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首页> 外文期刊>International journal of computational fluid dynamics >A Flow feature detection framework for large-scale computational data based onincremental proper orthogonal decomposition and data mining
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A Flow feature detection framework for large-scale computational data based onincremental proper orthogonal decomposition and data mining

机译：基于大规模计算数据的流程特征检测框架，基于onincrenceal正确正交分解和数据挖掘

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摘要

A framework based on incremental proper orthogonal decomposition (iPOD) and data mining to perform large-scale computational data analysis is presented. It includes iPOD to incrementally reduce data dimensions and decouple dynamic flow structures in massive CFD data; data mining to classify and identify candidate global regions of interest (ROIs) for focused analysis; feature detection to capture key flow features and ultimate ROIs (UROIs); and targeted data storage and visualisation. Quantitative results show that iPOD is able to process large datasets that overwhelm the batch-POD, leading to 4-16x data reduction in the temporal domain. Data mining and feature detection algorithms, respectively, identify 50-70% of the spatial domain with high probability of flow feature occurrence and only 2-30% containing key flow features. The UROI and associated data can be selectively stored and visualised. In contrast to batch-POD, iPOD reduces memory usage by more than 10x and time by up to 75%.
机译：提出了一种基于增量正确正交分解（iPod）和数据挖掘以执行大规模计算数据分析的框架。 它包括iPod以逐步减少数据尺寸并在大规模CFD数据中脱钩动态流量结构; 数据挖掘分类和识别候选人全球兴趣区域（ROI）以进行重点分析; 特征检测以捕获关键流量特征和终极ROI（Urois）; 和有针对性的数据存储和可视化。 定量结果表明，iPod能够处理大量数据集，从而压倒批处理POD，导致时间域中的4-16倍。 数据挖掘和特征检测算法分别识别50-70％的空间域，具有高概率的流量发生，仅包含钥匙流特征的2-30％。 可以选择性地存储和可视化UroI和相关数据。 与Batch-Pod相比，iPod将内存使用量减少超过10倍，最高可达75％。

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