2023 journal article
Tensor-based flow reconstruction from optimally located sensor measurements
JOURNAL OF FLUID MECHANICS, 962.
author keywords: low-dimensional models; big data; computational methods
TL;DR:
This work introduces a tensor-based sensor placement and flow reconstruction method which retains and exploits the inherent multidimensionality of the flow and is significantly more accurate than similar vectorized methods.
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www.cambridge.org (publisher PDF)
UN Sustainable Development Goal Categories
14. Life Below Water
(OpenAlex)
Source: Web Of Science
Added: August 7, 2023
Reconstructing high-resolution flow fields from sparse measurements is a major challenge in fluid dynamics. Existing methods often vectorize the flow by stacking different spatial directions on top of each other, hence confounding the information encoded in different dimensions. Here, we introduce a tensor-based sensor placement and flow reconstruction method which retains and exploits the inherent multidimensionality of the flow. We derive estimates for the flow reconstruction error, storage requirements and computational cost of our method. We show, with examples, that our tensor-based method is significantly more accurate than similar vectorized methods. Furthermore, the variance of the error is smaller when using our tensor-based method. While the computational cost of our method is comparable to similar vectorized methods, it reduces the storage cost by several orders of magnitude. The reduced storage cost becomes even more pronounced as the dimension of the flow increases. We demonstrate the efficacy of our method on three examples: a chaotic Kolmogorov flow, in situ and satellite measurements of the global sea surface temperature and three-dimensional unsteady simulated flow around a marine research vessel.