
1. 中国空气动力研究与发展中心 计算空气动力研究所,绵阳,621000
2. 四川天府流体大数据研究中心,成都,610299
Published:2026
移动端阅览
赵钟, 何先耀, 何磊, 等. 国家数值风洞风雷软件研发与开源应用进展[J]. 空气动力学学报, 2026, 44(3): 59−96.
Zhao Zhong, He Xianyao, He Lei, et al. Development and open source application progress of NNW-PHengLEI[J]. 2026, 44(3): 59-96.
赵钟, 何先耀, 何磊, 等. 国家数值风洞风雷软件研发与开源应用进展[J]. 空气动力学学报, 2026, 44(3): 59−96. DOI: 10.7638/kqdlxxb-2025.0270
Zhao Zhong, He Xianyao, He Lei, et al. Development and open source application progress of NNW-PHengLEI[J]. 2026, 44(3): 59-96. DOI: 10.7638/kqdlxxb-2025.0270.
计算流体力学(computational fluid dynamics
CFD)软件作为工业软件的重要组成部分,分为商业软件、内部自用软件、开源软件等类别,其面向需求、应用场景和功能特点各有不同。风雷软件(PHengLEI)作为国家数值风洞(NNW)套装软件之一,2020年面向全国开源,旨在构建自主CFD基础设施与生态。本文系统综述其近期研发进展、技术特色和典型应用范例。首先阐述开源对发展自主流体工业软件的意义,以及国内外知名开源CFD软件发展现状;然后回顾发展历程和最近技术进展,主要介绍高可扩展框架设计、高效大规模并行计算等特色技术,以及面向民用领域需求的网格生成、流场求解、风能应用等方面的技术进展;其次,简要统计软件开源应用情况,并结合3个典型范例阐述开源应用模式;最后,分析技术不足与改进方向,结合工业软件领域最近提出的开源实践范式,倡议通过开源社区协同创新,完善技术体系与工程化能力,推动我国自主流体工业软件生态持续发展。
Computational fluid dynamics (CFD) software constitutes a vital part of industrial software
and can be categorized into types such as commercial
in-house code
open-source software
each serving distinct requirements
application scenarios
and functional characteristics. As one of the foundational computational platforms of China’s National Numerical Wind-tunnel (NNW) project
PHengLEI was open-sourced nationwide in 2020
with the goal of building independent CFD infrastructure and a supporting ecosystem. This paper provides a systematic review of its latest RD progress
technical features
and representative application cases. It first discusses the significance of open source for developing indigenous fluid dynamics industrial software and surveys the current landscape of well-known open-source CFD software both domestically and internationally. The development history and latest technical advances of PHengLEI are then outlined
with focus on its highly extensible framework design
efficient large-scale parallel computing capabilities
and recent progress in mesh generation
flow solving
and wind-energy applications for civilian needs. Furthermore
the application status since its open-source release is briefly summarized
illustrated through three representative cases that showcase its practical implementation patterns. Finally
existing technical shortcomings and directions for improvement are analyzed. In light of emerging open-source practice paradigms in the industrial software field
we advocate for collaborative innovation through open-source communities to enhance the technical system and engineering capabilities
thereby fostering the sustainable development of China’s independent fluid dynamics industrial software ecosystem.
Chen Jianqiang. Advances in the key technologies of Chinese national numerical windtunnel project[J]. Scientia Sinica Technologica, 2021, 51(11): 1326−1347(in Chinese). doi: 10.1360/SST-2020-0334
Zhao Zhong, He Lei, He Xianyao. Design of general CFD software PHengLEI[J]. Computer Engineering and Science, 2020, 42(2): 210−219(in Chinese). doi: 10.3969/j.issn.1007-130X.2020.02.004
Zhao Zhong, Zhang Laiping, He Lei, et al. PHengLEI: A large scale parallel CFD framework for arbitrary grids[J]. Chinese Journal of Computers, 2019, 42(11): 2368−2383(in Chinese). doi: 10.11897/SP.J.1016.2019.02368
Li Runze, Zhang Yufei, Chen Haixin. Reinforcement learning method for supercritical airfoil aerodynamic design[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 523810(in Chinese).
Li Runze, Zhang Yufei, Chen Haixin. Evolution and development of “man-in-loop” in aerodynamic optimization design[J]. Acta Aerodynamica Sinica, 2017, 35(4): 529−543(in Chinese). doi: 10.7638/kqdlxxb-2017.0076
Yan Chao. Achievements and predicaments of CFD in aeronautics in past forty years[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(10): 526490(in Chinese).
Qian Depei, Wang Rui. Key issues in exascale computing[J]. Scientia Sinica (Informationis), 2020, 50(9): 1303−1326(in Chinese).
White paper on the development of domestic independent industrial simulation software[R]. The 7th China Simulation Technology Application Conference, Ningbo, 2025 (in Chinese) .
