CDS&E:A large-scale data discovery framework for understanding intermittent, performance-critical phenomena in simulations of offshore wind turbines

CDS

• 批准号: 1306869
• 负责人: Sutanu Sarkar
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1306869&HistoricalAwards=false
• 关键词: CDS& large; scale; data; discovery

CBET 1306869S. Sarkar (PI), S. Baden (co-PI) and Y. Bazilevs (co-PI)UC San DiegoLarge scale simulation data sets that describe spatial and temporal variation of physical phenomena over a multitude of scales often contain characteristic flow features that are infrequent but critical to understanding the natural phenomenon or engineered system in question. The research will lead to a novel data discovery framework, to flexibly identify, extract and interrogate features of interest in space-time, leading to both a reduction in size of stored data sets and identification of time periods where high-fidelity simulation of the system is necessary. The data discovery framework will provide algorithmic and data structure optimizations to enable the user to flexibly query the data over space and time. It will be implemented on Gordon, a new data-intensive platform with non-volatile storage - flash memory. The proposed research will bring together investigators with expertise in computer science, fluid mechanics and structural mechanics to advance computational and data-enabled science and engineering. The motivating engineering application is offshore wind turbines. The wind turbine simulations proposed here are ambitious and will couple computation of flow-structure interaction at the temporal resolution of seconds with simulations of realistic atmospheric turbulence. Such simulations of a full-scale wind turbine are unprecedented and will be enabled by a computational framework that brings together the following components: advanced large eddy simulation for atmospheric flow, isogeometric analysis for fluid-structure interaction, and a finite element method with weak boundary conditions for the blade aerodynamics. Atmospheric conditions in a marine boundary layer, particularly under conditions of stable stratification, lead to intermittent bursts of wind shear and pressure that can have an unusually large effect on the rotor blade structural response. Quantification and understanding of such large-impact events using the data discovery framework will enable better and more cost-effective blade design, better operational performance, and more accurate wind resource forecasting. There will also be better understanding of extreme atmospheric events that lead to wind turbine blade failure. The data discovery framework to be developed here will have broad impact since it can be employed in any field involving multi-scale, time-dependent data containing dynamically important objects of lower-order complexity. Some examples are cardiovascular imaging and modeling, hurricane prediction, epidemiological modeling, and cloud dynamics.

CBET 1306869 S。Sarkar（PI），S. Baden（co-PI）和Y. Bazilevs（co-PI）UC San Diego描述物理现象在多个尺度上的空间和时间变化的大规模模拟数据集通常包含特征流特征，这些特征不常见，但对于理解所讨论的自然现象或工程系统至关重要。该研究将导致一种新的数据发现框架，以灵活地识别，提取和询问时空中感兴趣的特征，从而减少存储数据集的大小，并识别需要高保真仿真系统的时间段。数据发现框架将提供算法和数据结构优化，使用户能够在空间和时间上灵活查询数据。它将在Gordon上实施，这是一个新的数据密集型平台，具有非易失性存储-闪存。拟议的研究将汇集具有计算机科学，流体力学和结构力学专业知识的研究人员，以推进计算和数据支持的科学和工程。激励工程应用是海上风力涡轮机。这里提出的风力涡轮机模拟是雄心勃勃的，并将耦合计算流结构相互作用的时间分辨率为秒与模拟现实的大气湍流。全尺寸风力涡轮机的这种模拟是前所未有的，并且将通过汇集以下组件的计算框架来实现：大气流的高级大涡模拟，流体-结构相互作用的等几何分析，以及叶片空气动力学的弱边界条件的有限元法。海洋边界层中的大气条件，特别是在稳定分层条件下，导致风切变和压力的间歇性爆发，这可能对转子叶片结构响应产生异常大的影响。使用数据发现框架量化和理解此类大影响事件将有助于更好和更具成本效益的叶片设计，更好的运营性能和更准确的风力资源预测。还将更好地了解导致风力涡轮机叶片故障的极端大气事件。这里要开发的数据发现框架将具有广泛的影响，因为它可以在涉及多尺度，时间依赖性的数据包含动态重要的对象的低阶复杂性的任何领域。例如心血管成像和建模、飓风预测、流行病学建模和云动力学。