A Flow feature detection framework for large-scale computational data based on incremental proper orthogonal decomposition and data mining. Issue 6 (9th August 2018)

• Record Type: Journal Article
• Authors: Robertson, Eric D.; Wang, Yi; Pant, Kapil; Grismer, Matthew J.; Camberos, José A.
• Is Part Of: International journal of computational fluid dynamics. Volume 32:Issue 6/7(2018)
• Page Start: 261
• Page End: 277
• Publication Date: 2018-08-09
• Subjects: Incremental proper orthogonal decomposition -- data mining -- computational fluid dynamics -- feature detection -- large-scale data; Fluid dynamics -- Data processing -- Periodicals; 532.05
• DOI: 10.1080/10618562.2018.1508657
• Languages: English
• ISSNs: 1061-8562

Abstract

ABSTRACT: 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 4–16× 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 10× and time by up to 75%.