• Journal of the Korean Society of Visualization
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• v.14 no.2
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• pp.12-17
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• 2016

In this work, we investigate the flow velocity controllability of a diffuser-type multiple hydrofoil duct by experimental and numerical flow visualization approaches. The flow velocity controllability is analyzed by changing the angle of the hydrofoil near the outlet, which is the diffuser, while the incoming flow velocity is 0.6 m/s in the experiment. When the diffuser angle is changed from 0 to 7.5 degree, the maximum velocity inside the duct is varied from 1.35 m/s to 1.52 m/s. Also, it is shown from the numerical analysis that the maximum velocity is varied from 1.09 m/s to 1.17 m/s in the same condition. Thus, the aspect of the acceleration in the duct due to the increase of the diffuser angle is similar between the both approaches. Therefore, the multiple hydrofoil duct can be used to control the flow speed inside the duct for continuously extracting power close to a rated capacity.

• Journal of the Korean Society of Visualization
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• v.17 no.2
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• pp.25-31
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• 2019

A device that consists of an array of hydrofoils (called a vane deployer) is widely used in ocean engineering. In general, the vane deployer has to spread out efficiently, which is possible by enhancing the lift-to-drag ratio. In the present study, using a computational fluid dynamics, we investigate the effect of hydrofoil arrangement on the lift-to-drag ratio to establish the condition in which a reasonable level of constant lift-to-drag ratio is achieved in a wide range of angle of attack, to avoid a degradation of the hydrodynamic performance. First, the flow around two-dimensional hydrofoil array is examined by varying the size of hydrofoil components, gap between the hydrofoils, and arrangement type. As a result, we determine the optimized hydrofoil array configuration whose lift-to-drag ratio is nearly independent on the angle of attack. Finally, a three-dimensional simulation is performed for the optimized geometry to estimate the performance of actual vane deployer.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.