skip to main content
research-article

Subspace fluid re-simulation

Published:21 July 2013Publication History
Skip Abstract Section

Abstract

We present a new subspace integration method that is capable of efficiently adding and subtracting dynamics from an existing high-resolution fluid simulation. We show how to analyze the results of an existing high-resolution simulation, discover an efficient reduced approximation, and use it to quickly "re-simulate" novel variations of the original dynamics. Prior subspace methods have had difficulty re-simulating the original input dynamics because they lack efficient means of handling semi-Lagrangian advection methods. We show that multi-dimensional cubature schemes can be applied to this and other advection methods, such as MacCormack advection. The remaining pressure and diffusion stages can be written as a single matrix-vector multiply, so as with previous subspace methods, no matrix inversion is needed at runtime. We additionally propose a novel importance sampling-based fitting algorithm that asymptotically accelerates the precomputation stage, and show that the Iterated Orthogonal Projection method can be used to elegantly incorporate moving internal boundaries into a subspace simulation. In addition to efficiently producing variations of the original input, our method can produce novel, abstract fluid motions that we have not seen from any other solver.

Skip Supplemental Material Section

Supplemental Material

tp104.mp4

References

  1. Amsallem, D., and Farhat, C. 2012. Stabilization of projection-based reduced-order models. International Journal for Numerical Methods in Engineering 91, 4, 358--377.Google ScholarGoogle ScholarCross RefCross Ref
  2. An, S. S., Kim, T., and James, D. L. 2008. Optimizing Cubature for Efficient Integration of Subspace Deformations. ACM Trans. on Graphics 27, 5 (Dec.), 165. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Anttonen, J., King, P., and Beran, P. 2003. POD-based reduced-order models with deforming grids. Mathematical and Computer Modelling 38, 41--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Baraff, D., and Witkin, A. 1992. Dynamic simulation of non-penetrating flexible bodies. In Computer Graphics (Proceedings of SIGGRAPH 92), 303--308. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Barbič, J., and James, D. L. 2005. Real-Time Subspace Integration for St. Venant-Kirchhoff Deformable Models. ACM Trans. on Graphics 24, 3 (Aug.), 982--990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bergmann, M., Cordier, L., and Brancher, J.-P. 2005. Optimal rotary control of the cylinder wake using proper orthogonal decomposition reduced-order model. Physics of Fluids 17, 9, 097101.Google ScholarGoogle ScholarCross RefCross Ref
  7. Berkooz, G., Holmes, P., and Lumley, J. L. 1993. The proper orthogonal decomposition in the analysis of turbulent flows. Annual Rev. Fluid Mech, 539--575.Google ScholarGoogle Scholar
  8. Bourguet, R., Braza, M., and Dervieux, A. 2011. Reduced-order modeling of transonic flows around an airfoil submitted to small deformations. Journal of Computational Physics 230, 1, 159--184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Bro, R., and De Jong, S. 1997. A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics 11, 5, 393--401.Google ScholarGoogle ScholarCross RefCross Ref
  10. Bro, R., 2001. The n-way toolbox. http://bit.ly/Wmq8zM.Google ScholarGoogle Scholar
  11. Brochu, T., Keeler, T., and Bridson, R. 2012. Linear-time smoke animation with vortex sheet meshes. In Proceedings of the ACM SIGGRAPH/Eurographics Sym. on Computer Animation, 87--95. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Carlberg, K., Bou-Mosleh, C., and Farhat, C. 2011. Efficient non-linear model reduction via a least-squares petrovgalerkin projection and compressive tensor approximations. International Journal for Numerical Methods in Engineering 86, 2, 155--181.Google ScholarGoogle ScholarCross RefCross Ref
  13. Chadwick, J. N., An, S. S., and James, D. L. 2009. Harmonic shells: a practical nonlinear sound model for near-rigid thin shells. ACM Trans. Graph. 28, 5 (Dec.), 119:1--119:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Chen, D., and Plemmons, R. 2007. Nonnegativity constraints in numerical analysis. In Symposium on the Birth of Numerical Analysis.Google ScholarGoogle Scholar
