Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow

Accurate measurement of pipeline flow is of great significance for industrial and environmental monitoring. Traditional intrusive methods have the disadvantages of high cost and damage to pipeline structure, while non-intrusive techniques can circumvent such issues. Although Taylor's frozen hypothesis has a theoretical advantage in non-intrusive velocity detection, current research focuses on planar flow fields, and its applicability in turbulent circular pipes remains controversial. Moreover, there is no precedent for combining it with distributed acoustic sensing (DAS) technology. This paper constructs a circular pipe turbulence model through large eddy simulation (LES), revealing the spatiotemporal distribution characteristics of turbulent kinetic energy and the energy propagation rules of FK spectra. It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. An experimental circulating pipeline DAS measurement system was set up to complete signal denoising and compare two principles of flow velocity verification. The results show that the vortex structure of turbulent flow in circular pipes remains stable in the convection direction, conforming to theoretical premises; the relative error of average flow velocity by this method is ≤3%, with significant improvements in accuracy and stability in high-flow zones. This study provides innovative methods and experimental basis for non-intrusive flow detection using DAS.

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Publication Details

Journal
Sensors
Published
2025-06-20
DOI
https://doi.org/10.3390/s25133840
Primary Topic
Wind and Air Flow Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow

Chongli Zhong, Hui Wang, Lei Liang, Ke Jiang et al.
Sensors
Wind and Air Flow Studies
article

Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow

Chongli Zhong, Hui Wang, Lei Liang, Ke Jiang, Shu Dai
article en

Abstract

Accurate measurement of pipeline flow is of great significance for industrial and environmental monitoring. Traditional intrusive methods have the disadvantages of high cost and damage to pipeline structure, while non-intrusive techniques can circumvent such issues. Although Taylor's frozen hypothesis has a theoretical advantage in non-intrusive velocity detection, current research focuses on planar flow fields, and its applicability in turbulent circular pipes remains controversial. Moreover, there is no precedent for combining it with distributed acoustic sensing (DAS) technology. This paper constructs a circular pipe turbulence model through large eddy simulation (LES), revealing the spatiotemporal distribution characteristics of turbulent kinetic energy and the energy propagation rules of FK spectra. It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. An experimental circulating pipeline DAS measurement system was set up to complete signal denoising and compare two principles of flow velocity verification. The results show that the vortex structure of turbulent flow in circular pipes remains stable in the convection direction, conforming to theoretical premises; the relative error of average flow velocity by this method is ≤3%, with significant improvements in accuracy and stability in high-flow zones. This study provides innovative methods and experimental basis for non-intrusive flow detection using DAS.

SensorsVol. 25(13)
Wuhan University of Technology (CN)
Ministry of Industry and Information Technology of the People's Republic of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 87%
Wind and Air Flow Studies
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Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow — Chongli Zhong, Hui Wang, et al. · Sensors (2025) | TGRS Research Map | TGRS