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Interactive Exploration of Stress Tensors Used in Computational Turbulent Combustion

  • Adrian Maries
  • Abedul Haque
  • S. Levent Yilmaz
  • Mehdi B. Nik
  • G. Elisabeta Marai
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involves solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this chapter, we discuss the challenges in dense symmetric-tensor visualization applied to turbulent combustion calculation, and analyze the feasibility of using several established tensor visualization techniques in the context of exploring space-time relationships in computationally-simulated combustion tensor data. To tackle the pervasive problems of occlusion and clutter, we propose a solution combining techniques from information and scientific visualization. Specifically, the proposed solution combines a detailed 3D inspection view based on volume rendering with glyph-based representations—used as 2D probes—while leveraging interactive filtering and flow salience cues to clarify the structure of the tensor datasets. Side-by-side views of multiple timesteps facilitate the analysis of time-space relationships. The resulting prototype enables an analysis style based on the overview first, zoom and filter, then details on demand paradigm originally proposed in information visualization. The result is a visual analysis tool to be utilized in debugging, benchmarking, and verification of models and solutions in turbulent combustion. We demonstrate this analysis tool on three example configurations and report feedback from combustion researchers.

Keywords

Large Eddy Simulation Direct Numerical Simulation Tensor Field Turbulent Combustion Bunsen Burner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Acknowledgements to the Pitt Visualization Research group and to NSF-IIS-0952720.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Adrian Maries
    • 1
  • Abedul Haque
    • 1
  • S. Levent Yilmaz
    • 2
  • Mehdi B. Nik
    • 3
  • G. Elisabeta Marai
    • 1
  1. 1.Department of Computer ScienceUniversity of PittsburghPittsburghUSA
  2. 2.Center for Simulation and ModelingUniversity of PittsburghPittsburghUSA
  3. 3.Department of Mechanical Engineering and Materials ScienceUniversity of PittsburghPittsburghUSA

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