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http://hdl.handle.net/2183/41120 Flow rate influence on sediment depth estimation in sewers using temperature sensors
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Schellart, Alma
Jensen, Henriette
Langeveld, Jeroen
Viklander, Maria
Lundy, Lian
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Manuel Regueiro-Picallo, Alma Schellart, Henriette Jensen, Jeroen Langeveld, Maria Viklander, Lian Lundy; Flow rate influence on sediment depth estimation in sewers using temperature sensors. Water Sci Technol 1 June 2024; 89 (11): 3133–3146. doi: https://doi.org/10.2166/wst.2024.193
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Abstract
[Abstract:] Enhancing sediment accumulation monitoring techniques in sewers will enable a better understanding of the build-up processes to develop improved cleaning strategies. Thermal sensors provide a solution to sediment depth estimation by passively monitoring temperature fluctuations in the wastewater and sediment beds, which allows evaluation of the heat-transfer processes in sewer pipes. This study analyses the influence of the flow conditions on heat-transfer processes at the water–sediment interface during dry weather flow conditions. For this purpose, an experimental campaign was performed by establishing different flow, temperature patterns, and sediment depth conditions in an annular flume, which ensured steady flow and room-temperature conditions. Numerical simulations were also performed to assess the impact of flow conditions on the relationships between sediment depth and harmonic parameters derived from wastewater and sediment-bed temperature patterns. Results show that heat transfer between water and sediment occurred instantaneously for velocities greater than 0.1 m/s, and that sediment depth estimations using temperature-based systems were barely sensitive to velocities between 0.1 and 0.4 m/s. A depth estimation accuracy of ±7 mm was achieved. This confirms the ability of using temperature sensors to monitor sediment build-up in sewers under dry weather conditions, without the need for flow monitoring.
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The data collected in the laboratory-scale experimental campaign is openly accessible in ZENODO and available in these in-text data citation reference: Regueiro-Picallo et al. (2024b), with a license Creative Commons Attribution 4.0 International (CC BY-NC 4.0). https://doi.org/10.5281/zenodo.10821837
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