Skip to main content
SpringerLink
Log in

SunwayURANS: 3D full-annulus URANS simulations of transonic axial compressors on Sunway TaihuLight

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Three-dimensional full-annulus Unsteady Reynolds Averaged Navier–Stokes (URANS) simulations play a crucial role in predicting the aerodynamic performance of the transonic axial compressor rotor. In this paper, we report our work, SunwayURANS, which can bring the transonic axial compressor rotor simulation a step closer to practical application. Multi-level parallel techniques are proposed to boost the simulation speed, including a process-level domain decomposition and a process mapping scheme, a thread-level solution to improve pipeline efficiency using DMA and register communication, and a processor-specific vectorization scheme. Aiming to perform the correct and stable simulation, we present a software framework, which includes the pre-processing module, solver module, and IO module. When using 7.2 billion grid points with 450 thousand cores, the simulation achieves 611.53 million grid points to be updated per second and performance of 95.805TFlops. Running in Sunway TaihuLight, experiments show that SunwayURANS does not change the accuracy of the original simulation software, and can be used in the actual simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Schuster DM (2011) The expanding role of applications in the development and validation of CFD at NASA. Comput Fluid Dyn 2010:3–29. https://doi.org/10.1007/978-3-642-17884-9_1

    Article  MATH  Google Scholar 

  2. Asgari H, Chen X, Morini M, Pinelli M, Sainudiin R, Spina PR, Venturini M (2016) NARX models for simulation of the start-up operation of a single-shaft gas turbine. Appl Therm Eng 93:368–376. https://doi.org/10.1016/j.applthermaleng.2015.09.074

    Article  Google Scholar 

  3. Morini M, Pinelli M, Venturini M (2007) Development of a one-dimensional modular dynamic model for the simulation of surge in compression systems. J Turbomach 129:437–447. https://doi.org/10.1115/1.2447757

    Article  Google Scholar 

  4. Liu A, Ju Y, Zhang C (2021) Parallel rotor/stator interaction methods and steady/unsteady flow simulations of multi-row axial compressors. Aerosp Sci Technol 34:1561–1573. https://doi.org/10.2514/1.B37038

    Article  Google Scholar 

  5. Fu H, Liao L, Ding N, Duan X, Gan L, Liang Y, Wang X, Yang J, Zheng Y, Liu W, Wang L, Yang G (2017) Redesigning CAM-SE for peta-scale climate modeling performance and ultra-high resolution on Sunway TaihuLight, In: The International Conference for High Performance Computing, Networking, Storage and Analysis, pp 1:1–1:12, https://doi.org/10.1145/3126908.3126909

  6. Fu H, He C, Chen B, Yin Z, Zhang Z, Zhang W, Zhang T, Xue W, Liu W, Yin W, Yang G, Chen X (2017) 18.9-Pflops nonlinear earthquake simulation on Sunway TaihuLight: enabling depiction of 18-Hz and 8-meter scenarios, In: The International Conference for High Performance Computing, Networking, Storage and Analysis, pp 2:1–2:12, https://doi.org/10.1145/3126908.3126909

  7. Yao J, Davis RL, Alonso JJ, Jameson A (2002) Massively parallel simulation of the unsteady flow in an axial turbine stage. J Propul Power 18(2):465–471. https://doi.org/10.2514/2.5957

    Article  Google Scholar 

  8. Gourdain N, Wlassow F, Ottavy X (2012) Effect of tip clearance dimensions and control of unsteady flows in a multi-stage high-pressure compressor. J Turbomach 134(5):051005. https://doi.org/10.1115/1.4003815

    Article  Google Scholar 

  9. Chen J, Hathaway MD, Herrick GP (2008) Prestall behavior of a transonic axial compressor stage via time-accurate numerical simulation. J Turbomach 130(4):041014. https://doi.org/10.1115/1.2812968

    Article  Google Scholar 

  10. Gan J, Im H, Zha G (2008) Simulation of Stall Inception of a High Speed Axial Compressor with Rotor-Stator Interaction, In: 51st AIAA/SAE/ASEE Joint Propulsion Conference, p 3932, https://doi.org/10.2514/6.2015-3932

  11. Yamada K, Furukawa M, Tamura Y, Saito S, Matsuoka A, Nakayama K (2017) Large-scale detached-eddy simulation analysis of stall inception process in a multistage axial flow compressor. J Turbomach 139(7):071002. https://doi.org/10.1115/1.4035519

    Article  Google Scholar 

  12. Zhao F, Dodds J, Vahdati M (2018) Poststall behavior of a multistage high speed compressor at off-design conditions. J Turbomach 140(12):121002. https://doi.org/10.1115/1.4041142

