Design and Validation of a Multipoint Pressure Measurement Network for Hydrodynamic State Estimation in Experimental Water Tanks
Abstract
This paper presents a distributed pressure measurement network for low-amplitude hydrodynamic pressure fluctuations in confined water tanks. The architecture uses modular 8-channel ESP32 acquisition units and is designed to scale to 30 modules (240 sensing points), while the experimentally characterized configuration comprises 96 sensors over a 1 m 2 instrumented area. The network operates at 200 Hz with wireless time-coherent sampling. The measurement chain is characterized through calm-water baseline measurements, a relative frequencyresponse sweep of the pneumatic coupling, timing-coherence tests, and a CNC-controlled dynamic repeatability experiment. In the selected 0.2 Hz to 8 Hz analysis band, the 96-channel configuration exhibits a median RMS noise floor of 20.1 mPa, a band-limited baseline expanded uncertainty of 0.041 Pa (k = 2) associated with quiescent noise and quantization, and a minimum detectable amplitude of about 0.060 Pa using a 3σ in-band noise criterion. The CNC experiment gives a median peak-topeak amplitude of 1.06 Pa and a median expanded repeatability uncertainty of 76.7 mPa. A dedicated software tool synchronizes pressure and video data and displays reconstructed pressure maps from the 96 localized measurements. Live-fish recordings are used as a representative application dataset rather than as the primary metrological validation. Beyond component integration, the contribution is a characterized measurement method combining non-invasive wall-pressure sensing, modular deployment, and data-efficient acquisition for studies of freely behaving aquatic animals.
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BibTeX
@article{porcon2026design,
title = {{Design and Validation of a Multipoint Pressure Measurement Network for Hydrodynamic State Estimation in Experimental Water Tanks}},
author = {Corentin Porcon and James Herbert-Read and Bastien Lagneaux and Gurvan Jodin and Florence Razan},
journal = {IEEE Transactions on Instrumentation and Measurement},
year = {2026},
month = sep,
doi = {10.1109/tim.2026.3712921},
url = {https://hal.science/hal-05750846v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Design and Validation of a Multipoint Pressure Measurement Network for Hydrodynamic State Estimation in Experimental Water Tanks
AU - Corentin Porcon
AU - James Herbert-Read
AU - Bastien Lagneaux
AU - Gurvan Jodin
AU - Florence Razan
PY - 2026
DA - 2026/09/15
JO - IEEE Transactions on Instrumentation and Measurement
DO - 10.1109/tim.2026.3712921
UR - https://hal.science/hal-05750846v1
AB - This paper presents a distributed pressure measurement network for low-amplitude hydrodynamic pressure fluctuations in confined water tanks. The architecture uses modular 8-channel ESP32 acquisition units and is designed to scale to 30 modules (240 sensing points), while the experimentally characterized configuration comprises 96 sensors over a 1 m 2 instrumented area. The network operates at 200 Hz with wireless time-coherent sampling. The measurement chain is characterized through calm-water baseline measurements, a relative frequencyresponse sweep of the pneumatic coupling, timing-coherence tests, and a CNC-controlled dynamic repeatability experiment. In the selected 0.2 Hz to 8 Hz analysis band, the 96-channel configuration exhibits a median RMS noise floor of 20.1 mPa, a band-limited baseline expanded uncertainty of 0.041 Pa (k = 2) associated with quiescent noise and quantization, and a minimum detectable amplitude of about 0.060 Pa using a 3σ in-band noise criterion. The CNC experiment gives a median peak-topeak amplitude of 1.06 Pa and a median expanded repeatability uncertainty of 76.7 mPa. A dedicated software tool synchronizes pressure and video data and displays reconstructed pressure maps from the 96 localized measurements. Live-fish recordings are used as a representative application dataset rather than as the primary metrological validation. Beyond component integration, the contribution is a characterized measurement method combining non-invasive wall-pressure sensing, modular deployment, and data-efficient acquisition for studies of freely behaving aquatic animals.
SN - 0018-9456
ER -