Wave diffraction of a hybrid wind turbine foundation with a double-layer aquaculture cage

A hybrid wind turbine foundation combined with a double-layer offshore net cage for marine aquaculture is proposed in this paper. To study the diffraction and hydrodynamic loads on the structure for waves with small steepness, a numerical model was established using linear potential theory and solved using the eigenfunction expansion method. A porosity parameter was introduced to describe the hydrodynamic characteristics of the net panels. The model was validated based on existing numerical results and experimental data. An empirical formula was derived to calculate the porosity parameter based on the opening ratios of the nets. The wavefield and wave force were calculated and analyzed by setting different porosity parameters, spacings between the exterior net and interior net, radius ratios of the exterior net to the wind turbine tower and thicknesses of the friction wheel. Noticeable differences in the wave elevation were observed between the upstream and downstream sides of the nets. At downstream sites, the wavefield exhibits different profiles, particularly for structures with low porosities. Sloshing modes were observed that impacted the force and wave elevation at certain frequencies. For the common fishing nets with large porosities, the spacing between the nets does not have a significant impact on the wavefield and wave force acting on the structure. Moreover, the radius and thickness of the friction wheel have a non-negligible influence on the force acting on the structure, which also narrows the intervals between adjacent sloshing frequencies. In summary, this study provides a perspective for the engineering design and hydrodynamic analysis of a hybrid wind turbine foundation with a double-layer aquaculture cage

PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection

Recently, polar-based representation has shown promising properties in perceptual tasks. In addition to Cartesian-based approaches, which separate point clouds unevenly, representing point clouds as polar grids has been recognized as an alternative due to (1) its advantage in robust performance under different resolutions and (2) its superiority in streaming-based approaches. However, state-of-the-art polar-based detection methods inevitably suffer from the feature distortion problem because of the non-uniform division of polar representation, resulting in a non-negligible performance gap compared to Cartesian-based approaches. To tackle this issue, we present PARTNER, a novel 3D object detector in the polar coordinate. PARTNER alleviates the dilemma of feature distortion with global representation re-alignment and facilitates the regression by introducing instance-level geometric information into the detection head. Extensive experiments show overwhelming advantages in streaming-based detection and different resolutions. Furthermore, our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on Waymo and ONCE validation set, thus achieving competitive results over the state-of-the-art methods.Comment: ICCV 202