Water velocity measurement using contact and Non-contact type sensor

Waghmare, Amit; Naik, Apurva

doi:10.1109/ccintels.2015.7437935

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…The sensor also compares favourably to other current works on velocity sensing. The low-cost contact- and non-contact-type water velocity sensors presented in [ 15 ] have root sum of squares errors of 10% and 12% over six data points over a range of 0.21 to 1.4 m/s for the contact type and 0.2 to 0.5 m/s for the non-contact type. While these errors are smaller than those for the sensor presented here, they are not too dissimilar and the small number of data points limits the confidence of any conclusion.…”

Section: Resultsmentioning

confidence: 99%

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A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement

Catsamas

Shi

Deletic

et al. 2022

Sensors

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Current commercial sensors to monitor water flow velocities are expensive, bulky, and require significant effort to install. Low-cost sensors open the possibility of monitoring storm and waste water systems at a much greater spatial and temporal resolution without prohibitive costs and resource investment. To aid in this, this work developed a low-cost, low-power velocity sensor based on acoustic Doppler velocimetry. The sensor, costing less than 50 USD is open-source, open-hardware, compact, and easily interfaceable to a wide range of data-logging systems. A freely available sensor design at this price point does not currently exist, and its novelty is in enabling high-resolution real-time monitoring schemes. The design is capable of measuring water velocities up to 1200 mm/s. The sensor is characterised and then verified in an in-field long-term test. Finally, the data from this test are then used to evaluate the performance of the sensor in a real-world scenario. The analysis concludes that the sensor is capable of effectively measuring water velocity.

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Section: Resultsmentioning

confidence: 99%

“…Low-cost contact and non-contact sensors have been proposed by [ 15 ]. Both the contact- and non-contact-type sensors are tested and produce accurate measurements of the water velocity.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement

Catsamas

Shi

Deletic

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…For instance, in [44,45], the Lucas-Kanade algorithm enables subpixel accuracy in estimating glacier flow and sea ice drift. Similarly, in [46], objects drifting on a lake surface are tracked from images analyzed with the Lucas-Kanade approach, and in [47], tracer particles are deployed and tracked onto the surface of a water stream. Further, the Lucas-Kanade algorithm has also been instrumental to investigate high-velocity fields in case of skimming flows above a stepped chute [48] and a flash flood event [49].…”

Section: Introductionmentioning

confidence: 99%

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

et al. 2018

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Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7-8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

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“…Unlike mechanical flow sensors, thermal flow sensors used thick film segmented thermistor to sense heat changes corresponding to different flow velocity [7]. However, Magnetic flow sensors have not been able to be established well in the ever-growing technology industry as their magnetic characteristics makes the reading of the sensor to be inaccurate when it is used near areas that have high magnetic fields or elements [8]. Acoustic sound waves were being used in [9,10] to with an effective signal processing capable machines or software that enables detection of flow rate easily.…”

Section: Introductionmentioning

confidence: 99%

“…Acoustic sound waves were being used in [9,10] to with an effective signal processing capable machines or software that enables detection of flow rate easily. Microelectromechanical system (MEMS) sensors have paved way into the technology market and are preferred due to their capability in [8,11] of using Nano-materials for optically assisted digital cameras and accelerometer for acquiring signals that corresponds to moving fluid flow rate. Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Design, simulation and practical experimentation of miniaturized turbine flow sensor for flow meter assessment

Abdullahi¹,

Habaebi²,

Malik³

2019

Bulletin EEI

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Flow sensors are very essential in many aspects of our daily lives. Many of the industrial processes need a very consistent flow sensor to monitor and check for irregularities in their system. Therefore, flow sensor is an important tool for advanced operation in industrial environment. In this paper, the design and development of a 3D fabricated flow sensor was carried out using SolidWork 3D CAD. SolidWork Flow Simulation was used to model the effect the turbine flow sensor would have on a constant flowing water while MATLAB Simulink flow graph was created to visualize the effect of turbine flow sensor response with voltage input. Afterwards, the design was 3D printed using UP Plus 2 3D printer. The experimentation involved selection of sensors, coding to control the turbine flow sensor and automatic data logging and storage. During the design phase, the sensors and actuators were assembled using locally sourced material. Subsequently, under controlled laboratory environment, the turbine flow sensor was tested using a DC motor which was programmed to control the revolution per minute(rpm) of the turbine flow sensor. The rpm and velocity of the turbine flow meter was measured and stored in a database via Microsoft Excel using Cool Term Software. A total number of 517 readings were analysed to evaluate the performance of the turbine flow sensor. The result shows that the turbine flow meter is responsive to the motor input voltage and yielded accurate measurement of rpm and velocity of turbine flow meter.

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Water velocity measurement using contact and Non-contact type sensor

Cited by 8 publications

References 2 publications

A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement

A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

Design, simulation and practical experimentation of miniaturized turbine flow sensor for flow meter assessment

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