Search

Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship's contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision. [ABSTRACT FROM AUTHOR]

Ceramic slurry is the raw material used in stereolithography, and its performance determines the printing quality. Rheological behavior, one of the most important physical factors in stereolithography, is critical in ceramic printing, significantly affecting the flow, spreading, and printing processes. The rheological behavior of SiO2 slurry used in stereolithography technology is investigated in the current research using different powder diameters and temperatures. The results present the apparent non-Newtonian behavior. The yielding characteristics occur in all cases. For single-powder cases, the viscosity decreases when the powder diameter is increased. When the nano-sized and micro-sized powders are mixed in different proportions, a more significant proportion of micron-sized powders will decrease the viscosity. With an increase in the nano-sized powders, the slurry exhibits the shear thinning behavior; otherwise, the shear thickening behavior is observed. Thus, the prediction model is built based on the use of the pelican optimization algorithm-deep extreme learning machine (POA-DELM), and the model in then compared with the fitted and traditional models to validate the effectiveness of the method. A more accurate viscosity prediction model will contribute to better fluid dynamic simulation in future work. [ABSTRACT FROM AUTHOR]

This study analyzes the performance degradation of traditional hybrid wind turbines under high blade-tip-speed ratio conditions and proposes solutions through two-dimensional Computational Fluid Dynamics (CFD) simulations. It also introduces the design of two innovative asynchronous-hybrid dual-rotor wind turbines. The results indicate a remarkable 98.5% enhancement in torque performance at low blade-tip-speed ratios with the hybrid wind turbine model. However, as the blade-tip-speed ratio increases, it leads to negative torque generation within the inner rotor of the conventional design, resulting in a reduction of the power coefficient by up to 13.1%. The introduction of the new wind turbine design addresses this challenge by eliminating negative torque at high blade-tip-speed ratios through adjustments in the inner rotor's operating range. This modification not only rectifies the negative torque issue but also enhances the performance of the outer rotor in the leeward region, consequently boosting the overall power coefficient. Moreover, the optimized inner rotor configuration effectively disrupts and shortens the wake length by 16.7%, with this effect intensifying as the rotational speed increases. This optimization is pivotal for enhancing the efficiency of multi-machine operations within wind farms. [ABSTRACT FROM AUTHOR]

Examining dual vertical-axis wind turbines (VAWTs) across various turbulence scenarios is crucial for advancing the efficiency of urban energy generation and promoting sustainable development. This study introduces a novel approach by employing two-dimensional numerical analysis through computational fluid dynamics (CFD) software to investigate the performance of VAWTs under varying turbulence intensity conditions, a topic that has been relatively unexplored in existing research. The analysis focuses on the self-starting capabilities and the effective utilization of wind energy, which are key factors in urban wind turbine deployment. The results reveal that while the impact of increased turbulence intensity on the self-starting performance of VAWTs is modest, there is a significant improvement in wind energy utilization within a specific turbulence range, leading to an average power increase of 1.41%. This phenomenon is attributed to the more complex flow field induced by heightened turbulence intensity, which delays the onset of dynamic stall through non-uniform aerodynamic excitation of the blade boundary layer. Additionally, the inherent interaction among VAWTs contributes to enhanced turbine output power. However, this study also highlights the trade-off between increased power and the potential for significant fatigue issues in the turbine rotor. These findings provide new insights into the optimal deployment of VAWTs in urban environments, offering practical recommendations for maximizing energy efficiency while mitigating fatigue-related risks. [ABSTRACT FROM AUTHOR]