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PadMesh: a parallel and distributed framework for interactive mesh generation software

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Abstract

Meshes are the input for computational fluid dynamics (CFD) analysis, whose size and quality have important impact on the simulation results. With the continuous advancement of high-fidelity CFD simulation, the scale of needed computational meshes has become larger and larger, which poses great challenges to the development of interactive mesh generation software. To address this issue, a parallel and distributed framework called PadMesh is proposed, which performs as the infrastructure for developing various interactive mesh generation software (e.g., structured, unstructured and Cartesian). First, the framework PadMesh is demonstrated, including the introduction of design principles, software architecture and key components, namely message-oriented middleware, client application, server application and parallel supporting module. Second, a parallel and distributed structured mesh generation software called PGridStar is developed based on PadMesh. Strategies are investigated on managing the visual data and distributed data for structured meshes. Finally, two functionalities of the preliminary PGridStar are presented, which validate the usability of PadMesh in developing interactive mesh generation software.

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References

  1. Baker TJ (2005) Mesh generation: art or science? Prog Aerosp Sci 41:29–63

    Article  Google Scholar 

  2. Weatherill NP, Hassan O, Morgan K, Jones JW, Larwood BG, Sorenson K (2002) Aerospace simulations on parallel computers using unstructured grids. Int J Numer Methods Fluids 40(1–2):171–187

    Article  Google Scholar 

  3. Nishikawa T, Yamade Y, Sakuma M, Kato C (2012) Application of fully-resolved large eddy simulation to kvlcc2. J Jpn Soc Naval Arch Ocean Eng 16:1–9

    Google Scholar 

  4. Bermejo-Moreno I, Bodart J, Larsson J, Barney BM, Nichols JW, Jones S, (2013) Solving the compressible Navier–Stokes equations on up to 1.97 million cores and 4.1 trillion grid points. In: Proceedings of SC 2013: the international conference for high performance computing, networking, storage and analysis. IEEE Computer Society, Denver, CO, USA, pp 1–10

  5. Pointwise, Inc (2020) Pointwise Homepage. https://pointwise.com/. Accessed 10 July 2020

  6. ANSYS, Inc (2020) ICEM Homepage. https://www.ansys.com/products/platform/ansys-meshing. Accessed 10 July 2020

  7. Ito Y, Shih AM, Erukala AK, Soni BK, Chernikov A, Chrisochoides NP, Nakahashi K (2007) Parallel unstructured mesh generation by an advancing front method. Math Comput Simul 75:200–209

    Article  MathSciNet  Google Scholar 

  8. Simonovski I, Cizelj L (2011) Automatic parallel generation of finite element meshes for complex spatial structures. Comput Mater Sci 50:1606–1618

    Article  Google Scholar 

  9. Chen J, Zhao D, Huang Z, Zheng Y, Wang D (2012) Improvements in the reliability and element quality of parallel tetrahedral mesh generation. Int J Numer Methods Eng 92:671–693

    Article  MathSciNet  Google Scholar 

  10. Loseille A, Menier V, Alauzet F (2015) Parallel generation of large-size adapted meshes. Procedia Eng 124:57–69

    Article  Google Scholar 

  11. Laug P, Guibault F, Borouchaki H (2017) Parallel meshing of surfaces represented by collections of connected regions. Adv Eng Softw 103:13–20

    Article  Google Scholar 

  12. Li X, Yu W, Liu C (2017) Geometry-aware partitioning of complex domains for parallel quad meshing. Comput Aided Des 85:20–33

    Article  Google Scholar 

  13. Xq Wang, Xl Jin, Dz Kou, Chen Jh (2017) A parallel approach for the generation of unstructured meshes with billions of elements on distributed-memory supercomputers. Int J Parallel Program 45(3):680–710

    Article  Google Scholar 

  14. Chrisochoides N (2016) Telescopic approach for extreme-scale parallel mesh generation for CFD applications. In: Proceedings of 46th AIAA fluid dynamics conference, AIAA, pp 1–8

  15. Ekelschot D, Ceze M, Garai A, Murman SM (2019) Parallel high-order anisotropic meshing using discrete metric tensors. In: Proceedings of AIAA Scitech 2019 Forum, AIAA, pp 1–14

  16. ANSYS, Inc (2020) ICEM Parallel Mesh. http://cfd2012.com/icem-parallel-meshing-and-repair.html. Accessed 10 July 2020

  17. Cole JM, Tackett GB, Rupert JK, Davis MN, Pimmel KA, Brown RB, Norris SR, McNeese WV (2004) Centralizing the runtime interface of high-fidelity aircraft models to distributed simulation architectures via the aviation mobility server. In: Proceedings of AIAA modeling and simulation technologies conference and exhibit, AIAA, pp 1–9

  18. Lee W, Stark JL, Markley JL (2014) Ponderosa-c/s: client-server based software package for automated protein 3d structure determination. J Biomol NMR 60:73–75

