Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A mesh-constrained discrete point method for incompressible flows with moving boundaries (2404.17542v2)

Published 26 Apr 2024 in physics.flu-dyn, cs.NA, math.NA, and physics.comp-ph

Abstract: Particle-based methods are a practical tool in computational fluid dynamics, and novel types of methods have been proposed. However, widely developed Lagrangian-type formulations suffer from the nonuniform distribution of particles, which is enhanced over time and result in problems in computational efficiency and parallel computations. To mitigate these problems, a mesh-constrained discrete point (MCD) method was developed for stationary boundary problems (Matsuda et al., 2022). Although the MCD method is a meshless method that uses moving least-squares approximation, the arrangement of particles (or discrete points (DPs)) is specialized so that their positions are constrained in background meshes to obtain a closely uniform distribution. This achieves a reasonable approximation for spatial derivatives with compact stencils without encountering any ill-posed condition and leads to good performance in terms of computational efficiency. In this study, a novel meshless method based on the MCD method for incompressible flows with moving boundaries is proposed. To ensure the mesh constraint of each DP in moving boundary problems, a novel updating algorithm for the DP arrangement is developed so that the position of DPs is not only rearranged but the DPs are also reassigned the role of being on the boundary or not. The proposed method achieved reasonable results in numerical experiments for well-known moving boundary problems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (67)
  1. doi:10.1093/mnras/181.3.375.
  2. doi:10.13182/NSE96-A24205.
  3. doi:10.1007/s00217-013-2077-8.
  4. doi:10.1016/j.compbiomed.2012.01.002.
  5. doi:10.1016/j.jbiomech.2022.111081.
  6. doi:10.1016/S0301-9322(00)00023-9.
  7. doi:10.1016/j.compfluid.2019.06.019.
  8. doi:10.1016/j.advwatres.2022.104363.
  9. doi:10.1016/j.polar.2023.100965.
  10. doi:10.1016/j.enganabound.2023.10.013.
  11. doi:10.1016/j.icarus.2017.10.003.
  12. doi:10.3847/PSJ/ac66e8.
  13. doi:10.1090/S0025-5718-1981-0616367-1.
  14. doi:10.1002/(SICI)1097-0207(19990320)44:8<1115::AID-NME547>3.0.CO;2-L.
  15. doi:10.1002/1097-0207(20000810)48:10<1503::AID-NME832>3.0.CO;2-D.
  16. doi:10.1016/S0045-7825(96)01132-2.
  17. doi:10.1007/s40571-014-0027-2.
  18. doi:10.1016/j.cma.2019.112624.
  19. doi:10.1016/j.ijheatmasstransfer.2020.119919.
  20. doi:10.1016/j.jcp.2016.02.039.
  21. doi:10.1006/jcph.1995.1010.
  22. doi:10.1006/jcph.2000.6439.
  23. doi:10.1016/j.apor.2021.102938.
  24. doi:10.1016/j.jcp.2009.05.032.
  25. doi:10.1016/S0309-1708(03)00030-7.
  26. doi:10.1016/j.jcp.2011.10.027.
  27. doi:10.1016/j.apm.2021.08.014.
  28. doi:10.1016/j.cma.2022.115788.
  29. doi:10.1016/j.jcpx.2023.100125.
  30. doi:10.1016/0021-9991(74)90051-5.
  31. doi:10.1016/j.compfluid.2016.12.011.
  32. doi:10.1016/j.compfluid.2020.104806.
  33. doi:10.1016/j.cma.2021.114416.
  34. doi:10.1016/j.cma.2023.116168.
  35. doi:10.1016/j.cma.2023.116159.
  36. doi:10.1007/s40571-015-0059-2.
  37. doi:10.1016/j.compfluid.2019.104242.
  38. doi:10.1007/s40571-023-00636-4.
  39. doi:10.1299/mej.22-00204.
  40. doi:10.1016/0045-7825(94)90112-0.
  41. doi:10.1016/j.compfluid.2014.07.025.
  42. doi:10.1016/j.compfluid.2020.104749.
  43. doi:10.1016/j.cma.2022.114809.
  44. doi:10.1016/j.ecoleng.2024.107216.
  45. doi:10.1016/j.jcp.2008.10.029.
  46. doi:10.1007/s40571-015-0046-7.
  47. doi:10.1016/j.cma.2019.06.035.
  48. doi:10.1016/j.oceaneng.2023.114497.
  49. doi:10.1016/j.jcp.2023.112674.
  50. doi:10.1090/S0025-5718-1968-0242392-2.
  51. doi:10.1006/jcph.2001.6916.
  52. doi:10.1006/jcph.1997.5859.
  53. doi:10.1016/j.jcp.2005.12.016.
  54. doi:10.1002/fld.4382.
  55. doi:10.1299/jfst.2021jfst0013.
  56. doi:10.1002/fld.1281.
  57. doi:10.1016/j.jcp.2013.03.011.
  58. doi:10.17875/gup2009-101.
  59. doi:10.1017/S0022112089002429.
  60. doi:10.1017/S002211209800860X.
  61. doi:10.1006/jfls.2002.0449.
  62. doi:10.1016/j.compfluid.2009.07.011.
  63. doi:10.1016/j.jcp.2019.109122.
  64. doi:10.1016/S0021-9991(03)00310-3.
  65. doi:10.1103/PhysRevLett.85.2216.
  66. doi:10.1002/fld.1381.
  67. doi:10.1016/j.jcp.2012.04.012.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets