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Onshore U-OWC Wave Energy Converter: A Hydrodynamic Study of Its Capture Performance Impacted by Air-Compressibility Effects

2026-08-10T03:12:15Z

標題: Onshore U-OWC Wave Energy Converter: A Hydrodynamic Study of Its Capture Performance Impacted by Air-Compressibility Effects 作者: Chow, Yi-Chih; Chang, Hong-Yang; Nguyen, Duy Tong; Lin, Chen-Chou 摘要: The distinct engineering advantages of Oscillating Water Column (OWC) systems have driven substantial academic interest lately. This work examines the onshore U-shaped OWC (U-OWC), selected for its cost-effective installation integrated with existing coastal infrastructure and its superior broadband response to diverse wave climates. Time-domain CFD simulations, incorporating the scaling-rematched approach, were conducted to quantify key hydrodynamic and air-compressibility coefficients, including the amplitude of the wave exciting force, fluid damping coefficient, added mass, absorption factor, and the effective PTO (power take-off) damping and air-compressibility coefficients. These parameters collectively elucidate the underlying hydrodynamics and how they are interwoven with the compressibility of the air in the plenum chamber, thereby impacting the U-OWC's energy-capture performance under incident waves. A principal finding is the identification of a C+ interval wherein air compressibility enhances capture performance in the lower wave-period range examined (<8.0 s). The added mass of the present U-OWC exhibits a remarkably pronounced decrease around the wave period of 8.0 s, which can be verified by a simple resonance formula of heave buoys to underline its strong near-resonance behavior.

Meshless potential flow numerical wave tank with unified wave generating and absorbing boundaries based on generalized finite difference method

2026-08-10T03:11:43Z

標題: Meshless potential flow numerical wave tank with unified wave generating and absorbing boundaries based on generalized finite difference method 作者: Huang, Ji; Lyu, Hong-Guan; Fan, Chia-Ming; Chen, Jiahn-Horng; Chu, Chi-Nan 摘要: This paper presents an improved meshless potential-flow numerical wave tank (NWT) based on the generalized finite difference method (GFDM). A triangular computational domain is introduced to establish a unified wave-generation and wave-absorption framework, thereby enabling directional wave generation and effective suppression of wave reflections within a single formulation. Several benchmark problems involving wave propagation and wave-structure interaction are considered to evaluate the accuracy and convergence of the proposed model. The results demonstrate that the proposed NWT accurately predicts wave fields and hydrodynamic loads, and therefore provides an efficient and reliable numerical tool for coastal and ocean engineering applications.

Deep Learning-Assisted Field-Effect Transistor for Polychromatic Light Sensing and Recognition

2026-08-10T03:11:32Z

標題: Deep Learning-Assisted Field-Effect Transistor for Polychromatic Light Sensing and Recognition 作者: Chen, Guan-Ying; Shen, Yu-Zhen; Huang, Kai-Chun; Luo, Zheng-Yu; Lee, Shu-Sheng; Lin, Chih-Ting 摘要: image sensors have been widely utilized for image capture; however, they rely on color filtering and demosaicing algorithms for color reconstruction in each pixel. This single-photodetector, single-color architecture constrains light utilization and image resolution. In this study, we proposed a deep learning (DL)-assisted field-effect transistor (FET) to decouple mixed light components into their respective wavelengths and intensities simultaneously. Through sequential-bilateral-voltage driving, a single FET generates a series of drain current shifts (DCSs) driven by transient photoelectric effects. Using a convolutional neural network (CNN), the DCS map is decoded into multiple light components. Experiments were conducted with combinations of wavelengths-635 nm (lambda(red)), 510 nm (lambda(green)), and 450 nm (lambda(blue))-and intensities ranging from 0.1 to 0.9 W/cm(2). In monochromatic light experiments, the DCS-CNN achieved an average mean squared error (mse) of 0.0014 and mean absolute error (MAE) of 0.0216, outperforming the baseline flat DCS multilayer perceptron (MLP) by 86% in mse and 43% in MAE, respectively. In addition, our experiments confirmed the robustness of the DCS training method under laser source conditions, with and without 90(degrees) rotation. In polychromatic light experiments, the proposed DCS-CNN achieved 84.5% accuracy in detecting light from 64 distinct combinations. To deepen model understanding, we investigated the impact of transistor operation regions and transient current variation maps on light detection capabilities. Overall, this study demonstrates the potential of the DL-FET architecture for enabling single-shot color sampling in cameras without color filters.

小水線面雙體離岸風電人員運輸船耐海性能評估

2026-03-17T01:40:38Z

標題: 小水線面雙體離岸風電人員運輸船耐海性能評估作者: 方志中; 何達立