Visualization of Parameter Sensitivity of 2D Time-Dependent Flow
Conference paper
First Online:
- 1k Downloads
Abstract
In this paper, we present an approach to analyze 1D parameter spaces of time-dependent flow simulation ensembles. By extending the concept of the finite-time Lyapunov exponent to the ensemble domain, i.e., to the parameter that gives rise to the ensemble, we obtain a tool for quantitative analysis of parameter sensitivity both in space and time. We exemplify our approach using 2D synthetic examples and computational fluid dynamics ensembles.
Keywords
Visualization of flow ensembles Parameter sensitivityNotes
Acknowledgments
The research leading to these results has been done within the subproject A7 of the Transregional Collaborative Research Center SFB / TRR 165 “Waves to Weather” funded by the German Science Foundation (DFG).
References
- 1.Bonneau, G.-P., et al.: Overview and state-of-the-art of uncertainty visualization. In: Hansen, C.D., Chen, M., Johnson, C.R., Kaufman, A.E., Hagen, H. (eds.) Scientific Visualization. MV, pp. 3–27. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6497-5_1CrossRefGoogle Scholar
- 2.Chandler, J., Bujack, R., Joy, K.I.: Analysis of error in interpolation-based pathline tracing. In: EuroVis Short Paper Proceeedings (2016)Google Scholar
- 3.Chen, C., Biswas, A., Shen, H.W.: Uncertainty modeling and error reduction for pathline computation in time-varying flow fields. In: IEEE Pacific Visualization Symposium (PacificVis), pp. 215–222, April 2015Google Scholar
- 4.Ferstl, F., Bürger, K., Westermann, R.: Streamline variability plots for characterizing the uncertainty in vector field ensembles. IEEE Trans. Vis. Comput. Graph. 22(1), 767–776 (2016)CrossRefGoogle Scholar
- 5.Ferstl, F., Kanzler, M., Rautenhaus, M., Westermann, R.: Visual analysis of spatial variability and global correlations in ensembles of iso-contours. Comput. Graph. Forum 35(3), 221–230 (2016)CrossRefGoogle Scholar
- 6.Haller, G.: Distinguished material surfaces and coherent structures in three-dimensional fluid flows. Physica D 149, 248–277 (2001)MathSciNetCrossRefGoogle Scholar
- 7.Hummel, M., Obermaier, H., Garth, C., Joy, K.I.: Comparative visual analysis of Lagrangian transport in CFD ensembles. IEEE Trans. Vis. Comput. Graph. 19(12), 2743–2752 (2013)CrossRefGoogle Scholar
- 8.McLoughlin, T., et al.: Visualization of input parameters for stream and pathline seeding. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 6(4), 124–135 (2015)Google Scholar
- 9.Pfaffelmoser, T., Westermann, R.: Visualizing contour distributions in 2D ensemble data. In: EuroVis Short Paper Proceedings, pp. 55–59 (2013)Google Scholar
- 10.Sanyal, J., Zhang, S., Dyer, J., Mercer, A., Amburn, P., Moorhead, R.: Noodles: a tool for visualization of numerical weather model ensemble uncertainty. IEEE Trans. Vis. Comput. Graph. 16(6), 1421–1430 (2010)CrossRefGoogle Scholar
- 11.Shadden, S., Lekien, F., Marsden, J.: Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows. Physica D: Nonlinear Phenom. 212(3–4), 271–304 (2005)MathSciNetCrossRefGoogle Scholar
- 12.Üffinger, M., Sadlo, F., Ertl, T.: A time-dependent vector field topology based on streak surfaces. IEEE Trans. Vis. Comput. Graph. 19(3), 379–392 (2013)CrossRefGoogle Scholar
- 13.Whitaker, R.T., Mirzargar, M., Kirby, R.M.: Contour boxplots: a method for characterizing uncertainty in feature sets from simulation ensembles. IEEE Trans. Vis. Comput. Graph. 19(12), 2713–2722 (2013)CrossRefGoogle Scholar
Copyright information
© Springer Nature Switzerland AG 2018