  15. De Witt, T., Lessig, C., and Fiume, E. 2012. Fluid simulation using laplacian eigenfunctions. ACM Trans. Graph. 31, 1, 10:1--10:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Deparis, S., and Rozza, G. 2009. Reduced basis method for multi-parameter-dependent steady navierstokes equations: Applications to natural convection in a cavity. Journal of Computational Physics 228, 12, 4359--4378. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Fedkiw, R., Stam, J., and Jensen, H. W. 2001. Visual simulation of smoke. In Proceedings of SIGGRAPH, 15--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Foster, N., and Metaxas, D. 1997. Modeling the motion of a hot, turbulent gas. In Proceedings of SIGGRAPH, ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, 181--188. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Golub, G., and Van Loan, C. 1996. Matrix Computations, 3rd ed. The Johns Hopkins University Press, Baltimore. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Guennebaud, G., Jacob, B., et al., 2010. Eigen v3. http://eigen.tuxfamily.org.Google ScholarGoogle Scholar
  21. Henderson, R. D. 2012. Scalable fluid simulation in linear time on shared memory multiprocessors. In Proceedings of the Digital Production Symposium, ACM Press, 43--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Homescu, C., Petzold, L. R., and Serban, R. 2005. Error estimation for reduced-order models of dynamical systems. SIAM Journal on Numerical Analysis 43, 4, 1693--1714. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. James, D. L., and Fatahalian, K. 2003. Precomputing interactive dynamic deformable scenes. ACM Transactions on Graphics 22, 3 (July), 879--887. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Kim, T., and James, D. L. 2009. Skipping steps in deformable simulation with online model reduction. ACM Transactions on Graphics 28, 5 (Dec.), 123:1--123:9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kim, T., and James, D. L. 2011. Physics-based character skinning using multi-domain subspace deformations. In ACM SIGGRAPH/Eurographics Sym. on Computer Animation, ACM, New York, NY, USA, 63--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Kim, T., Thürey, N., James, D., and Gross, M. 2008. Wavelet turbulence for fluid simulation. ACM Trans. Graph. 27 (August), 50:1--50:6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Kim, D., Sra, S., and Dhillon, I. S. 2012. A non-monotonic method for large-scale non-negative least squares. Optimization Methods and Software (OMS) (Jan.).Google ScholarGoogle Scholar
  28. Kleinberg, J., and Tardos, E. 2006. Algorithm Design. Addison-Wesley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Klingner, B. M., Feldman, B. E., Chentanez, N., and O'Brien, J. F. 2006. Fluid animation with dynamic meshes. In Proceedings of SIGGRAPH, 820--825. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Krysl, P., Lall, S., and Marsden, J. E. 2001. Dimensional model reduction in non-linear finite element dynamics of solids and structures. International Journal for Numerical Methods in Engineering 51, 479--504.Google ScholarGoogle ScholarCross RefCross Ref
  31. Lawson, C. L., and Hanson, R. J. 1974. Solving Least Square Problems. Prentice Hall, Englewood Cliffs, NJ.Google ScholarGoogle Scholar
  32. LeGresley, P. A., and Alonso, J. J. 2001. Investigation of non-linear projection for pod based reduced order models for aerodynamics. In AIAA Aerospace Sciences Meeting and Exhibit.Google ScholarGoogle Scholar
  33. Lentine, M., Zheng, W., and Fedkiw, R. 2010. A novel algorithm for incompressible flow using only a coarse grid projection. ACM Trans. Graph. 29 (July), 114:1--114:9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Losasso, F., Gibou, F., and Fedkiw, R. 2004. Simulating water and smoke with an octree data structure. ACM Trans. Graph. 23, 457--462. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Lumley, J. 1967. The structure of inhomogeneous turbulent flows. Atmospheric turbulence and radio wave propagation, 166--178.Google ScholarGoogle Scholar
  36. Luo, Y., and Duraiswami, R. 2011. Efficient parallel nonnegative least squares on multicore architectures. SIAM Journal on Scientific Computing 33, 5, 2848--2863. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Meyer, M., and Anderson, J. 2007. Key Point Subspace Acceleration and Soft Caching. ACM Transactions on Graphics 26, 3 (July), 74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Moës, N., Dolbow, J., and Belytschko, T. 1999. A finite element method for crack growth without remeshing. International Journal for Numerical Methods in Engineering 46, 1, 131--150.Google ScholarGoogle ScholarCross RefCross Ref
