Advertisement

Journal of Visualization

, Volume 21, Issue 5, pp 819–834 | Cite as

Adjoint-enhanced flow visualization

  • Christopher Koehler
  • Ryan Durscher
  • Philip Beran
  • Nitin Bhagat
Regular Paper
  • 118 Downloads

Abstract

This work presents a novel method of visually capturing the relative importance of different flow regions with respect to a quantity of interest. Existing flow visualization techniques are enhanced to convey flow importance. This is accomplished by filtering their output with additional information obtained from the adjoint counterpart of the physical flow field. The additional adjoint data is of equal resolution to the physical flow field. The adjoint flow data links physical fluid regions to user chosen quantities of interest. Thus, regions that are less relevant to the quantity of interest can be masked or deemphasized to reduce clutter and to highlight the behavior of the more important flow regions. The concept is demonstrated on a series of fluid simulations. The visualizations highlight the temporally changing importance of flow features, regions of influence in complex flows, and occlusion reduction in 3D flows. The method is also demonstrated by checking the impact of perturbations introduced in flow regions that were found to be both important and unimportant based on the adjoint data.

Graphical abstract

Keywords

Flow visualization Sensitivity analysis Unsteady flow Adjoint Vortex dynamics Clutter reduction 

Notes

Acknowledgements

The early portion of this work was supported by the Air Force Office of Scientific Research under Laboratory Task 09RB01COR (monitored by Dr. Doug Smith).

Supplementary material

12650_2018_490_MOESM1_ESM.mpeg (5.6 mb)
Supplementary material 1 (mpeg 5744 KB)
12650_2018_490_MOESM2_ESM.mpeg (4.8 mb)
Supplementary material 2 (mpeg 4880 KB)
12650_2018_490_MOESM3_ESM.mpeg (4.2 mb)
Supplementary material 3 (mpeg 4272 KB)
12650_2018_490_MOESM4_ESM.mpeg (2.7 mb)
Supplementary material 4 (mpeg 2771 KB)

