{'fraction_non_alphanumeric': 0.027570093457943926, 'fraction_numerical': 0.008411214953271028, 'mean_word_length': 5.278592375366569, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Efficient Block Detection Algorithm For Structured Grid Generation

This paper presents an efficient block detection algorithm that can convert a complex wire frame geometry into a set of oriented multi–block structured faces or blocks. The algorithm uses a recursive depth first search in conjunction with the Grid Topology Model (GTM) data structure. The GTM data structure is a Boundary Representation (B–Rep) radial edge non–manifold solid modeling data structure. The block detection algorithm abstracts the user from having to manually specify orientation and connectivity information traditionally needed in other grid generation systems. Introduction A multi–block structured grid has multiple regions (blocks) within a single geometry that have their own corresponding local orientation and coordinate system. Each block may match other blocks at their end vertices, or be composed of composite elements that allow partial face matching. For clarity and brevity, only full face matched blocks will be considered in this paper. In two dimensions (2D), a cycle of four Edges determines a Face (from this point on the definition of a 2D block). In three dimensions (3D), a cycle of 4 Faces with closing end Faces (a logical cube) determines a block. Traditional grid generation systems force their users to manually build blocks and their associated orientations [2,5,6,8,9,11]. Automatically detecting and building these blocks from a graph of Faces, Edges and Vertices that correspond to the geometry of a configuration is the focus of this paper. The only task the user must do is build the geometry that forms the wire–frame model of the blocking. The Grid Topology Model Data Structure Perhaps the most important feature of the automated block detection algorithm is the underlying Grid Topology Model (GTM) data structure. A variety of grid generation systems were investigated [2,5,6,8,9,11], but none of them were sufficient for supporting automated block detection. The GTM data structure was based on a standard representation of non– manifold solid geometry and efficiency of adjacency queries. Weiler’s Radial Edge Non–