Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning

TL;DR

This paper introduces a nonparametric Bayesian dictionary learning method for joint space-time wind field data extrapolation, providing uncertainty quantification and adaptive basis selection, with demonstrated improved accuracy in complex wind data scenarios.

Contribution

The methodology offers a novel Bayesian approach that enhances wind data extrapolation accuracy and quantifies uncertainty without requiring predefined basis functions.

Findings

Improved extrapolation accuracy in high-dimensional wind data.

Effective uncertainty quantification in wind field estimates.

Successful application to simulated and experimental wind data.

Abstract

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.