CoChat
Sign Up
Cited by 111Fluid Dynamics Simulations and InteractionsNumerical methods in engineeringAdvanced Numerical Methods in Computational Mathematics

The DOI matches its Crossref record.

An angular momentum conserving affine-particle-in-cell method

Open access
Jiang, Chenfanfu, Schroeder, Craig, Teran, Joseph·2017·Journal of Computational Physics
DOI 10.1016/j.jcp.2017.02.050
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jcp.2017.02.050

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The APIC method used for transfer between grid and particles allows exact conservation of momentum and angular momentum.

    “Here we used the Affine Particle In Cell (APIC) method for the transfer between grid and particles 36 which allows an exact conservation of momentum and angular momentum.”

    From Mechanisms of slab avalanche release and impact in the Dyatlov Pass incident in 1959 (2021) · cites this paper as [36]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 29

  1. Harlow, 1964, The particle-in-cell method for numerical solution of problems in fluid dynamics, Methods Comput. Phys., 3, 319
  2. Brackbill, 1986, FLIP: a method for adaptively zoned, particle-in-cell calculations of fluid flows in two dimensions, J. Comput. Phys., 65, 314, 10.1016/0021-9991(86)90211-1 · DOI
  3. Brackbill, 1988, FLIP: a low-dissipation, PIC method for fluid flow, Comput. Phys. Commun., 48, 25, 10.1016/0010-4655(88)90020-3 · DOI
  4. Burgess, 1992, Mass matrix formulation of the FLIP particle-in-cell method, J. Comput. Phys., 103, 1, 10.1016/0021-9991(92)90323-Q · DOI
  5. Brackbill, 1987, On modelling angular momentum and vorticity in compressible fluid flow, Comput. Phys. Commun., 47, 1, 10.1016/0010-4655(87)90063-4 · DOI
  6. Love, 2006, An unconditionally stable, energy–momentum consistent implementation of the material point method, Comput. Methods Appl. Mech. Eng., 195, 3903, 10.1016/j.cma.2005.06.027 · DOI
  7. Langdon, 1970, Effects of spatial grid simulation in plasmas, J. Comput. Phys., 6, 247, 10.1016/0021-9991(70)90024-0 · DOI
  8. Okuda, 1972, Nonphysical noises and instabilities in plasma simulation due to a spatial grid, J. Comput. Phys., 10, 475, 10.1016/0021-9991(72)90048-4 · DOI
  9. Brackbill, 1988, The ringing instability in particle-in-cell calculations of low-speed flow, J. Comput. Phys., 75, 469, 10.1016/0021-9991(88)90123-4 · DOI
  10. Sulsky, 1994, A particle method for history-dependent materials, Comput. Methods Appl. Mech. Eng., 118, 179, 10.1016/0045-7825(94)90112-0 · DOI
  11. Sulsky, 1995, Application of a particle-in-cell method to solid mechanics, Comput. Phys. Commun., 87, 236, 10.1016/0010-4655(94)00170-7 · DOI
  12. Monaghan, 1989, On the problem of penetration in particle methods, J. Comput. Phys., 82, 1, 10.1016/0021-9991(89)90032-6 · DOI
  13. Nairn, 2015, Numerical simulation of orthogonal cutting using the material point method, Eng. Fract. Mech., 149, 262, 10.1016/j.engfracmech.2015.07.014 · DOI
  14. Hammerquist, 2017, A new method for material point method particle updates that reduces noise and enhances stability, Comput. Methods Appl. Mech. Eng., 10.1016/j.cma.2017.01.035 · DOI
  15. Gritton, 2017, Improving accuracy in the MPM method using a null space filter, Comput. Part. Mech., 4, 131, 10.1007/s40571-016-0134-3 · DOI
  16. Bardenhagen, 2004, The generalized interpolation material point method, Comput. Model. Eng. Sci., 5, 477
  17. Wallstedt, 2007, Improved velocity projection for the material point method, Comput. Model. Eng. Sci., 19, 223
  18. Jiang, 2015, The affine particle-in-cell method, ACM Trans. Graph., 34, 51:1, 10.1145/2766996 · DOI
  19. Yabe, 2001, The constrained interpolation profile method for multiphase analysis, J. Comput. Phys., 169, 556, 10.1006/jcph.2000.6625 · DOI
  20. Steffen, 2008, Analysis and reduction of quadrature errors in the material point method (MPM), Int. J. Numer. Methods Eng., 76, 922, 10.1002/nme.2360 · DOI
  21. Bardenhagen, 2004, The generalized interpolation material point method, Comput. Model. Eng. Sci., 5, 477
  22. Nguyen, 2017, On a family of convected particle domain interpolations in the material point method, Finite Elem. Anal. Des., 126, 50, 10.1016/j.finel.2016.11.007 · DOI
  23. Sadeghirad, 2011, A convected particle domain interpolation technique to extend applicability of the material point method for problems involving massive deformations, Int. J. Numer. Methods Eng., 86, 1435, 10.1002/nme.3110 · DOI
  24. Gonzalez, 2000, Exact energy and momentum conserving algorithms for general models in nonlinear elasticity, Comput. Methods Appl. Mech. Eng., 190, 1763, 10.1016/S0045-7825(00)00189-4 · DOI
  25. Laursen, 2001, A new solution procedure for application of energy-conserving algorithms to general constitutive models in nonlinear elastodynamics, Comput. Methods Appl. Mech. Eng., 190, 6309, 10.1016/S0045-7825(01)00257-2 · DOI
  26. Simo, 1992, The discrete energy–momentum method. conserving algorithms for nonlinear elastodynamics, Z. Angew. Math. Phys., 43, 757, 10.1007/BF00913408 · DOI
  27. Simo, 1992, Exact energy–momentum conserving algorithms and symplectic schemes for nonlinear dynamics, Comput. Methods Appl. Mech. Eng., 100, 63, 10.1016/0045-7825(92)90115-Z · DOI
  28. Lew, 2004, Variational time integrators, Int. J. Numer. Methods Eng., 60, 153, 10.1002/nme.958 · DOI
  29. Gast, 2015, Optimization integrator for large time steps, IEEE Trans. Vis. Comput. Graph., 21, 1103, 10.1109/TVCG.2015.2459687 · DOI

