基于非参数贝叶斯字典学习的联合时空风场数据外推及不确定性量化

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
George D. Pasparakis , Ioannis A. Kougioumtzoglou , Michael D. Shields
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引用次数: 0

摘要

风速/压力测量,无论是在野外还是在风洞中,通常仅限于少数传感器位置,这阻碍了高分辨率的时空速度/压力场。提出了一种基于非参数贝叶斯字典学习的时空风场联合数据外推和依赖有限/不完全测量的相关统计估计方法。具体而言,利用稀疏/不完整的测量数据,制定了一个时变优化问题,以确定随机风场的相关低维表示的扩展系数。与问题的另一种标准压缩抽样处理方法相比,开发的方法显示出以下优点。首先,贝叶斯公式还可以量化估计中的不确定性。其次,规避了标准的基于cs的应用程序中对扩展基的先验选择的要求。相反,本文以基于所获取的数据的自适应方式完成此操作。总的来说,即使在任意形式的高维数据和相对较大的外推距离的情况下,该方法也显示出更高的外推精度。因此,它可以潜在地用于广泛的风力工程应用,在这些应用中,各种限制规定了使用有限数量的传感器。通过考虑两个案例研究,证明了该方法的有效性。第一个问题涉及模拟风速记录的外推,该记录与三维(二维和时间)域中规定的联合波数-频率功率谱密度相一致。第二部分涉及四维(三维和时间)边界层风洞实验数据的外推,这些数据具有显著的空间变异性和非高斯特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint space–time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
Wind velocity/pressure measurements, whether in the field or a wind tunnel, are typically limited to a small number of sensor locations, which prohibits high resolution of the spatio-temporal velocity/pressure field. A methodology is developed, based on nonparametric Bayesian dictionary learning, for joint space–time wind field data extrapolation and estimation of related statistics by relying on limited/incomplete measurements. Specifically, utilizing sparse/incomplete measured data, a time-dependent optimization problem is formulated for determining the expansion coefficients of an associated low-dimensional representation of the stochastic wind field. Compared to an alternative, standard, compressive sampling treatment of the problem, the developed methodology exhibits the following advantages. First, the Bayesian formulation enables also the quantification of the uncertainty in the estimates. Second, the requirement in standard CS-based applications for an a priori selection of the expansion basis is circumvented. Instead, this is done herein in an adaptive manner based on the acquired data. Overall, the methodology exhibits enhanced extrapolation accuracy, even in cases of high-dimensional data of arbitrary form, and of relatively large extrapolation distances. Thus, it can be used, potentially, in a wide range of wind engineering applications where various constraints dictate the use of a limited number of sensors. The efficacy of the methodology is demonstrated by considering two case studies. The first relates to the extrapolation of simulated wind velocity records consistent with a prescribed joint wavenumber-frequency power spectral density in a three-dimensional domain (2D and time). The second pertains to the extrapolation of four-dimensional (3D and time) boundary layer wind tunnel experimental data that exhibit significant spatial variability and non-Gaussian characteristics.
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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