References

  1. Chan Y, Correa CD, Ma K (2013) The generalized sensitivity scatterplot. IEEE TVCG 19(10):1768–1781Google Scholar
  2. Chan Y, Correa C, Ma K (2010) Flow-based scatterplots for sensitivity analysis. In: Visual analytics science and technology (VAST), 2010 IEEE Symposium on, pp 43–50Google Scholar
  3. Chaudhuri A, Lee TY, Shen HW, Wenger R (2014) Exploring flow fields using space-filling analysis of streamlines. IEEE TVCG 20(10):1392–1404Google Scholar
  4. Chen M, Ebert D, Hagen H, Laramee RS, van Liere R, Ma K, Ribarsky W, Scheuermann G, Silver D (2009) Data, information, and knowledge in visualization. IEEE Comput Graph Appl 29(1):12–19CrossRefGoogle Scholar
  5. Correa C, Crnovrsanin T, Ma K (2012) Visual reasoning about social networks using centrality sensitivity. IEEE TVCG 18(1):106–120Google Scholar
  6. Demir I, Kehrer J, Westermann R (2016) Screen-space silhouettes for visualizing ensembles of 3D isosurfaces. In: IEEE Pacific Visualization Symposium, pp 204–208Google Scholar
  7. Doleisch H (2007) Simvis: interactive visual analysis of large and time-dependent 3D simulation data. Simul Conf 2007(Winter):712–720Google Scholar
  8. Giles MB, Pierce NA (2000) An introduction to the adjoint approach to design. Flow Turbul Combust 65(3–4):393–415CrossRefzbMATHGoogle Scholar
  9. Günther T, Rössl C, Theisel H (2013) Opacity optimization for 3D line fields. ACM Trans Graph 32(4):1:201–1:208CrossRefzbMATHGoogle Scholar
  10. Guo Z, Ward MO, Rundensteiner EA, Ruiz C (2011) Pointwise local pattern exploration for sensitivity analysis. In: Visual analytics science and technology (VAST), 2011 IEEE conference on, pp 131–140Google Scholar
  11. Koehler C, Wischgoll T, Dong H, Gaston Z (2011) Vortex visualization in ultra low reynolds number insect flight. IEEE TVCG 17(12):2071–2079Google Scholar
  12. Laramee R, Hauser H, Doleisch H, Vrolijk B, Post F, Weiskopf D (2004) The state of the art in flow visualization: dense and texture-based techniques. Comput Graph Forum 23:2004CrossRefGoogle Scholar
  13. Li L, Shen H (2007) Image based streamline generation and rendering. IEEE TVCG 13(3):630–640Google Scholar
  14. Li L, Hsieh H, Shen H (2008)Illustrative streamline placement and visualization. In: IEEE Pacific visualization symposium, pp 79–86Google Scholar
  15. Ma J, Wang C, Shene C, Jiang J (2014) A graph-based interface for visual analytics of 3D streamlines and pathlines. IEEE TVCG 20(8):1127–1140Google Scholar
  16. Marchesin S, Chen C, Ho C, Ma K (2010) View-dependent streamlines for 3D vector fields. IEEE TVCG 16(6):1578–1586Google Scholar
  17. Marta AC, Shankaran S, Wang Q, Venugopal P (2013) Interpretation of adjoint solutions for turbomachinery flows. AIAA J 51(7):1733–1744CrossRefGoogle Scholar
  18. Martins J, Hwang J (2012) Review and unification of methods for computing derivatives of multidisciplinary computational models. AIAA J 51(11):2582–2599CrossRefGoogle Scholar
  19. Matvienko V, Krüger J (2013) A metric for the evaluation of dense vector field visualizations. IEEE TVCG 19(7):1122–1132Google Scholar
  20. Mavriplis DJ (2007) Discrete adjoint-based approach for optimization problems on three-dimensional unstructured meshes. AIAA J 45(4):741–750CrossRefGoogle Scholar
  21. Ma J, Wang C, Shene CK (2013) Coherent view-dependent streamline selection for importance-driven flow visualization. Proc. SPIE 8654(865):407–865, 407–15Google Scholar
  22. McLoughlin T, Laramee RS, Peikert R, Post FH, Chen M (2010) Over two decades of integration-based, geometric flow visualization. Comput Graph Forum 29(6):1807–1829CrossRefGoogle Scholar
  23. McNamara A, Treuille A, Popović Z, Stam J (2004) Fluid control using the adjoint method. ACM Trans Graph 23(3):449–456CrossRefGoogle Scholar
  24. Muigg P, Kehrer J, Oeltze S, Piringer H, Doleisch H, Preim B, Hauser H (2008) A four-level focus+context approach to interactive visual analysis of temporal features in large scientific data. Comput Graph Forum:775–782Google Scholar
  25. Nemec M, Aftosmis M, Wintzer M (2008) Adjoint-based adaptive mesh refinement for complex geometries. In: 46th AIAA aerospace sciences meeting and exhibit, aerospace sciences meetings. American Institute of Aeronautics and Astronautics, pp 1–23Google Scholar
  26. Nielsen EJ, Diskin B (2013) Discrete adjoint-based design for unsteady turbulent flows on dynamic overset unstructured grids. AIAA J 51(6):1355–1373CrossRefGoogle Scholar
  27. Ozer S, Silver D, Bemis K, Martin P (2014) Activity detection in scientific visualization. IEEE TVCG 20(3):377–390Google Scholar
  28. Schlemmer M, Hotz I, Hamann B, Morr F, Hagen H (2007)Priority streamlines: a context-based visualization of flow fields. In: EuroVis, pp 227–234Google Scholar
  29. Shi K, Theisel H, Hauser H, Weinkauf T, Matkovic K, Hege HC, Seidel HP (2009) Path line attributes—an information visualization approach to analyzing the dynamic behavior of 3D time-dependent flow fields. In: Topology-based methods in visualization II, mathematics and visualization, pp 75–88Google Scholar
  30. Stegmaier S, Rist U, Ertl T (2005) Opening the can of worms: an exploration tool for vortical flows. In: IEEE visualization. IEEE Computer Society, Los Alamitos, CA, USA, pp 463–470Google Scholar
  31. Stompel A, Lum E, Ma KL (2002) Feature-enhanced visualization of multidimensional, multivariate volume data using non-photorealistic rendering techniques. In: Pacific Graphics conferenceGoogle Scholar
  32. Tao J, Wang C, Shene C, Kim SH (2014) A deformation framework for focus+context flow visualization. IEEE TVCG 20(1):42–55Google Scholar
  33. Theisel H, Weinkauf T, Hege HC, Seidel HP (2003) Saddle connectors—an approach to visualizing the topological skeleton of complex 3D vector fields. In: IEEE visualization, pp 225–232Google Scholar
  34. Tricoche X, Scheuermann G, Hagen H (2000) A topology simplification method for 2D vector fields. In: IEEE visualization, pp 359–366Google Scholar
  35. Vanella M, Rabenold P, Balaras E (2010) A direct-forcing embedded-boundary method with adaptive mesh refinement for fluid–structure interaction problems. J Comput Phys 229(18):6427–6449MathSciNetCrossRefzbMATHGoogle Scholar
  36. Verma V, Kao D, Pang A (2000) A flow-guided streamline seeding strategy. In: IEEE visualization, pp 163–170Google Scholar
  37. Viola I, Feixas M, Sbert M, Groller ME (2006) Importance-driven focus of attention. IEEE TVCG 12(5):933–940Google Scholar
  38. Viola I, Kanitsar A, Groller ME (2004) Importance-driven volume rendering. In: IEEE visualization, pp 139–145Google Scholar
  39. Wang Q (2013) Forward and adjoint sensitivity computation of chaotic dynamical systems. J Comput Phys 235:1MathSciNetCrossRefzbMATHGoogle Scholar
  40. Wang Q, Gao JH (2013) The drag-adjoint field of a circular cylinder wake at reynolds numbers 20, 100 and 500. J Fluid Mech 730:145–161MathSciNetCrossRefzbMATHGoogle Scholar
  41. Wang C, Yu H, Ma KL (2008) Importance-driven time-varying data visualization. IEEE TVCG 14(6):1547–1554Google Scholar
  42. Yang Z, Sarkar P, Hu H (2012) Visualization of the tip vortices in a wind turbine wake. J Vis 15(1):39–44CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.Universal Technology CorporationDaytonUSA
  2. 2.US Air Force Research LaboratoryWright-Patterson Air Force BaseDaytonUSA
  3. 3.University of Dayton Research InstituteDaytonUSA

Personalised recommendations