Chun-Ming Chen, Soumya Dutta, Xiaotong Liu, Gregory Heinlein,
Han-Wei Shen and Jen-Ping Chen
Abstract:
Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing, high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.
Preview Video:
Background music credit: Apple iMovie 10
Paper:
Typographical Errors:
- Fig. 12 (c): The time step should be 325 instead of 355.
- Fig. 12 (d): The time step should be 327 instead of 371.
- Reference [2]: The author name should be C.C. Aggarwal, instead of C.C. Aggarawal.
- We apologize for these errors.
Affiliation:
- Chun-Ming Chen, Soumya Dutta, Xiaotong Liu and Han-Wei Shen are with The Department of Computer Science and Engineering, The Ohio State University
- Gregory Heinlein and Jenping Chen are with The Department of Mechanical and Aerospace Engineering, The Ohio State University
Contact:
- Chun-Ming Chen : chen.1701 at osu.edu [website]