Cited by

Sorted by citations

Metadata

Authors
Jiang, C., Schroeder, C., & Teran, J.
Affiliations
  • University of Pennsylvania (US)
  • University of California, Riverside (US)
  • University of California, Los Angeles (US)
Corresponding author
Jiang, Chenfanfu
ORCID
Venue
Journal of Computational Physics
Publisher
Elsevier BV
Published
2017-06
Volume
338
Pages
137-164
ISSN
0021-9991 (ISSN-L)
DOI
10.1016/j.jcp.2017.02.050
OpenAlex
W2303895937
Cited by
111
References
0
Citations by year
2026: 15 · 2025: 14 · 2024: 20 · 2023: 9 · 2022: 10 · 2021: 12 · 2020: 13 · 2019: 10 · 2018: 5 · 2017: 2
FWCI
6.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Computational Physics (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • NSF (CCF-1422795)
  • ONR (N000141110719, N000141210834)
  • DOD (W81XWH-15-1-0147)
  • Intel (20112360)
Topics
  • Physical Sciences › Engineering › Computational Mechanics › Fluid Dynamics Simulations and Interactions
  • Physical Sciences › Engineering › Mechanics of Materials › Numerical methods in engineering
  • Physical Sciences › Engineering › Computational Mechanics › Advanced Numerical Methods in Computational Mathematics
Keywords
angular momentum conservation, conservation of linear momentum, numerical diffusion, lumped mass, velocity noise, material point method, hyperelasticity, midpoint rule, velocity filtering

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review