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    A stiffly accurate integrator for elastodynamic problems

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    Type
    Article
    Authors
    Michels, Dominik L.
    Luan, Vu Thai
    Tokman, Mayya
    KAUST Department
    Visual Computing Center (VCC)
    Date
    2017-07-21
    Online Publication Date
    2017-07-21
    Print Publication Date
    2017-07-20
    Permanent link to this record
    http://hdl.handle.net/10754/625991
    
    Metadata
    Show full item record
    Abstract
    We present a new integration algorithm for the accurate and efficient solution of stiff elastodynamic problems governed by the second-order ordinary differential equations of structural mechanics. Current methods have the shortcoming that their performance is highly dependent on the numerical stiffness of the underlying system that often leads to unrealistic behavior or a significant loss of efficiency. To overcome these limitations, we present a new integration method which is based on a mathematical reformulation of the underlying differential equations, an exponential treatment of the full nonlinear forcing operator as opposed to more standard partially implicit or exponential approaches, and the utilization of the concept of stiff accuracy which ensures that the efficiency of the simulations is significantly less sensitive to increased stiffness. As a consequence, we are able to tremendously accelerate the simulation of stiff systems compared to established integrators and significantly increase the overall accuracy. The advantageous behavior of this approach is demonstrated on a broad spectrum of complex examples like deformable bodies, textiles, bristles, and human hair. Our easily parallelizable integrator enables more complex and realistic models to be explored in visual computing without compromising efficiency.
    Citation
    Michels DL, Luan VT, Tokman M (2017) A stiffly accurate integrator for elastodynamic problems. ACM Transactions on Graphics 36: 1–14. Available: http://dx.doi.org/10.1145/3072959.3073706.
    Sponsors
    This work has been partially supported by the National Science Foundation of the United States or America (grant 1419105), the King Abdullah University of Science and Technology (KAUST baseline funding), the Max Planck Center for Visual Computing and Communication (MPC-VCC) funded by Stanford University and the Federal Ministry of Education and Research of the Federal Republic of Germany (grants FKZ-01IMC01 and FKZ-01IM10001).
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    ACM Transactions on Graphics
    Conference/Event name
    ACM SIGGRAPH 2017
    DOI
    10.1145/3072959.3073706
    Additional Links
    https://dl.acm.org/citation.cfm?doid=3072959.3073706
    ae974a485f413a2113503eed53cd6c53
    10.1145/3072959.3073706
    Scopus Count
    Collections
    Articles; Visual Computing Center (VCC)

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