计算机辅助工程(CAE)工具的r树数据结构实现

V. Shelar, Selamani Subramani, Jebaseelan Davidson
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引用次数: 2

摘要

搜索和处理几何数据是任何计算机辅助工程应用程序(CAE)的基本要求。空间搜索和局部搜索在CAD和CAE应用中对于减少模型准备时间具有重要意义。有许多有效的算法被用来搜索几何数据。在不同的CAE平台上,现有的邻居搜索策略存在局限性,且效率不高。R-tree是用于空间访问方法的树型数据结构。本文介绍了r树数据结构及其在一个CAE工具中的实现,用于邻域搜索和局部搜索。它满足了CAE工具中当前邻居搜索的要求。结果表明,与传统方法相比,节省了相当多的时间。这项工作的结论是,r树的实现有助于在CAD和CAE工具中识别邻近零件和减少模型准备时间。
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R-tree data structure implementation for Computer Aided Engineering (CAE) tools
Searching and handling geometric data are basic requirements of any Computer Aided Engineering application (CAE). Spatial search and local search has greater importance in CAD and CAE applications for reducing the model preparation time. There are many efficient algorithms being made to search geometrical data. Current neighbour search strategy is limited and not efficient in different CAE platforms. R-tree is tree data structure used for spatial access methods. This paper presents a review of R-tree data structure with its implementation in one of the CAE tool for neighbour search and local search. It satisfies current neighbour search requirements in CAE tools. Results shows considerable amount of time saving compared to the conventional approach. This work concludes that R-tree implementation can be helpful in identifying neighbour part and reducing model preparation time in CAD and CAE tools.
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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
19
审稿时长
16 weeks
期刊介绍: The International Journal for Simulation and Multidisciplinary Design Optimization is a peer-reviewed journal covering all aspects related to the simulation and multidisciplinary design optimization. It is devoted to publish original work related to advanced design methodologies, theoretical approaches, contemporary computers and their applications to different fields such as engineering software/hardware developments, science, computing techniques, aerospace, automobile, aeronautic, business, management, manufacturing,... etc. Front-edge research topics related to topology optimization, composite material design, numerical simulation of manufacturing process, advanced optimization algorithms, industrial applications of optimization methods are highly suggested. The scope includes, but is not limited to original research contributions, reviews in the following topics: Parameter identification & Surface Response (all aspects of characterization and modeling of materials and structural behaviors, Artificial Neural Network, Parametric Programming, approximation methods,…etc.) Optimization Strategies (optimization methods that involve heuristic or Mathematics approaches, Control Theory, Linear & Nonlinear Programming, Stochastic Programming, Discrete & Dynamic Programming, Operational Research, Algorithms in Optimization based on nature behaviors,….etc.) Structural Optimization (sizing, shape and topology optimizations with or without external constraints for materials and structures) Dynamic and Vibration (cover modelling and simulation for dynamic and vibration analysis, shape and topology optimizations with or without external constraints for materials and structures) Industrial Applications (Applications Related to Optimization, Modelling for Engineering applications are very welcome. Authors should underline the technological, numerical or integration of the mentioned scopes.).
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