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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 869Evaluating Sampling Strategies for Visualizing...

Evaluating Sampling Strategies for Visualizing Uncertain Multi-Phase Fluid Simulation Data

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Abstract:

With Eulerian Method of Moment (MoM) solvers for the CFD simulation of multi-phase fluid flow, the positions of bubbles or droplets are not modeled explicitly, but through scalar fields of moments. These fields can be interpreted as probability density functions describing the distribution of bubble locations. To enable intuitive visualization that allows direct visual comparisons between simulation and physical experiment, explicit instances of the bubble distribution are required. In this work, we examine one sampling-based method for obtaining sets of bubble positions from density fields. Based on an example dataset, we study the influence of the main parameter, the kernel size, on the resulting bubble set. We identify a tradeoff between numerical accuracy and temporal continuity for the visualization.

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Physical Modeling for Virtual Manufacturing Systems and Processes

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Periodical:

Applied Mechanics and Materials (Volume 869)

Pages:

139-148

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.869.139

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Online since:

August 2017

Authors:

Mathias Hummel*, Lisa Jöckel, Jan Schäfer, Mark W. Hlawitschka, Christoph Garth

Keywords:

Flow Visualization, Method of Moments, Sampling

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* - Corresponding Author

[1] J. W. Tukey, Exploratory data analysis. Pearson, (1977).

[2] J. L. Hintze and R. D. Nelson, Violin plots: a box plot-density trace synergism, The American Statistician, vol. 52, no. 2, pp.181-184, (1998).

DOI: 10.1080/00031305.1998.10480559

[3] K. Potter, J. Kniss, R. Riesenfeld, and C. R. Johnson, Visualizing summary statistics and uncertainty, in Computer Graphics Forum, vol. 29, pp.823-832, Wiley Online Library, (2010).

DOI: 10.1111/j.1467-8659.2009.01677.x

[4] K. Potter, P. Rosen, and C. R. Johnson, From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches, pp.226-249. Berlin, Heidelberg: Springer Berlin Heidelberg, (2012).

DOI: 10.1007/978-3-642-32677-6_15

[5] G. -P. Bonneau, H. -C. Hege, C. R. Johnson, M. M. Oliveira, K. Potter, P. Rheingans, and T. Schultz, Overview and State-of-the-Art of Uncertainty Visualization, pp.3-27. London: Springer London, (2014).

DOI: 10.1007/978-1-4471-6497-5_1

[6] S. K. Lodha, A. Pang, R. E. Sheehan, and C. M. Wittenbrink, Uflow: Visualizing uncertainty in fluid flow, " in Visualization, 96. Proceedings., pp.249-254, IEEE, (1996).

DOI: 10.1109/visual.1996.568116

[7] R. P. Botchen and D. Weiskopf, Texture-based visualization of uncertainty in flow fields, in 16th IEEE Visualization Conference, VIS 2005, Minneapolis, MN, USA, October 23-28, 2005, pp.647-654, (2005).

DOI: 10.1109/vis.2005.97

[8] K. Bürger, P. Kondratieva, J. H. Krüger, and R. Westermann, Importance-driven particle techniques for flow visualization, in IEEE VGTC Pacific Visualization Symposium 2008, PacificVis 2008, Kyoto, Japan, March 5-7, 2008, pp.71-78, (2008).

DOI: 10.1109/pacificvis.2008.4475461

[9] M. W. Hlawitschka, J. Schäfer, M. Hummel, C. Garth, and H. -J. Bart, Populationsbilanzmodellierung mit einem Mehrphasen-CFD-Code und vergleichende Visualisierung, Chemie Ingenieur Technik, vol. 88, no. 10, pp.1480-1491, (2016).

DOI: 10.1002/cite.201600006

[10] M. Hummel, L. Jöckel, J. Schäfer, M. W. Hlawitschka, and C. Garth, Visualizing uncertain multi-phase fluid simulation data using a sampling approach, in EuroVis 2017 Proceedings, 2017. Accepted.

DOI: 10.4028/www.scientific.net/amm.869.139

[11] H. Hulburt and S. Katz, Some problems in particle technology - a statistical mechanical formulation, Chemical Engineering Science, vol. 19, no. 8, pp.555-574, (1964).

DOI: 10.1016/0009-2509(64)85047-8

[12] R. McGraw, Description of aerosol dynamics by the quadrature method of moments, Aerosol Science and Technology, vol. 27, pp.255-265, AUG (1997).

DOI: 10.1080/02786829708965471

[13] D. Marchisio and R. Fox, Solution of population balance equations using the direct quadrature method of moments, Journal of Aerosol Science, vol. 36, pp.43-73, Jan (2005).

DOI: 10.1016/j.jaerosci.2004.07.009

[14] C. Drumm, M. Attarakih, M. W. Hlawitschka, and H. -J. Bart, One-group reduced population balance model for CFD simulation of a pilot-plant extraction column, Industrial & Engineering Chemistry Research, vol. 49, no. 7, pp.3442-3451, (2010).

DOI: 10.1021/ie901411e

[15] D. L. Marchisio, J. T. Pikturna, R. O. Fox, R. D. Vigil, and A. A. Barresi, Quadrature method of moments for population-balance equations, AIChE Journal, vol. 49, no. 5, pp.1266-1276, (2003).

DOI: 10.1002/aic.690490517

[16] J. Von Neumann, Various techniques used in connection with random digits, Monte Carlo Method, National Bureau of Standards Series, vol. 12, pp.36-38, (1951).

[17] J. S. Liu, Monte Carlo strategies in scientific computing. Springer Science & Business Media, (2008).

[18] J. R. Dormand and P. J. Prince, A family of embedded runge-kutta formulae, Journal of computational and applied mathematics, vol. 6, no. 1, pp.19-26, (1980).

DOI: 10.1016/0771-050x(80)90013-3

[19] M. Rosenblatt, Remarks on some nonparametric estimates of a density function, The Annals of Mathematical Statistics, vol. 27, pp.832-837, 09 (1956).

DOI: 10.1214/aoms/1177728190