Practical animation of compressible flow for shock waves and related phenomena

Nipun Kwatra, Jón T. Grétarsson, Ronald Fedkiw

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


We propose a practical approach to integrating shock wave dynamics into traditional smoke simulations. Previous methods either simplify away the compressible component of the flow and are unable to capture shock fronts or use a prohibitively expensive explicit method that limits the time step of the simulation long after the relevant shock waves and rarefactions have left the domain. Instead, we employ a semi-implicit formulation of Euler's equations, which allows us to take time steps on the order of the fluid velocity (ignoring the more stringent acoustic wave-speed restrictions) and avoids the expensive characteristic decomposition typically required of compressible flow solvers. We also propose an extension to Euler's equations to model combustion of fuel in explosions. The flow is two-way coupled with rigid and deformable solid bodies, treating the solid-fluid interface effects implicitly in a projection step by enforcing a velocity boundary condition on the fluid and integrating pressure forces along the solid surface. As we handle the acoustic fluid effects implicitly, we can artificially drive the sound speed c of the fluid to ∞ without going unstable or driving the time step to zero. This permits the fluid to transition from compressible flow to the far more tractable incompressible flow regime once the interesting compressible flow phenomena (such as shocks) have left the domain of interest, and allows the use of state-of-the-art smoke simulation techniques.
Original languageEnglish (US)
Title of host publicationACMSIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2010
PublisherAssociation for Computing Machinery,
Number of pages9
ISBN (Print)9783905674279
StatePublished - Jul 2 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-24
Acknowledged KAUST grant number(s): 42959
Acknowledgements: Research supported in part by ONR N0014-06-1-0393, ONR N00014-06-1-0505, ONR N00014-05-1-0479 for a computing cluster, NIH U54-GM072970, NSF ACI-0323866, and King Abdullah University of Science and Technology (KAUST) 42959.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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