Hex-dominant mesh generation has recently received increasing attention from researchers and the simulation community due to its robustness compared to pure hex-mesh generation techniques. In this work, we present a first structure-informed simplification framework, aiming to reduce the number of non-hex cells from the hex-dominant meshes. Our framework extracts individual sub-structures via parallel relations from the input hex-dominant mesh, decomposes self-tangent and self-intersecting sub-structures for structure complexity control, and collapses certain sets of edges that are adjacent to particular non-hex cells to remove them. We design a filtering and ranking strategy to select edges and sub-structures for collapsing. To better understand the complexity of sub-structure configurations, we introduced a novel relation graph that captures the connections between edges and between edges and sub-structures. Additionally, we designed a smoothing algorithm for hex-dominant meshes that enhances hex cell quality, even in meshes containing various cell types. We evaluate the effectiveness of our framework by applying it to many hex-dominant meshes produced by three state-of-the-art hex-dominant meshing techniques. Our results achieve various levels of reduction in the number of non-hex cells of the input meshes, affirming the applicability of our framework for improving the hex-dominant meshes.

Hairpin vortices are fundamental structures within turbulent boundary layers, playing a crucial role in energy dissipation, mixing, and momentum transport. However, accurately extracting these structures remains challenging due to their irregular shapes, varying scales, and entanglement with surrounding vortical structures. This article presents a novel framework for the extraction of hairpin vortices from turbulent boundary layers. The method begins by identifying vortical regions and decomposing them into smaller segments using merge tree-based segmentation. A novel bottom-up rejoining approach is then introduced to group candidate segments according to the geometric and physical characteristics of hairpin vortices, resulting in regions that encompass complete hairpin vortex structures. These regions are subsequently refined and validated through skeleton analysis to detect the characteristic hairpin shape and are further confirmed using additional scalar-based criteria. Finally, smooth enclosing surfaces are generated for effective visualization. To enable quantitative evaluation, reference hairpin vortices are extracted from several flow datasets and used as ground truth. Compared with existing approaches, the proposed method eliminates manual parameter tuning, reduces under- and over-segmentation, and significantly improves both accuracy and computational efficiency. Demonstrations on multiple turbulent flow cases show that the method is robust and effective for hairpin vortex extraction under varying boundary layer conditions.

Hex-dominant mesh generation has received significant attention in recent research due to its superior robustness compared to pure hex-mesh generation techniques. In this work, we introduce the first structure for analyzing hex-dominant meshes. This structure builds on the base complex of pure hex-meshes but incorporates the non-hex elements for a more comprehensive and complete representation. We provide its definition and describe its construction steps. Based on this structure, we present an extraction and categorization of sheets using advanced graph matching techniques to handle the non-hex elements. This enables us to develop an enhanced visual analysis of the structure for any hex-dominant meshes. We apply this structure-based visual analysis to compare hex-dominant meshes generated by different methods to study their advantages and disadvantages. This complements the standard quality metric based on the non-hex element percentage for hex-dominant meshes. Moreover, we propose a strategy to extract a cleaned (optimized) valence-based singularity graph wireframe to analyze the structure for both mesh and sheets. Our results demonstrate that the proposed hybrid base complex provides a coarse representation for mesh element, and the proposed valence singularity graph wireframe provides a better internal visualization of hex-dominant meshes.

A varied channels region proposal and classification network (VCRPCN) is developed based on a deep convolutional neural network (DCNN) and the characteristics of the animals appearing for automatic wildlife animal classification in camera trapped images, the architecture of the network is improved by feeding different channels into different components of the network to accomplish different aims, i.e. the animal images and their background images are employed in the region proposal component to extract region candidates for the animal's location, and the animal images combined with the region candidates are fed into the classification component to identify their categories. This novel architecture considers changes to the image due to the animals' appearances, and identifies potential animal regions in images and extracts their local features to describe and classify them. Five hundred low contrast animal images have been collected. All images have low contrast due to being acquired during the night. Cross‐validation is employed to statistically measure the performance of the proposed algorithm. The experimental results demonstrate that in comparison with the well‐known object detection network, faster R‐CNN, the proposed VCRPCN achieved higher accuracy with the same dataset and training configuration with an average accuracy improvement of 21%.