    Article  Google Scholar 

  19. Brandt A, Staub M (2016) Improving operations and reducing maintenance via server-side software. In: Proceedings of SpaceOps 2016 conference, AIAA, pp 1–11

  20. Imlay ST, Taflin D, Mackey C (2018) A subzone-based client–server technique for i/o efficient analysis and visualization of large remote datasets. In: Proceedings of 2018 AIAA aerospace sciences meeting, AIAA, pp 1–11

  21. Sarkarati M, Briess K, Kayal H (2006) Design and implementation of a remote, server–client-based telemetry retrieval and monitoring system. In: Proceedings of SpaceOps 2006 conference, AIAA, pp 1–8

  22. Lombillo I, Blanco H, Pereda J, Villegas L, Carrasco C, Balbas J (2016) Structural health monitoring of a damaged church: design of an integrated platform of electronic instrumentation, data acquisition and client/server software. Struct Control Health Monitor 23:69–81

    Article  Google Scholar 

  23. Khalfallah M, Figay N, Silva CD, Ghodous P (2016) A cloud-based platform to ensure interoperability in aerospace industry. J Intell Manuf 27(1):119–129

    Article  Google Scholar 

  24. Thames L, Schaefer D (2016) Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52:12–17

    Article  Google Scholar 

  25. Zhang Z, Zhang Y, Lu J, Xu X, Gao F, Xiao G (2018) CMFGIA: a cloud manufacturing application mode for industry alliance. Int J Adv Manuf Technol 98(9–12):2967–2985

    Article  Google Scholar 

  26. Liu Y, Xu X (2017) Industry 4.0 and cloud manufacturing: a comparative analysis. J Manuf Sci Eng Trans ASME 139(3):034701

    Article  Google Scholar 

  27. Pedone G, Mezgar I (2018) Model similarity evidence and interoperability affinity in cloud-ready industry 4.0 technologies. Comput Ind 100:278–286

    Article  Google Scholar 

  28. SpringSource (2020) RabbitMQ Homepage. https://www.rabbitmq.com/. Accessed 10 July 2020

  29. Li Z, Hodgson ME, Li W (2018) A general-purpose framework for parallel processing of large-scale lidar data. Int J Digit Earth 11(1):26–47

    Article  Google Scholar 

  30. Liu Z (2019) A prototype framework for parallel visualization of large flow data. Adv Eng Softw 130:14–23

    Article  Google Scholar 

  31. Wu R, Huang L, Yu P, Zhou H (2017) SunwayMR: a distributed parallel computing framework with convenient data-intensive applications programming. Future Gener Comput Syst 71:43–56

    Article  Google Scholar 

  32. Pinto VG, Schnorr L, Stanisic L, Legrand A, Thibault S, Danjean V (2018) A visual performance analysis framework for task-based parallel applications running on hybrid clusters. Concurr Comput Pract Exp 30:e4472

    Article  Google Scholar 

  33. Hotzer J, Reiter A, Hierl H, Steinmetz P, Selzer M, Nestler B (2018) The parallel multi-physics phase-field framework Pace3D. J Comput Sci 26:1–12

    Article  MathSciNet  Google Scholar 

  34. Padula SL, Gillian RE (2006) Multidisciplinary environments: a history of engineering framework development. In: Proceedings of 11th AIAA/ISSMO multidisciplinary analysis and optimization conference, AIAA, pp 1–11

  35. Chen YY, Yu STJ (2011) Constructing a supercomputing framework by using python for hybrid parallelism and GPU cluster. In: Proceedings of 20th AIAA computational fluid dynamics conference, AIAA, pp 1–17

  36. Commer M, Kowalsky MB, Doetsch J, Newman GA, Finsterle S (2014) MPiTOUGH2: a parallel parameter estimation framework for hydrological and hydrogeophysical applications. Comput Geosci 65:127–135

    Article  Google Scholar 

  37. Xia Z, Wang Q, Wang Y, Yu C (2015) A CAD/CAE incorporate software framework using a unified representation architecture. Adv Eng Softw 87:68–85

    Article  Google Scholar 

  38. Bhatia M, Beran P (2018) Mast: an open-source computational framework for design of multiphysics systems. In: Proceedings of 2018 AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference, AIAA, pp 1–20

  39. Lawlor OS, Chakravorty S, Wilmarth TL, Choudhury N, Dooley I, Zheng G, Kale LV (2006) ParFUM: a parallel framework for unstructured meshes for scalable dynamic physics applications. Eng Comput 22:215–235

    Article  Google Scholar 

  40. Zagaris G, Pirzadeh SZ, Chrisochoides N (2009) A framework for parallel unstructured grid generation for practical aerodynamic simulations. In: Proceedings of 47th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, AIAA, pp 1–16

