• Journal of Hydrogen and New Energy
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• v.33 no.1
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• pp.95-104
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• 2022

Micro hydropower is a readily available renewable energy source that can be harvested utilizing hydrokinetic turbines from shallow water canals, irrigation and industrial channel flows, and run-off river stream flows. These sources generally have low head (<1 m) and low velocity which makes it difficult to harvest energy using conventional turbines. A horizontal-axis screw turbine was designed and numerically tested to extract power from such low-head water sources. The 3-bladed screw-type turbine is placed horizontally perpendicular to the incoming flow, partially submerged in a narrow water channel at no-head condition. The turbine hydraulic performances were studied using Computational Fluid Dynamics models. Turbine design parameters such as the shroud diameter, the hub-to-shroud ratios, and the submerged depths were obtained through a steady-state parametric study. The resulting turbine configuration was then tested by solving the unsteady multiphase free-surface equations mimicking an actual open channel flow scenario. The turbine performance in the shallow channel were studied for various Tip Speed Ratios (TSR). The highest power coefficient was obtained at a TSR of 0.3. The turbine was then scaled-up to test its performance on a real site condition at a head of 0.3 m. The highest power coefficient obtained was 0.18. Several losses were observed in the 3-bladed turbine design and to minimize losses, the number of blades were increased to five. The power coefficient improved by 236% for a 5-bladed screw turbine. The fluid losses were minimized by increasing the blade surface area submerged in water. The turbine performance was increased by 74.4% after dipping the turbine to a bottom wall clearance of 30 cm from 60 cm. The final output of the novel horizontal-axis screw turbine showed a 2.83 kW power output at a power coefficient of 0.63. The turbine is expected to produce 18,744 kWh/year of electricity. The design feasibility test of the turbine showed promising results to harvest energy from small hydropower sources.

• Composites Research
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• v.28 no.4
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• pp.155-161
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• 2015

A composite structure was fabricated with embedded impact detection capabilities for applications in Structural Health Monitoring (SHM). By embedding sensor functionality in the composite, the structure can successfully perform impact localization in real time. Smart resin, composed of $Pb(Ni_{1/3}Nb_{2/3})O_3-Pb(Zr,\;Ti)O_2$ (PNN-PZT) powder and epoxy resin with 1:30 wt%, was used instead of conventional epoxy resin in order to activate the sensor function in the composite structure. The embedded impact sensor in the composite was fabricated using Hand Lay-up and Vacuum Assisted Resin Transfer Molding(VARTM) methods to inject the smart resin into the glass-fiber fabric. The electrodes were fabricated using silver paste on both the upper and bottom sides of the specimen, then poling treatment was conducted to activate the sensor function using a high voltage amplifier at 4 kV/mm for 30 min at room temperature. The composite's piezoelectric sensitivity was measured to be 35.13 mV/N by comparing the impact force signals from an impact hammer with the corresponding output voltage from the sensor. Because impact sensor functionality was successfully embedded in the composite structure, various applications of this technique in the SHM industry are anticipated. In particular, impact localization on large-scale composite structures with complex geometries is feasible using this composite embedded impact sensor.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.87-103
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• 2017

In recent years, many small and medium-sized manufacturing companies are making process innovation and product innovation through the public knowledge services. K Research institute provides different types of knowledge services in combination and due to this complexity, it is difficult to analyze the performance of knowledge service programs precisely. In this study, we derived performance items from bottom-up viewpoints, rather than top-down approaches selecting those items as in previous performance analysis. As a result, 74 items were finded from 82 companies in the K Research Institute case book, and the final result was refined to 17 items. After that a case-performance matrix was constructed, and binary data was entered to analyze. As a result, three clusters were identified through K-means clustering as 'enhancement of core competitiveness (product and patent),' 'expansion of domestic and overseas market,' and 'improvement of operational efficiency.'