融合CFD信息的风场层析成像

This proposal aims at establishing a new measurement system by utilizing wireless sensor networks incorporating CFD data. The project will carry out the following investigations: (i) the optimized arrangement of the sensor network, (ii) construction of the measurement matrix, (iii) Tomographic algorithms for wind field reconstruction, based on real time measurement by sensor networks by fusion of CFD data as prior information. The goals of the project are (i) as a new scientific approach, establishing a methodology for wind field reconstruction via CT technology, with data fusion of prior information based on physical principles and real time measurement of distributed data; (ii) facilitating an optimization functional that can process data of different forms based on different physical principles. (iii) providing wind farms with preview data of wind fields in real time. The novelty of the proposal lies on (i) Converting tomography methods into a wind field reconstruction methods to realize a wind field “preview” based on real time data of the sensors in a wind farm, instead of the conventional “forecast”. (ii) to incorporate CFD data in the optimization functional in a combination of different norms. (iii) including time dimension into the measurement matrix, iv) breakthrough in the arrangement of sensors from the traditional enclosure layout to the arbitrary arrangement, and accordingly the methodology of the structure reconstruction measurement matrix, which will enable the expansion or subdivision of the measurement zone. (v) flexibility in the selection and combination of the sensors and corresponding measurement matrix, which will well suit the need of Fault Tolerance in hush environments.

为应对风场风速波动性给风力发电带来的问题以及对风速预报的需求，本课题提出一种新型层析成像策略，依据传感器网络测量，结合计算流体力学先验信息，构造风场速度场重建体系。研究i）传感器系统的优化布置；ii）测量矩阵的构建；iii）基于传感器网络实时数据与CFD先验信息的风场重建算法。目标是i）建立融合CFD先验信息与实时检测分布信息的层析成像风速场重建方法；ii）实现多机理异类信息相融合的目标泛函，iii）为风电厂实时提供未来速度分布。创新点是：i）利用传感器网络实现风场图像重建，实现风场中基于实时测量数据的“风速预览”，不是传统的预测；ii）以组合式成像目标泛函纳入CFD先验信息；iii）测量矩阵中纳入时间尺度，iv) 突破封闭式层析成像传感器排列传统, 建立测量矩阵的组合与分解机制，以及测量区域大型化与局部精细化的机制；v）以传感器及测量矩阵的灵活组合，补正不完全测量信息，适合系统容错需求。

为应对风场风速波动性给风力发电带来的问题以及对风速预报的需求，本课题提出一种新.型层析成像策略，依据传感器网络测量，结合计算流体力学先验信息，构造风场速度场重建体系。研究i）传感器系统的优化布置；ii）测量矩阵的构建；iii）基于传感器网络实时数据与CFD先验信息的风场重建算法。.研究的结果为：i）建立融合CFD先验信息与实时检测分布信息的层析成像风速场重建方法；ii）实现多机理异类信息相融合的目标泛函，iii）为风电厂实时提供未来速度分布。.创新点是：i）利用传感器网络实现风场图像重建，实现风场中基于实时测量数据的“风速预览”，不是传统的预测；ii）以组合式成像目标泛函纳入CFD先验信息；iii）建立了传感器优化布置的方法，iv) 突破封闭式层析成像传感器排列传统, 建立测量矩阵的组合与分解机制，以及测量区域大型化与局部精细化的机制。

内容获取失败，请点击重试