Tang Bin, Xu Puyang, Li Baojun, et al. Research on the collaborative open ecology of industrial software production, teaching and research based on open source mode[J]. Software Guide, 2025, 24(6): 213−220(in Chinese). doi: 10.11907/rjdk.242024
Gao Liang, Li Peigen, Huang Pei, et al. Development strategies of industrial software for digital design[J]. Strategic Study of CAE, 2023, 25(2): 254−262(in Chinese). doi: 10.15302/J-SSCAE-2023.07.017
Li Hongman, Zhang Baolin, Chen Ping. Draw lessons from foreign experience and strive to improve the scale of China’s industrial software market[J]. STF Monthly, 2025(1): 57−61(in Chinese). doi: 10.3969/j.issn.2096-4935.2025.01.011
Gao Libing, Liu Maozhen, Liu Dongqing, et al. Strategies for open-source ecosystem development in China’s process manufacturing industrial software: a framework and policy recommendations[J]. Chemical Industry and Engineering Progress, 2025, 44(10): 5503−5514(in Chinese). doi: 10.16085/j.issn.1000-6613.2025-0297
Ricci A. Review of OpenFOAM applications in the computational wind engineering: from wind environment to wind structural engineering[J]. Meccanica, 2025, 60(6): 1695−1735. doi: 10.1007/s11012-024-01826-x
Zhang Jian, Zhou Naichun, Li Ming, et al. R D and design of industrial CFD software for aeronautics and astronautics[J]. Journal of Software, 2022, 33(5): 1529−1550(in Chinese). doi: 10.13328/j.cnki.jos.006547
Wang Zhiqiang, Wu Sheng, Xiao Guoqiang, et al. Study of open-source software license compliance[J]. Journal of Software, 2022, 33(8): 3035−3058(in Chinese). doi: 10.13328/j.cnki.jos.006374
Li Shu. The Internet extreme risk prevention and the game of great powers[J]. Journal of Tongji University (Social Science Section), 2022, 33(4): 48−57(in Chinese). doi: 10.3969/j.issn.1009-3060.2022.04.005
Li Dandan, Li Min. Research on the trends and pathways of open source empowering the innovative development of industrial software[J]. Modern Science, 2025(1): 69−79(in Chinese).
Xin Xiaohua, Zhao Rao, Fu Haiwei. Multi-dimensional efforts to promote open source construction and break through[J]. China Industry and Information Technology, 2025(6): 48−53(in Chinese). doi: 10.3969/j.issn.1674-9138.2025.06.009
Xin Xiaohua, Guo Xinzhu, Xu Zhixin, et al. Risk challenges and countermeasures for promoting the construction of open source supply chain security system of China[J]. Digital Transformation, 2025, 2(6): 63−68(in Chinese).
Scheufler H, Roenby J. TwoPhaseFlow: A framework for developing two phase flow solvers in OpenFOAM[J]. OpenFOAM® Journal, 2023, 3: 200−224. doi: 10.51560/ofj.v3.80
Galeazzo F C C, Garcia-Gasulla M, Boella E, et al. Performance comparison of CFD microbenchmarks on diverse HPC architectures[J]. Computers, 2024, 13(5): 115. doi: 10.3390/computers13050115
Huang L F, Li Y Z, Benites-Munoz D, et al. A review on the modelling of wave-structure interactions based on OpenFOAM[J]. OpenFOAM® Journal, 2022, 2: 116−142. doi: 10.51560/ofj.v2.65
Fahlbeck J, Nilsson H, Salehi S. A head loss pressure boundary condition for hydraulic systems[J]. OpenFOAM® Journal, 2022, 2: 1−12. doi: 10.51560/ofj.v2.69
Chourdakis G, Schneider D, Uekermann B. OpenFOAM-preCICE: Coupling OpenFOAM with external solvers for multi-physics simulations[J]. OpenFOAM® Journal, 2023, 3: 1−25. doi: 10.51560/ofj.v3.88
Maier W T, Needels J T, Garbacz C, et al. SU2-NEMO: an open-source framework for high-Mach nonequilibrium multi-species flows[J]. Aerospace, 2021, 8(7): 193. doi: 10.3390/aerospace8070193
Burghardt O, Gomes P, Kattmann T, et al. Discrete adjoint methodology for general multiphysics problems: a modular and efficient algorithmic outline with implementation in an open-source simulation software[J]. Structural and Multidisciplinary Optimization, 2022, 65: 28. doi: 10.1007/s00158-021-03117-5
Caccia F, Abergo L, Guardone A. Towards blade-resolved ice accretion simulations of flexible blades using mixed-fidelity models[J]. Journal of Physics: Conference Series, 2024, 2767(2): 022046. doi: 10.1088/1742-6596/2767/2/022046
Agarwal D, Marques S, Robinson T T. Aerodynamic shape optimisation using parametric CAD and discrete adjoint[J]. Aerospace, 2022, 9(12): 743. doi: 10.3390/aerospace9120743