  41. Harris RE, Williams BR (2013) Ventus: An overset adaptive Cartesian simulation framework for moving boundary problems. In: Proceedings of 21st AIAA computational fluid dynamics conference, AIAA, pp 1–19

  42. Hsieh YM, Pan MS (2014) ESFM: an essential software framework for meshfree methods. Adv Eng Softw 76:133–147

    Article  Google Scholar 

  43. Hereth EA, Sreenivas K, Taylor LK, III DSN (2017) Automatic parallel octree grid generation software with an extensible solver framework and a focus on urban simulations. In: Proceedings of 55th AIAA aerospace sciences meeting, AIAA, pp 1–34

  44. Burstedde C, Wilcox LC, Ghattas O (2011) p4est: scalable algorithms for parallel adaptive mesh refinement on forests of octrees. SIAM J Sci Comput 33(3):1103–1133

    Article  MathSciNet  Google Scholar 

  45. Lawrence Livermore National Laboratory (2020) SAMRAI Homepage. https://computation.llnl.gov/projects/samrai. Accessed 10 July 2020

  46. Wissink AM, Hornung RD, Kohn SR, Smith SS, Elliott N (2001) Large scale parallel structured AMR calculations using the samrai framework. In: Proceedings of the 2001 ACM/IEEE conference on supercomputing, IEEE, pp 22–22

  47. Fattebert JL, Hornung RD, Wissink AM (2007) Finite element approach for density functional theory calculations on locally-refined meshes. J Comput Phys 223(2):759–773

    Article  MathSciNet  Google Scholar 

  48. Griffith BE, Hornung RD, McQueen DM, Peskin CS (2007) An adaptive, formally second order accurate version of the immersed boundary method. J Comput Phys 223(1):10–49

    Article  MathSciNet  Google Scholar 

  49. The Apache Software Foundation (2020) RocketMQ Homepage. https://rocketmq.apache.org/. Accessed 10 July 2020

  50. The Apache Software Foundation (2020) ActiveMQ Homepage. http://activemq.apache.org/. Accessed 10 July 2020

  51. Redis Labs (2020) Redis Homepage. https://redis.io. Accessed 10 July 2020

  52. The Apache Software Foundation (2020) Kafka Homepage. http://kafka.apache.org. Accessed 10 July 2020

  53. ZeroMQ Group (2020) ZeroMQ Homepage. http://zeromq.org. Accessed 10 July 2020

  54. The Khronos Group Inc (2020) WebGL—OpenGL ES for the Web. https://www.khronos.org/webgl/. Accessed 10 July 2020

  55. Bardis G, Koumpouros Y, Sideris N, Voulodimos A, Doulamis N (2019) Webgl enabled smart avatar warping for body weight animated evolution. Entertain Comput 32:100324

    Article  Google Scholar 

  56. Liu D, Peng J, Wang Y, Huang M, He Q, Yan Y, Ma B, Yue C, Xie Y (2019) Implementation of interactive three-dimensional visualization of air pollutants using webgl. Environ Model Softw 114:188–194

    Article  Google Scholar 

  57. Lu F, Qi L, Jiang X, Liu G, Liu Y, Chen B, Pang Y, Hu X (2020) NNW-GridStar: interactive structured mesh generation software for aircrafts. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2020.102803

    Article  Google Scholar 

  58. Lu F, Pang Y, Jiang X, Sun J, Huang Y, Wang Z, Ju J (2018) Automatic generation of structured multiblock boundary layer mesh for aircrafts. Adv Eng Softw 115:297–313

    Article  Google Scholar 

  59. Kageyama A, Yamada T (2014) An approach to exascale visualization: interactive viewing of in-situ visualization. Comput Phys Commun 185(1):79–85

    Article  Google Scholar 

  60. Childs H, Bennett J, Garth C, Hentschel B (2019) In situ visualization for computational science. IEEE Comput Gr Appl 39(6):76–85

    Article  Google Scholar 

  61. USCiLab (2020) cereal—A C++11 library for serialization. https://github.com/USCiLab/cereal. Accessed 10 July 2020

  62. Boost Organization (2020) Serialization tutorial. https://www.boost.org/doc/libs/1_70_0/libs/serialization/doc/index.html. Accessed 10 July 2020

  63. Copernica Marketing Software (2020) AMQP-CPP. https://github.com/CopernicaMarketingSoftware/AMQP-CPP

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Acknowledgements

This work was supported by the Pre-Research Generic Technology Project (41406030201), the National Numerical Windtunnel Project and the National Key Research and Development Plan of China under Grant No. 2017YFB0202101. We also express our gratitude to Wenshuai Zhang (ROMTEC) and Yuefan Hu for their technical supports.

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Correspondence to Xiong Jiang.

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Lu, F., Chen, B., Qi, L. et al. PadMesh: a parallel and distributed framework for interactive mesh generation software. Engineering with Computers 38, 1271–1292 (2022). https://doi.org/10.1007/s00366-020-01049-0

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