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Overview and applications of the reproducing Kernel Particle methods

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Summary

Multiple-scale Kernel Particle methods are proposed as an alternative and/or enhancement to commonly used numerical methods such as finite element methods. The elimination of a mesh, combined with the properties of window functions, makes a particle method suitable for problems with large deformations, high gradients, and high modal density. The Reproducing Kernel Particle Method (RKPM) utilizes the fundamental notions of the convolution theorem, multiresolution analysis and window functions. The construction of a correction function to scaling functions, wavelets and Smooth Particle Hydrodynamics (SPH) is proposed. Completeness conditions, reproducing conditions and interpolant estimates are also derived. The current application areas of RKPM include structural acoustics, elastic-plastic deformation, computational fluid dynamics and hyperelasticity. The effectiveness of RKPM is extended through a new particle integration method. The Kronecker delta properties of finite element shape functions are incorporated into RKPM to develop a Cm kernel particle finite element method. Multiresolution and hp-like adaptivity are illustrated via examples.

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  1. W. K. Liu (Professor of Mechanical and Civil Engineering) & T. Belytschko (Walter P. Murphy Professor of Computational Mechanics)

    Present address: Northwestern University, Evanston, Illinois

  2. Y. Chen (Post-doctoral Fellow) & S. Jun (Graduate Student)

    Present address: Department of Mechanical Engineering, Northwestern University, Evanston, Illinois

  3. J. S. Chen (Assistant Professor), C. Pan (Granduate Student) & C. T. Chang

    Present address: Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa

  4. R. A. Uras (Engineer)

    Present address: Reactor Engineering Division, Argonne National Laboratory, Argonne, Illinois

Authors and Affiliations

  1. Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road, 60208, Evanston, Illinois

    W. K. Liu (Professor of Mechanical and Civil Engineering), Y. Chen (Post-doctoral Fellow), S. Jun (Graduate Student), J. S. Chen (Assistant Professor), T. Belytschko (Walter P. Murphy Professor of Computational Mechanics), C. Pan (Granduate Student), R. A. Uras (Engineer) & C. T. Chang

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Liu, W.K., Chen, Y., Jun, S. et al. Overview and applications of the reproducing Kernel Particle methods. ARCO 3, 3–80 (1996). https://doi.org/10.1007/BF02736130

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