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The mechanical and hydrodynamic characteristics of single-piece nets are key to the design and optimization of offshore aquaculture net cages. A numerical approach for offshore submerged aquaculture net materials based on the Morison equations and finite element is proposed, simulating the hydrodynamic characteristics of single-piece nets under varying parameters such as wire diameter, mesh size, and flow velocity, and simulating the impact of marine organism attachment on nets by modifying the drag coefficient. The simulation results of nets made from materials such as Copper–Zinc Alloy (Cu-Zn), Zinc–Aluminum Alloy (Zn-Al), Semi-Rigid Polyethylene Terephthalate (PET), and Ultra-High Molecular Weight Polyethylene (UHMWPE) are compared, which provides a theoretical basis for optimizing design parameters and selecting materials for nets based on force conditions and hydrodynamic characteristics. The simulation results indicate that the current force on the net is positively correlated with flow velocity; the maximum displacement of the net is also positively correlated with the flow rate. Compared to other materials, the Cu-Zn net is subjected to the greatest water flow force, while the UHMWPE net experiences the greatest displacement; the larger the diameter of the netting twine, the greater the current force on the net; the mesh size is inversely related to the current force on the net. With increasing drag coefficient, both the maximum displacement of the net and the current force experiences increase, and UHMWPE material nets are more sensitive to increases in the drag coefficient, which indicates a greater impact from the attachment of marine organisms. The density and elastic modulus of the netting material affect the rate of increase in force on the net. The research results can provide a basis for further research on material selection and design of deep-sea aquaculture nets.

Seepage analysis of earth dams is essential for design and efficient control measures. An earth dam can be rendered ineffective if it exhibits uncontrolled seepage through foundations and if subjected to uplift. A variety of methods have been developed to determine seepage and uplift pressures under embankments, but results exhibit differences. In this study an overview of graphical, numerical, analytical, and physical model methods to determine seepage and uplift pressure has been provided. The underlying methods i.e., D. Forchheimer flow nets, Element Methods Finite (FEM) solutions from SLIDE software, Velocity Hodographic and Schwartz Christoffel transformations, and seepage tank model respectively have been applied on Sukian Dyke of Mangla Dam as a case study. It is conclusive that all methods provide overestimates for seepage values, however FEM provides results closer to actual field values. Statistical analysis was performed to rank the most suitable method for determining uplift pressure. D. Forchheimer flow nets and transformations predict uplift pressures closer to field values. The crucial aspect of the study is to quantify the accuracy of the prediction methods.

Determining the water permeability of concrete in structures remains a conundrum because of difficulties in removing the influences of moisture. This study describes the extended flow-net theory developed on the basis of the two-pressure-head concept, which provides a means of measuring permeability under the partially saturated condition. Surface-mounted tests and standard laboratory water penetration tests were carried out to verify this approach. Before determining the water permeability, steady-state flow rates at two different pressure levels were evaluated and the effects of initial moisture conditions on flow behavior were investigated. The results indicate that the proposed approach does offer a useful means of determining the water permeability of structural concrete, although it cannot be claimed tobe universally applicable for all moisture conditions likely to be encountered in practice. Keywords: extended flow-net theory; in-place water permeability; two-pressure-head test; unsaturated flow.

In this study, enhanced net channel based heat sink designs for cooling HCPV systems at geometrical concentration ratios ranging from 500× to 3000× are presented. The effect of increasing the number of layers in the parallel flow net channel, as well as the fraction of the coolant mass flow rate in the counter flow net channel, on the overall performance of the HCPV systems, are investigated. The various configurations of each proposed net channel based-heat sink design are examined, and a comparative analysis between the different proposed designs is performed under the climate weather conditions of Dammam city, Saudi Arabia. On one hand, the double-layered counter flow net channel heat sink outperformed the other designs in terms of electrical efficiency and in keeping the solar cell operating well below the safe operating limits, achieving a reduction in maximum cell temperature relatively compared to the parallel flow net channel with five layers and conventional mini channel of 11.72% and 12.01%, respectively. On the other hand, for effective usability of the heat recovery rate by the cooling mechanism, the parallel flow net channel is the most appropriate design since it has recorded 27.55% higher outlet water temperature than the double-layered counter flow net channel.

Steady, 2-,3-D Darcian flows generated by a dipole (a pair of horizontal or vertical injection–abstraction wells closely placed one above another), with circulation of fresh water inside an interface confined lens or “bubble” underneath an impermeable caprock, surrounded by a static saline groundwater, are analytically studied. For 2-D dipole, the complex potential domain is a plane with a horizontal cut. This domain is conformally mapped onto a reference half-plane where the Keldysh–Sedov formula is used to obtain the complex physical coordinates. Explicit closed-form expressions for the vase-shaped interface, flow net, isohypses, magnitudes of the Darcian velocity and Riesenkampf’s resultant force are obtained, depending on the dipole moment, its position with respect to the caprock, and the ratio of densities of the two fluids. It is shown that for sufficiently small injection-pumping rates the fresh water “vase” separates from the caprock and becomes a circle, inside which streamlines are Newtons’ loops of monodiametral degenerate hyperbolae (cubics). Two numerical codes, MT3DMS and SEAWAT, are also used for delineation of isoconcentric lines, which qualitatively corroborate the analytical solutions in delineation of the “bubble” in the part where the sharp interface model predicts stable free boundaries and evidencing “dimples” on the boundary of the “bubble” where the saline water overlies the fresh one. For 3-D dipole not bounded by the caprock, the analytical fresh water “bubble” is a sphere and solution follows, mutatis mutandis , from the textbook formulae for flow of an ideal fluid past an impermeable sphere. The Stokes streamlines inside the sphere are sixtics; isotachs are plotted in an axial section. Stability of the soil matrix near the wells is also discussed.