    Article  Google Scholar 

  13. Cozzi L, Rubechini F, Marconcini M, Arnone A, Astrua P, Schneider A, Silingardi A (2017) Facing the challenges in CFD modelling of multistage axial compressors, In: Turbo Expo: Power for Land, Sea, and Air, p V02BT41A007, https://doi.org/10.1115/GT2017-63240

  14. Liu A, Ju Y, Zhang C (2018) Parallel simulation of aerodynamic instabilities in transonic axial compressor rotor. J Propuls Power 116:106859. https://doi.org/10.1016/j.ast.2021.106859

    Article  Google Scholar 

  15. He F, Dong X, Zou N, Wu W, Zhang X (2020) Structured mesh-oriented framework design and optimization for a coarse-grained parallel CFD solver based on hybrid MPI/OpenMP programming. J Supercomput 76(4):2815–41. https://doi.org/10.1007/s11227-019-03063-6

    Article  Google Scholar 

  16. Asensio IA, Laguna AA, Aissa MH, Poedts S, Ozak N, Lani A (2019) A GPU-enabled implicit Finite Volume solver for the ideal two-fluid plasma model on unstructured grids. Comput Phys Commun 239:16–32. https://doi.org/10.1016/j.cpc.2019.01.019

    Article  MathSciNet  Google Scholar 

  17. Stone CP, Walden A, Zubair M, Nielsen EJ (2021) Accelerating unstructured-grid CFD algorithms on NVIDIA and AMD GPUs, In: 2021 IEEE/ACM 11th Workshop on Irregular Applications: Architectures and Algorithms (IA3), pp 19–26, https://doi.org/10.1109/IA354616.2021.00010

  18. Walden A, Nielsen E, Diskin B, Zubair M (2019) A mixed precision multicolor point-implicit solver for unstructured grids on GPUs, In: 2021 IEEE/ACM 11th Workshop on Irregular Applications: Architectures and Algorithms (IA3), pp 22–30, https://doi.org/10.1109/IA354616.2021.00010

  19. Ha S, Park J, You D (2021) A multi-GPU method for ADI-based fractional-step integration of incompressible Navier–Stokes equations. Comput Phys Commun 265:107999. https://doi.org/10.1016/j.cpc.2021.107999

    Article  MathSciNet  Google Scholar 

  20. Zhu X, Phillips E, Spandan V, Donners J, Ruetsch G, Romero J, Ostilla-Mónico R, Yang Y, Lohse D, Verzicco R, Fatica M (2018) AFiD-GPU: a versatile Navier-Stokes solver for wall-bounded turbulent flows on GPU clusters. Comput Phys Commun 229:199–210. https://doi.org/10.1016/j.cpc.2018.03.026

    Article  Google Scholar 

  21. Ha S, Park J, You D (2018) A GPU-accelerated semi-implicit fractional-step method for numerical solutions of incompressible Navier-Stokes equations. J Comput Phys 352:246–64. https://doi.org/10.1016/j.jcp.2017.09.055

    Article  MathSciNet  Google Scholar 

  22. Wang YX, Zhang LL, Liu W, Cheng XH, Zhuang Y, Chronopoulos AT (2018) Performance optimizations for scalable CFD applications on hybrid CPU+ MIC heterogeneous computing system with millions of cores. Comput Fluids 173:226–236. https://doi.org/10.1016/j.compfluid.2018.03.005

    Article  MathSciNet  MATH  Google Scholar 

  23. Che Y, Xu C, Wang Z (2020) Load balancing a multi-block grids-based application on heterogeneous platform, In: 2020 IEEE 23rd International Conference on Computational Science and Engineering (CSE), pp 44–49, https://doi.org/10.1109/CSE50738.2020.00014

  24. Nguyen MT, Castonguay P, Laurendeau E (2019) GPU parallelization of multigrid RANS solver for three-dimensional aerodynamic simulations on multiblock grids. J Supercomput 75(5):2562–2583. https://doi.org/10.1007/s11227-018-2653-6

    Article  Google Scholar 

  25. Jude D, Baeder JD (2016) Extending a three-dimensional GPU RANS solver for unsteady grid motion and free-wake coupling, In: 54th AIAA Aerospace Sciences Meeting, p 1811, https://doi.org/10.2514/6.2016-1811

  26. Mishra A, Jude D, Baeder JD (2018) A gpu accelerated adjoint solver for shape optimization, In: 2018 Fluid Dynamics Conference, p 3557. https://doi.org/10.2514/6.2018-3557

