A Flow feature detection framework for large-scale computational data based on incremental proper orthogonal decomposition and data mining

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%.

KEYWORDS:

• Incremental proper orthogonal decomposition
• data mining
• computational fluid dynamics
• feature detection
• large-scale data

Acknowledgements

This research was sponsored by DoD/AFOSR under the contract number FA9550-14-C-0002. This article was cleared for public release by the 88th Air Base Wing at Wright-Patterson AFB, case number 88ABW-2016-5246.

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Yi Wang http://orcid.org/0000-0002-5750-3181

Additional information

Funding

This work was supported by Air Force Office of Scientific Research [Grant Number FA9550-14-C-0002].