Alberto Guarnieri

Francesco Pirotti, Alberto Guarnieri, Sebastiano Chiodini et al.

Point clouds are collected nowadays from a plethora of sensors, some having higher accuracies and higher costs, some having lower accuracies but also lower costs. Not only there is a large choice for different sensors, but also these can be transported by different platforms, which can provide different scan geometries. In this work we test the extraction of four different keypoint detectors and three feature descriptors. We benchmark performance in terms of calculation time and we assess their performance in terms of accuracy in their ability in coarse automatic co-registration of two clouds that are collected with different sensors, platforms and scan geometries. One, which we define as having the higher accuracy, and thus will be used as reference, was surveyed via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox Horizon over a walking path with un-even ground.The novelty in this work consists in comparing several strategies for fast alignment of point clouds from very different surveying geometries, as the drone has a bird's eye view and the bicycle a ground-based view. An added challenge is related to the lower cost of the bicycle sensor ensemble that, together with the rough terrain, reasonably results in lower accuracy of the survey. The main idea is to use range images to capture a simplified version of the geometry of the surveyed area and then find the best features to match keypoints. Results show that NARF features detected more keypoints and resulted in a faster co-registration procedure in this scenariowhereas the accuracy of the co-registration is similar to all the combinations of keypoint detectors and features.

Xiangyu Hu, Alberto Guarnieri

This paper presents a Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) method solving the two-equation Reynolds-Averaged Navier-Stokes (RANS) model {for the turbulent wall-bounded flows with or without flow separation. The inconsistency between the Lagrangian nature of the SPH and RANS model, primarily caused by intense shearing and near-wall discontinuities, is firstly revealed and addressed by the improved mainstream and near-wall treatments, respectively.}The mainstream treatments, including Adaptive Riemann-eddy Dissipation (ARD) and { de-noised} transport velocity formulation, address dissipation incompatibility, turbulent kinetic energy disturbance and over-prediction issues. The near-wall treatments, such as the particle-based wall model realization, weighted near-wall compensation scheme, {and constant wall-normal spacing strategy}, improve the accuracy and stability of the adopted wall model, where the wall dummy particles are still used for future coupling of solid dynamics. Besides, to perform rigorous convergence tests, {a level-set-based Boundary-Offset Technique (BOT)} is developed to {ensure consistent wall-normal distance} across different resolutions. Several benchmark wall-bounded turbulent flow cases are simulated, including straight, mildly curved, strongly curved, Half Converging-Diverging (HCD) channels, and a fish-pass. The present method yields smoothed and reasonably accurate results, and, to the best of our knowledge, achieves for the first time satisfactory convergence of both velocity and turbulent kinetic energy in SPH-RANS simulations. The proposed method bridges particle-based and mesh-based RANS models, providing adaptability for other turbulence models and potential for turbulent fluid-structure interaction (FSI) simulations.