  39. Molemaker, J., Cohen, J. M., Patel, S., and Noh, J. 2008. Low viscosity flow simulations for animation. In ACM SIGGRAPH/Eurographics Sym. on Computer Animation, 9--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Moler, C., and Van Loan, C. 2003. Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later. SIAM Review 45, 1, 3--49.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Mullen, P., Crane, K., Pavlov, D., Tong, Y., and Desbrun, M. 2009. Energy-preserving integrators for fluid animation. ACM Trans. Graph. 28, 3 (July), 38:1--38:8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Narain, R., Sewall, J., Carlson, M., and Lin, M. C. 2008. Fast animation of turbulence using energy transport and procedural synthesis. ACM Trans. Graph. 27 (December), 166:1--166:8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Pentland, A., and Williams, J. 1989. Good vibrations: Modal dynamics for graphics and animation. In Computer Graphics (Proceedings of SIGGRAPH 89), 215--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Pfaff, T., Thuerey, N., and Gross, M. 2012. Lagrangian vortex sheets for animating fluids. ACM Trans. Graph. 31, 4 (July), 112:1--112:8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Pharr, M., and Humphreys, G. 2010. Physically-Based Rendering: From Theory to Implementation. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Portugal, L. F., Júdice, J. J., and Vicente, L. N. 1994. A comparison of block pivoting and interior-point algorithms for linear least squares problems with nonnegative variables. Mathematics of Computation 63, 208 (Oct.), 625--643. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. 1992. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Rabani, E., and Toledo, S. 2001. Out-of-core svd and qr decompositions. In SIAM Conference on Parallel Processing for Scientific Computing.Google ScholarGoogle Scholar
  49. Schechter, H., and Bridson, R. 2008. Evolving sub-grid turbulence for smoke animation. In ACM SIGGRAPH/Eurographics Sym. on Computer Animation, 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Selle, A., Fedkiw, R., Kim, B., Liu, Y., and Rossignac, J. 2008. An unconditionally stable maccormack method. J. Sci. Comput. 35, 2--3 (June), 350--371. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Seo, J., Irving, G., Lewis, J. P., and Noh, J. 2011. Compression and direct manipulation of complex blendshape models. ACM Trans. Graph. 30, 6 (Dec.), 164:1--164:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Serre, G., Lafon, P., Gloerfelt, X., and Bailly, C. 2012. Reliable reduced-order models for time-dependent linearized euler equations. Journal of Computational Physics 231, 15, 5176--5194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Sethian, J. 1999. Level set methods and fast marching methods. Cambridge University Press.Google ScholarGoogle Scholar
  54. Shah, A. 2007. Cooking effects. In ACM SIGGRAPH 2007 courses, ACM, New York, NY, USA, SIGGRAPH '07, 45--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Sifakis, E., and Barbič, J. 2012. Fem simulation of 3d deformable solids: a practitioner's guide to theory, discretization and model reduction. In ACM SIGGRAPH 2012 Courses, ACM, New York, NY, USA, SIGGRAPH '12, 20:1--20:50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Stam, J. 1999. Stable fluids. In SIGGRAPH 1999, 121--128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Stanton, M., Sheng, Y., Wicke, M., Perazzi, F., Yuen, A., and andAdrien Treuille, S. N. 2013. Non-polynomial galerkin projection on deforming meshes. ACM Trans. Graph. 32 (July). Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Treuille, A., Lewis, A., and Popović, Z. 2006. Model reduction for real-time fluids. ACM Transactions on Graphics 25, 3 (July), 826--834. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Vasilescu, M. A. O., and Terzopoulos, D. 2004. Tensortextures: multilinear image-based rendering. ACM Trans. Graph. 23, 3 (Aug.), 336--342. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Wicke, M., Stanton, M., and Treuille, A. 2009. Modular bases for fluid dynamics. ACM Trans. on Graphics 28, 3 (Aug.), 39. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Subspace fluid re-simulation

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Graphics
          ACM Transactions on Graphics  Volume 32, Issue 4
          July 2013
          1215 pages
          ISSN:0730-0301
          EISSN:1557-7368
          DOI:10.1145/2461912
          Issue’s Table of Contents

          Copyright © 2013 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 21 July 2013
          Published in tog Volume 32, Issue 4

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader