Wind speed inference from environmental flow–structure interactions. Part 2. Leveraging unsteady kinematics

Author:

Cardona Jennifer L.ORCID,Dabiri John O.

Abstract

Abstract This work explores the relationship between wind speed and time-dependent structural motion response as a means of leveraging the rich information visible in flow–structure interactions for anemometry. We build on recent work by Cardona, Bouman and Dabiri (Flow, vol. 1, 2021, E4), which presented an approach using mean structural bending. Here, we present the amplitude of the dynamic structural sway as an alternative signal that can be used when mean bending is small or inconvenient to measure. A force balance relating the instantaneous loading and instantaneous deflection yields a relationship between the incident wind speed and the amplitude of structural sway. This physical model is applied to two field datasets comprising 13 trees of 4 different species exposed to ambient wind conditions. Model generalization to the diverse test structures is achieved through normalization with respect to a reference condition. The model agrees well with experimental measurements of the local wind speed, suggesting that tree sway amplitude can be used as an indirect measurement of mean wind speed, and is applicable to a broad variety of diverse trees.

Funder

National Science Foundation

Publisher

Cambridge University Press (CUP)

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5. Cardona, J. L. , Howland, M. F. , & Dabiri, J. O. (2019). Seeing the wind: visual wind speed prediction with a coupled convolutional and recurrent neural network. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8–14 December 2019, Vancouver, Canada.

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