  27. Mostafazadeh Davani B, Marti F, Pourghassemi B, Liu F, Chandramowlishwaran A (2017) Unsteady Navier–Stokes computations on GPU architectures, In: 23rd AIAA Computational Fluid Dynamics Conference, p 4508, https://doi.org/10.2514/6.2017-4508

  28. Monfaredi M, Trompoukis X, Tsiakas K, Giannakoglou K (2021) Unsteady continuous adjoint to URANS coupled with FW-H analogy for aeroacoustic shape optimization. Comput Fluids 230:105136. https://doi.org/10.1016/j.compfluid.2021.105136

    Article  MathSciNet  MATH  Google Scholar 

  29. Mostafazadeh Davani B, Marti F, Pourghassemi B, Liu F, Chandramowlishwaran A (2017) Unsteady Navier–Stokes computations on GPU architectures. In: 23rd AIAA Computational Fluid Dynamics Conference 2017, p 4508, https://doi.org/10.2514/6.2017-4508

  30. Savin GI, Benderskiy LA, Lyubimov DA, Rybakov AA (2019) RANS/ILES method optimization for effective calculations on supercomuter. Lobachevskii J Math 40(5):566–73. https://doi.org/10.1134/S1995080219050172

    Article  MathSciNet  MATH  Google Scholar 

  31. Wang Y, He X, Yang S, Tan G (2020) Towards a heterogeneous architecture solver for the incompressible Navier–Stokes equations. CCF Trans High Perf Comput 2(2):123–34. https://doi.org/10.1007/s42514-020-00034-9

    Article  Google Scholar 

  32. Lei Y, Zhang X, Han L, Dong X, Li J (2019) MIC-THPCM: MIC-based heterogeneous parallel optimization for axial compressor rotor, In: IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp 646–653, https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00098

  33. Liu Z, Chu X, Lv X, Meng H, Shi S, Han W, Xu J, Fu H, Yang G (2019) SunwayLB: enabling extreme-scale lattice Boltzmann method based computing fluid dynamics simulations on Sunway TaihuLight, In: 2019 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2019, pp 557–566. https://doi.org/10.1109/IPDPS.2019.00065

  34. Lin J, Wen M, Meng D, Liu X, Nukada A, Matsuoka S (2018) Optimizing preconditioned conjugate gradient on TaihuLight for OpenFOAM, In: 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018, pp 273–282, https://doi.org/10.1109/CCGRID.2018.00042

  35. Li M, Liu Y, Yang H, Luan Z, Gan L, Yang G, Qian D (2020) Accelerating sparse Cholesky factorization on Sunway manycore architecture. IEEE Trans Parallel Distrib Syst 31(7):1636–1650. https://doi.org/10.1109/TPDS.2019.2953852

    Article  Google Scholar 

  36. Liu X, Lu Z, Yuan W, Ma W, Zhang J (2020) Massively parallel CFD simulation software: CCFD development and optimization based on Sunway TaihuLight. Sci Program 8847481(1–8847481):17. https://doi.org/10.1155/2020/8847481

    Article  Google Scholar 

  37. Ao Y, Yang C, Liu F, Yin W, Jiang L, Sun Q (2018) Performance optimization of the HPCG benchmark on the Sunway TaihuLight supercomputer. ACM Trans Arch Code Optim 15(1):11:1-11:20. https://doi.org/10.1145/3182177

    Article  Google Scholar 

  38. Liu F, Ji S (1996) Unsteady flow calculations with a multigrid Navier–Stokes method. AIAA J 34:2047–2053. https://doi.org/10.2514/3.13351

    Article  MATH  Google Scholar 

  39. Spalart P, Allmaras S (1992) A one-equation turbulence model for aerodynamic flows. La Recherche Aérospatiale 1(1):5–21. https://doi.org/10.2514/6.1992-439

    Article  Google Scholar 

  40. Zhou B, Huang Y, Xu J, Guo S, Qi H (2019) Memory latency optimizations for the elementary functions on the Sunway architecture. J Supercomput 75(7):3917–3944. https://doi.org/10.1007/s11227-018-02741-1

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant number 2016YFB0200902.

Author information

Authors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, 710049, China

    Heng Chen, Ziheng Wang, Xi Xiao, Jingbo Li, Xiaoshe Dong & Xingjun Zhang

Authors
  1. Heng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xi Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingbo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoshe Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xingjun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoshe Dong.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Wang, Z., Xiao, X. et al. SunwayURANS: 3D full-annulus URANS simulations of transonic axial compressors on Sunway TaihuLight. J Supercomput 78, 19167–19187 (2022). https://doi.org/10.1007/s11227-022-04628-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04628-8

Keywords

Search

Navigation