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Farzam Allafchi
Mechanical Engineering Department, University of Victoria, Victoria, BC V8W 2Y2, Canada

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Letter
Published: 16 December 2020 in Sensors
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A velocity meter was designed and built in order to meet market needs for an affordable instrument that measures the range of velocity magnitudes and direction experienced in medium- to large-sized water bodies. The velocity meter consists of a graduated plate with an injector protruding from the center and a camera held downward above the plate. Once the Dye Injection Velocity (DIV) meter is in the flow, dye is injected and the camera records the dye fluid transport. The recorded video is analyzed to determine the local flow velocity and direction. The DIV was calibrated for a range of velocities between 0.0094 m/s and 0.1566 m/s using particle image velocimetry (PIV) in a flow visualization flume. The accuracy of the instrument was found to be +6.3% and −9.8% of full scale. The coefficient of determination of the calibration curve was equal to 98%. Once calibrated, the DIV was deployed to the Inverness Stormwater pond in Calgary, Canada, for validation tests against an Acoustic Doppler Velocity (ADV) meter. During the validation tests, both flow velocity magnitude and direction were measured at several spatial points. The velocity magnitude results showed good agreement and the Mann-Whitney test showed no statistically significant difference (p-value > 0.05). At two spatial points, the differences between direction data were significant, which could be caused by the random errors involved in the validation test. However, the averaged data showed good agreement.

ACS Style

Farzam Allafchi; Caterina Valeo; Angus Chu; Jianxun He; Waltfred Lee; Peter Oshkai; Norman Neumann. A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies. Sensors 2020, 20, 7204 .

AMA Style

Farzam Allafchi, Caterina Valeo, Angus Chu, Jianxun He, Waltfred Lee, Peter Oshkai, Norman Neumann. A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies. Sensors. 2020; 20 (24):7204.

Chicago/Turabian Style

Farzam Allafchi; Caterina Valeo; Angus Chu; Jianxun He; Waltfred Lee; Peter Oshkai; Norman Neumann. 2020. "A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies." Sensors 20, no. 24: 7204.

Journal article
Published: 15 May 2019 in Water
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A hydrological model was integrated with a computational fluid dynamics (CFD) model to determine bacteria levels distributed throughout the Inverness stormwater pond in Calgary, Alberta. The Soil Conservation Service (SCS) curve number model was used as the basis of the hydrological model to generate flow rates from the watershed draining into the pond. These flow rates were then used as input for the CFD model simulations that solved the Reynolds-Averaged Navier-Stokes (RANS) equations with k-ɛ turbulence model. E. coli, the most commonly used fecal indicator bacteria for water quality research, was represented in the model by passive scalars with different decay rates for free bacteria and attached bacteria. Results show good agreement with measured data in each stage of the simulations. The middle of the west wing of the pond was found to be the best spot for extracting water for reuse because it had the lowest level of bacteria both during and after storm events. In addition, only one of the four sediment forebays was found efficient in trapping bacteria.

ACS Style

Farzam Allafchi; Caterina Valeo; Jianxun He; Norman F. Neumann. An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds. Water 2019, 11, 1016 .

AMA Style

Farzam Allafchi, Caterina Valeo, Jianxun He, Norman F. Neumann. An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds. Water. 2019; 11 (5):1016.

Chicago/Turabian Style

Farzam Allafchi; Caterina Valeo; Jianxun He; Norman F. Neumann. 2019. "An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds." Water 11, no. 5: 1016.