and M. S. Eldred