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Kriging based sequence interpolation and probability distribution correction for gaussian wind field data reconstruction
Journal of Wind Engineering and Industrial Aerodynamics ( IF 4.2 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.jweia.2020.104340
Qiushuang Lin , Chunxiang Li

Abstract Data reconstruction is an important research topic for missing data recovery and data supplement. Spatial interpolation is often used for data reconstruction. The interpolation for time series is usually conducted at each time point by common methods, which is sometimes inefficient. Additionally, statistical characteristics recovering may not be taken into account together during interpolation, which affects the utilization of data in structural design and safety assessment. In this paper, supposing that the data of a potential observation point are completely missing (no historical observation information at all), Kriging based Sequence Interpolation (KSI) combined with probability distribution correction is proposed for data reconstruction. KSI is proposed for two purposes. First, the primary interpolation results can be obtained by KSI, namely global interpolation once. Second, the Probability Density Function (PDF) of missing points can be reconstructed by KSI and subsequently provide a reference to correct the primary interpolation results. The proposed method is verified by both the simulated and field monitoring data, and the results show that the calculation efficiency is greatly improved compared with common methods. The fusion of data reconstruction and PDF reconstruction is novel and the effectiveness is verified via the structural dynamic response analysis. The RMS of structural displacement responses induced by the reconstructed wind load is almost coincide with that by the actual wind load.

中文翻译:

用于高斯风场数据重建的基于克里金法的序列插值和概率分布校正

摘要 数据重建是缺失数据恢复和数据补充的重要研究课题。空间插值常用于数据重建。时间序列的插值通常在每个时间点通过常用方法进行,有时效率低下。此外,在插值过程中可能不会同时考虑统计特性恢复,这会影响数据在结构设计和安全评估中的利用。在本文中,假设潜在观测点的数据完全缺失(完全没有历史观测信息),结合概率分布校正的克里金法序列插值(KSI)方法进行数据重构。建议 KSI 有两个目的。首先,可以通过KSI获得初级插值结果,即全局插值一次。其次,缺失点的概率密度函数 (PDF) 可以通过 KSI 重建,并随后为校正主要插值结果提供参考。通过模拟和现场监测数据对所提方法进行了验证,结果表明,与常用方法相比,计算效率有较大提高。数据重建和PDF重建的融合是新颖的,并通过结构动力响应分析验证了其有效性。重构风荷载引起的结构位移响应的均方根值与实际风荷载引起的结构位移响应的均方根值几乎一致。缺失点的概率密度函数 (PDF) 可以通过 KSI 重建,并随后提供参考以校正主要插值结果。通过模拟和现场监测数据对所提方法进行了验证,结果表明,与常用方法相比,计算效率有较大提高。数据重建和PDF重建的融合是新颖的,并通过结构动力响应分析验证了其有效性。重构风荷载引起的结构位移响应的均方根值与实际风荷载引起的结构位移响应的均方根值几乎一致。缺失点的概率密度函数 (PDF) 可以通过 KSI 重建,并随后提供参考以校正主要插值结果。通过模拟和现场监测数据对所提方法进行了验证,结果表明,与常用方法相比,计算效率有较大提高。数据重建和PDF重建的融合是新颖的,并通过结构动力响应分析验证了其有效性。重构风荷载引起的结构位移响应的均方根值与实际风荷载引起的结构位移响应的均方根值几乎一致。数据重建和PDF重建的融合是新颖的,并通过结构动力响应分析验证了其有效性。重构风荷载引起的结构位移响应的均方根值与实际风荷载引起的结构位移响应的均方根值几乎一致。数据重建和PDF重建的融合是新颖的,并通过结构动力响应分析验证了其有效性。重构风荷载引起的结构位移响应的均方根值与实际风荷载引起的结构位移响应的均方根值几乎一致。
更新日期:2020-10-01
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