Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 23, Issue 1, Pages 811-820Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2016.2598604
Keywords
In situ analysis; rotating stall analysis; Gaussian mixture model; incremental distribution modeling; feature analysis; high performance computing; collaborative development
Categories
Funding
- NSF [IIS- 1250752, IIS-1065025]
- US Department of Energy [DE- SC0007444, DE-DC0012495]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1250752] Funding Source: National Science Foundation
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Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.
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