The proposed RMS-FlowNet++ is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation that can operate on high-density point clouds. For hierarchical scene flow estimation, existing methods rely on expensive Farthest-Point-Sampling (FPS) to sample the scenes, must find large correspondence sets across the consecutive frames and/or must search for correspondences at a full input resolution. While this can improve the accuracy, it reduces the overall efficiency of these methods and limits their ability to handle large numbers of points due to memory requirements. In contrast to these methods, our architecture is based on an efficient design for hierarchical prediction of multi-scale scene flow. To this end, we develop a special flow embedding block that has two advantages over the current methods: First, a smaller correspondence set is used, and second, the use of Random-Sampling (RS) is possible. In addition, our architecture does not need to search for correspondences at a full input resolution. Exhibiting high accuracy, our RMS-FlowNet++ provides a faster prediction than state-of-the-art methods, avoids high memory requirements and enables efficient scene flow on dense point clouds of more than 250K points at once. Our comprehensive experiments verify the accuracy of RMS-FlowNet++ on the established FlyingThings3D data set with different point cloud densities and validate our design choices. Furthermore, we demonstrate that our model has a competitive ability to generalize to the real-world scenes of the KITTI data set without fine-tuning.

Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. Therefore, there are many security management problems. The existing solutions can only solve the problems of a small-scale crowd, which contains a simple abnormal behavior and a clear surveillance video. However, the performance hasn’t reached a satisfactory result for a large-scale crowd. Therefore, we propose an abnormal behavior detection method based on optical flow and generative adversarial network (GAN). There are three main contributions in this paper. Firstly, the dynamic features of the model are extracted based on the optical flows. The effectiveness of the features is validated by experiments. Secondly, we propose an optical flow framework based on GAN and use a transfer learning strategy to detect behavioral abnormalities in large-scale crowd scenes. The framework uses U-Net and Flownet to generate and distinguish the normal and abnormal behaviors of individuals within the massive crowds. Finally, a number of abnormal behavior pilgrimage videos from different scenes is collected and tested. The accuracy of UMN scenes 1, 2, 3, and UCSD reaches 99.4%, 97.1%, 97.6% and 89.26%, respectively. It also achieves 79.63% of detection accuracy in the large-scale crowd videos using Abnormal Behaviors HAJJ dataset.

Flow field prediction is essential for airfoil design. It is a time-consuming task to obtain the flow fields around an airfoil. Convolution neural networks (CNN) have been applied for flow field prediction in recent years. However, CNN-based methods rely heavily on convolutional kernels to process information within local neighborhoods, making it difficult to capture global information. In this paper, we propose a novel self-attention generative network referred to as SAG-FlowNet, both for original and optimization airfoil flow field prediction. We investigate the self-attention mechanism with a multi-layer convolutional generative network. We use the self-attention module to capture various information within and between flow fields, and with the help of the attention module, the CNN can utilize the information with stronger relationships regardless of their distances to achieve better flow field prediction results. Through extensive experiments, we explore the proposed SAG-FlowNet performance. The experimental results show that the method has accurate and universal performance for the reconstruction and prediction of the flow field both for original and optimized airfoils. SAG-FlowNet is promising for fast flow field prediction and has potential applications in accelerating airfoil design.

According to this study, the ECAP process with different BP is performed on the engineering magnesium alloy ( AZ31 ). The effect of BP on the strain condition and flow paths may differ as the statically deformed portion of the sample passes through the strain zone. The BP application of 25, 50 and 100 MPa brings about the forward typical shear rotation of structural components in the horizontal direction. The complex (20 x 20 x 200) mm 3 ECAP engine is applied with movable outer walls and specially designed sliding bottoms for low friction (moving die). The flow model is made by a cubic flow net of the DEFORM software tool on the workpiece. Structural features and initial micro-hardness measurements made on the x and y planes of the workpiece also received a special attention. Since BP leads to increased density the micro-hardness could be improved and ascribed to the change in texture.

Inspired by biological vision mechanism, event‐based cameras have been developed to capture continuous object motion and detect brightness changes independently and asynchronously, which overcome the limitations of traditional frame‐based cameras. Complementarily, spiking neural networks (SNNs) offer asynchronous computations and exploit the inherent sparseness of spatio‐temporal events. Notably, event‐based pixel‐wise optical flow estimations calculate the positions and relationships of objects in adjacent frames; however, as event camera outputs are sparse and uneven, dense scene information is difficult to generate and the local receptive fields of the neural network also lead to poor moving objects tracking. To address these issues, an improved event‐based self‐attention optical flow estimation network (SA‐FlowNet) that independently uses criss‐cross and temporal self‐attention mechanisms, directly capturing long‐range dependencies and efficiently extracting the temporal and spatial features from the event streams is proposed. In the former mechanism, a cross‐domain attention scheme dynamically fusing the temporal‐spatial features is introduced. The proposed network adopts a spiking‐analogue neural network architecture using an end‐to‐end learning method and gains significant computational energy benefits especially for SNNs. The state‐of‐the‐art results of the error rate for optical flow prediction on the Multi‐Vehicle Stereo Event Camera (MVSEC) dataset compared with the current SNN‐based approaches is demonstrated. As event camera outputs are sparse and uneven, dense scene information is difficult to generate and the local receptive fields of the neural network also lead to poor moving objects tracking. To address these issues, we propose an improved event‐based self‐attention optical flow estimation network (SA‐FlowNet) that independently uses criss‐cross and temporal self‐attention mechanisms, directly capturing long‐range dependencies, collecting all pixel‐related information and efficiently extracting the temporal and spatial features from the event streams.