CAREER: Multi-scale modeling of short-term forecasting and grid integration of wind energy over complex terrain

PI：Inanc Senocak提案编号：1056110机构：博伊西州立大学职务： 职业：复杂地形下风能短期预测和并网的多尺度建模人们对提高风能资源的利用率越来越感兴趣。 但是，增加风能在整体能源生产中的比例比简单地在平坦的多风地区安装风力发电场要复杂得多。 这项研究的总体目标是使用多尺度建模方法更好地了解不同大气稳定性条件下复杂地形峡谷中湍流的特征，以便这些风能资源可以可靠地用于发电。智能MeritA多尺度模拟方法，连接大气过程中尺度下到微观尺度，复杂的地形特征可以解决的建议。 具体而言，提出了一种计算快速风模拟能力，以使用图形处理单元（GPU）集群上的加速计算流体动力学（CFD）模拟来预测短期（例如，0-6小时）涡轮机级的风。大涡模拟（LES）技术与动力学过程将用于高分辨率地研究复杂地形下的大气流动。 提出了一种模拟引擎来支持新兴GPU集群上的多尺度耦合计算。 在这个引擎中，一个中尺度大气模型（如天气研究和预报模型）将在中央处理单元（CPU）上执行。 微尺度地形解析CFD模型将与中尺度模型同时在GPU上执行，以提供涡轮机级的高分辨率模拟。微尺度模拟还将用于改进中尺度模型中的表层参数化，以建立模拟模型之间的单向耦合。 中尺度和微尺度模型将同时执行，以预测复杂地形上的风力，用于风能生产。 模拟结果将用于进行湍流动能收支分析，这将有助于识别和理解不同稳定性条件下复杂地形环境中湍流产生的主要来源。 将检查风速的标准差并与现有的相关性进行比较。 模拟将根据从爱达荷州一个复杂地形地区的气象站获得的测量结果进行验证。 实验和计算数据将进行研究，以找到适当的缩放参数来表征峡谷内和沿着山脊的流动结构。 更广泛的影响在短期风电预测和电网整合方面存在巨大的技术差距。 提高各种地形的短期预报精度对风能产业具有重要意义。 预计在项目结束时，将更好地了解用于在这些风环境中进行短期预报的复杂地形流。拟议的教育和推广活动集中在超级计算和科学可视化应用于风能作为一种手段，以激发学生对计算科学的兴趣。 配备平铺显示可视化集群的计算建模实验室将用于研究和教育。 一个GPU加速的教育CFD模型与教程集，以支持热和流体科学教育将建立在这个实验室用于流体力学课程。加速计算平台将使本科生和研究生能够快速完成计算密集型模拟，以研究基础物理。 GPU加速的教育CFD模型的免费版本也将免费提供。 将开发一个超级计算展台，供博伊西州立大学正在进行的K-12外展项目使用（例如，电子女孩、电子夏令营、电子日）。 模拟室将包括模拟和模拟复杂地形中的风的实践练习，以及地球和空间科学科学数据和高分辨率图像的可视化。

PI: Inanc SenocakProposal Number: 1056110Institution: Boise State UniversityTitle: CAREER: Multi-scale modeling of short-term forecasting and grid integration of wind energy over complex terrainThere is a growing interest to increase the utilization of wind energy resources for electricity production. But increasing the percentage of wind energy in overall energy production is much more complex than simply installing wind farms in windy areas with flat terrains. The overall goal of this research is to better understand the characteristics of turbulent flows in complex terrain with canyons under different atmospheric stability conditions using a multi-scale modeling approach, so that these wind energy resources can be reliably harnessed for the production of electricity. Intellectual MeritA multi-scale modeling approach that connects atmospheric processes at the meso-scale down to the micro scale where complex terrain features can be resolved is proposed. Specifically, a computationally fast wind simulation capability is proposed to forecast winds at the turbine level for the short-term (e.g. 0-6 hours), using accelerated computational fluid dynamics (CFD) simulations on graphics processing units (GPU) clusters. The large eddy simulation (LES) technique with the dynamic procedure will be applied to study atmospheric flows in complex terrain with high resolution. A simulation engine is proposed to support multi-scale coupled computations on emerging GPU clusters. In this engine, a meso-scale atmospheric model (e.g. the Weather Research and Forecasting model) will be executed on central-processing units (CPU). A microscale terrain-resolving CFD model will be executed on the GPU concurrently with the meso-scale model to provide high resolution simulations at the turbine level. The micro-scale simulations will also be used to improve the surface layer parameterizations in the meso-scale model to establish a one-and -a-half-way coupling between the simulation models. The meso and micro scale models will be executed concurrently to forecast winds over complex terrain for wind energy production. Simulation results will be used to perform turbulent kinetic energy budget analysis, which will help identify and understand the primary source of turbulence production in complex terrain environment under different stability conditions. Standard deviations of the wind velocity will be examined and compared against existing correlations. Simulations will be validated against measurements obtained from a test area in the state of Idaho in a complex terrain area instrumented with weather stations. Both experimental and computational data will be studied to find appropriate scaling parameters to characterize the flow structure within canyons and along ridges. Broader ImpactsSubstantial technology gaps exist in short-term wind power forecasting and grid integration. Improving the accuracy of short-term forecasts for all types of terrain is of great importance to wind energy industry. A better understanding of complex terrain flows used to develop short-term forecasts in these wind environments is expected at the end of the project. The proposed education and outreach activities are centered on supercomputing and scientific visualization applied to wind energy as a means to motivate student interest in the computational sciences. A computational modeling laboratory equipped with a tiled-display visualization cluster will be used for research and education. A GPU-accelerated educational CFD model with a tutorial set to support thermal and fluid sciences education will be set up in this laboratory for use in fluid mechanics courses. The accelerated computational platform will enable both undergraduate and graduate students to finish compute-intensive simulations quickly to investigate the underlying physics. A free version of GPU-accelerated educational CFD model will also be made publically available. A supercomputing booth will be developed for use at on-going K-12 outreach programs at Boise State University (e.g. e-Girls, e-Camp, e-Day). The simulation booth will include hands-on exercises for modeling and simulation of winds in complex terrain and visualization of scientific data and high resolution imagery from earth and space sciences.