Wind Turbine Array Performance Based on Coupling CFD with Doppler Lidar Measurements

项目编号：1335868机构：亚利桑那州立大学题目：基于CFD和多普勒激光雷达耦合测量的风力涡轮机阵列性能随着现代风力涡轮机的尺寸越来越大，大气边界层（ABL）变率对其运行的影响变得越来越重要。该项目将开发并验证一种新方法，将多普勒激光雷达获得的现场风速测量数据整合到风电场流量和功率表征的高保真计算模型中。多普勒激光雷达测量提供了由区域地形和当地天气模式调制的微尺度气象结构的最可靠信息。基于大涡模拟（LES）的高保真风电场流动模型可生成包括非定常涡轮负荷在内的高分辨率时空数据，用于评估结构响应和功率性能。在实际大气测量的驱动下，LES模拟将为研究风力发电机组阵列对随机大气环境的非定常响应提供宝贵的信息。了解这些非定常响应是建立可靠的风力机控制和风电场布局优化模型所必需的。将开发一种独特的多尺度方法，将实时观测捕获的亚中尺度大气运动的影响传播到由粘性效应和叶片/尾流相互作用控制的风力发电厂和风力涡轮机尺度。该方法将结合多普勒激光雷达数据的最优插值同化来检索矢量场信息，通过驱动辅助ABL LES模拟来匹配激光雷达观测来重建较小尺度，并将同化的时空随机入流与基于LES和致动器线空气动力学的最先进的风电场流动表征模型相结合。使用一种新颖的计算实验，pi将能够研究时空风变率对非定常涡轮负荷和尾流物理的影响，并通过计算分离不同的变率来源，如风湍流、风速切变、风方向切变，目的是表征这些影响的相对强度，并创建最可靠的简化入流模型，以捕获基本物理。简化模型的建立对于提高风电行业的设计参数和标准至关重要。数值模拟基于由阿贡国家实验室开发和支持的高阶谱元计算求解器Nek5000，由于其高阶精度和极高的可扩展性，目前可以最有效地利用高端计算资源。了解风力发电厂对现实风况的时空响应将有助于改进设计和减少不确定性，从而降低平准化能源成本（LCOE）。吸取的教训和发现的课题将在科学界广泛传播。该项目将成为为当地高中生提供实践研究和教育经验的中心主题。通过竞争性的多元化奖学金，pi将在项目第二年的夏季与四名高中学生（包括少数民族学生、女性学生和低收入家庭学生）合作。

PI: Peet, YuliaProposal Number: 1335868Institution: Arizona State UniversityTitle: Wind Turbine Array Performance Based on Coupling CFD with Doppler Lidar MeasurementsAs the size of the modern wind turbines grow, the effect of the atmospheric boundary layer (ABL) variability on their operation becomes significantly more important. This project will develop and validate a novel methodology to integrate field wind velocity measurements obtained by the Doppler lidar into a high-fidelity computational model for wind plant flow and power characterization. Doppler lidar measurements give the most reliable information of the microscale meteorological structures modulated by the regional terrain and local weather patterns. High-fidelity wind plant flow models based on Large Eddy Simulations (LES) generate highly-resolved spatio-temporal data including unsteady turbine loads for assessing the structural response and power performance. When driven by realistic atmospheric measurements, LES simulations will provide invaluable information to study the unsteady response of wind turbine arrays to the stochastic atmospheric environment. Knowledge of these unsteady responses is required to create reliable models for wind turbine control and wind farm layout optimization.A unique multiscale approach will be develop to propagate the effects of the sub-mesoscale atmospheric motion captured by the real-time observations down to the wind plant and wind turbine scales governed by the viscous effects and blade/wake interactions. This approach will combine the optimal interpolation assimilation of the Doppler lidar data to retrieve the vector field information, reconstructing the smaller scales by driving the auxiliary ABL LES simulations to match the lidar observations, and integrating the assimilated spatio-temporal stochastic inflow with the state-of-the-art wind plant flow characterization model based on LES and actuator line aerodynamics. Using a novel computational experiments the PIs will be able to study the effects of spatio-temporal wind variability on unsteady turbine loads and wake physics, and computationally isolate the different sources of variability such as wind turbulence, wind speed shear, wind directional shear, with the goal of characterizing the relative strengths of these effects and creating the most reliable simplified inflow models that capture the essential physics. The creation of simplified models is crucial for improving design parameters and standards for wind turbine industry. The numerical simulations are based on high-order spectral element computational solver Nek5000 developed and supported by Argonne National Laboratory that currently offers the most efficient utilization of high-end computing resources due to its high-order accuracy and extreme scalability.Understanding spatio-temporal responses of wind plants to realistic wind conditions will result in improved design and reduction of uncertainty, thus lowering Levelized Cost of Energy (LCOE). The lessons learned and the topics discovered will be broadly disseminated within the scientific community. This project will be a central theme for providing hands-on research and educational experience for the local high-school students. Through competitive diversity-oriented scholarships, PIs will work with four high-school students including minority, female and low-income students, over the summer following the second year of the project.