A Flow Feature Detection Framework for Massive Computational Data Analytics

Friday, December 7, 2018 - 1:00pm to 2:00pm
Storey Innovation Center (Room 2277)

Dr. Yi Wang from the Department of Mechanical Engineering, University of South Carolina will give a talk on Friday Dec. 7th in the Storey Innovation Center (Room 2277) from 13:00 - 14:00.

In this seminar a framework based on the incremental proper orthogonal decomposition (iPOD) and the data mining method to perform integrated analysis on large-scale computational data will be presented for targeted data visualization, discovery, and learning. Four key components will be introduced, including (1) iPOD based on the mean value and the subspace updating method to incrementally reduce data dimensions, decouple the time-averaged and time-varying flow structures, and extract coherent structures and modes in massive Computational Fluid Dynamics (CFD) data; (2) data mining to classify the flow regions of similar dynamic characteristics and identify the candidate and global ROIs (GROIs) for focused analysis; (3) feature detection to capture flow features of interest and determine ultimate ROIs (UROIs); and (4) selective storage and targeted visualization of data in UROIs. Case studies on vortex and shock wave detection that are of significant interest to aerospace and defense applications will be presented to demonstrate the framework. Computational performance of the framework in terms of data volume, reduction ratio, resource usage, and storage requirements will also be discussed. Our quantitative results clearly show that iPOD is able to process large datasets that overwhelm the traditional batch POD leading to 4-16X data reduction in the temporal domain through spectral projection. By data mining 50% to 70% of the spatial domain with high probability of flow feature occurrence is identified as candidate GROIs for efficient, confined feature detection. Key features in the UROI consisting of only 2% to 30% of the original data are successfully captured by our feature detection algorithms, and can be selectively stored and visualized for targeted discovery and learning. In contrast to batch-POD, iPOD reduces physical memory usage by more than 10X and processing time by up to 75% and is far more appropriate for large data analytics.

Yi Wang obtained his B.S. and M.S. in Machinery and Energy Engineering from Shanghai Jiao Tong University, P.R.China in 1998 and 2000, respectively; and his Ph.D. in Mechanical Engineering from the Carnegie Mellon University in 2005. Currently he is an associate professor of mechanical engineering and is the principal investigator (PI) of the Integrated Multiphysics & Systems Engineering Laboratory (iMSEL) at the University of South Carolina. He has served as a PI or a Co-PI on multiple DoD-, MDA-, NASA-, and NIH-funded projects to develop advanced methodologies and techniques in computational and data-enabled science and engineering (CDS&E), including reduced order modeling, data reduction, large-scale and/or real-time data analytics, hierarchical system-level simulation, and system engineering. The applications of these technologies span spacecraft and missile thermal analysis, aeroservoelasticity and aerothermoservoelasticity, massive computational data management, real-time flight load data processing, integrated multi-scale fluidics systems (design, fabrication, and experimentation) for environmental monitoring, biodefense, and regenerative medicine. He has coauthored 4 book chapters, and 80 journal and conference publications. He is also the co-inventor of 5 patents.