Xu Z, Cambier L, Alonso J J, et al. Towards a scalable hierarchical high-order CFD solver[C]//Proc of the AIAA Scitech 2021 Forum, VIRTUAL EVENT. Reston, Virginia: AIAA, 2021: 0494. doi: 10.2514/6.2021-0494
Vitale S, Pini M, Colonna P. Multistage turbomachinery design using the discrete adjoint method within the open-source software SU2[J]. Journal of Propulsion and Power, 2020, 36(3): 465−478. doi: 10.2514/1.B37685
Rausa A, Guardone A, Auteri F. Implementation of γ - Reθ transition model within SU2: model validation and verification[C ] //Proc of the AIAA SCITECH 2023 Forum, National Harbor, MD Online. Reston, Virginia: AIAA, 2023: 1570. doi: 10.2514/6.2023-1570
Abergo L. Aeroacustics computation based on harmonic balance solution[J]. Materials Research Proceedings, 2023, 37: 10−16. doi: 10.21741/9781644902813-3
Park M A, Laflin K R, Chaffin M S, et al. CFL3D, FUN3D, and NSU3D contributions to the fifth drag prediction workshop[J]. Journal of Aircraft, 2014, 51(4): 1268−1283. doi: 10.2514/1.C032613
Sclafani A J, DeHaan M A, Vassberg J C, et al. Drag prediction for the common research model using CFL3D and OVERFLOW[J]. Journal of Aircraft, 2014, 51(4): 1101−1117. doi: 10.2514/1.C032571
Liu Y W, Yong X S, Tang Y M. Evaluation of various numerical methods for blade row interaction in turbomachinery[C]//Proc of the Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, Rotterdam, Netherlands. American Society of Mechanical Engineers, 2022: V10CT32A048. doi: 10.1115/gt2022-84388
Yong X S, Liu Y W, Tang Y M. Development of unsteady reduced-order methods for multi-row turbomachinery flow simulation based on the computational fluids laboratory three-dimensional solver[J]. Physics of Fluids, 2024, 36(4): 045135. doi: 10.1063/5.0197247
Shoev G V, Shershnev A A, Polevshchikov D P, et al. Implementation of the spalart–allmaras turbulence model in a finite-volume gas-dynamic solver[J]. Journal of Applied Mechanics and Technical Physics, 2025, 66(1): 65−79. doi: 10.1134/S0021894425010146
Li Xinliang, Fu Dexun, Ma Yanwen, et al. Development of high accuracy CFD software hoam-OpenCFD[J]. Scientific Research Informatization Technology and Application, 2010(1): 53−59(in Chinese). doi: CNKI:SUN:KYXH.0.2010-01-008
Dang G L, Liu S W, Guo T B, et al. Direct numerical simulation of compressible turbulence accelerated by graphics processing unit: an open-access database of high-resolution direct numerical simulation[J]. AIP Advances, 2022, 12(12): 125111. doi: 10.1063/5.0132489
Qi H, Li X L, Yu C P, et al. Direct numerical simulation of hypersonic boundary layer transition over a lifting-body model HyTRV[J]. Advances in Aerodynamics, 2021, 3(1): 31. doi: 10.1186/s42774-021-00082-x
Yan Z, Fu Y W, Wang L F, et al. Effect of chemical reaction on mixing transition and turbulent statistics of cylindrical Richtmyer–Meshkov instability[J]. Journal of Fluid Mechanics, 2022, 941: A55. doi: 10.1017/jfm.2022.329
Dang G L, Liu S W, Guo T B, et al. Direct numerical simulation of compressible turbulence accelerated by graphics processing unit: an open-source high accuracy accelerated computational fluid dynamic software[J]. Physics of Fluids, 2022, 34(12): 126106. doi: 10.1063/5.0127684
Gao Xiaocheng, Mao Meiliang, Chen Jianqiang, et al. Application of CHANT1.0 in complex configurations and complex flows[C]//The 14th National Conference on Computational Fluid Dynamics, Guiyang, 2009 (in Chinese).
Wang Yuntao, Zhang Yulun, Hong Junwu, et al. Development and application of TRIP2.0_SOLVER[J]. Acta Aerodynamica Sinica, 2007, 25(2): 163−168,188(in Chinese). doi: 10.3969/j.issn.0258-1825.2007.02.005
Dong Weizhong. Numerical calculation and analysis of the influence of thermochemical non-equilibrium effect on hypersonic flow[D]. Beijing: Beihang University, 1996 (in Chinese).
Tang Wei, Gui Yewei. Brief introduction of thermal protection and thermal structure performance calculation software system HEATips[C]//The 14th National Conference on Computational Fluid Dynamics, Guiyang, 2009 (in Chinese).
Jain R, Biedron R T, Jones W, et al. Modularization and Validation of nasa fun3d as a hpcmp create-av Helios Near-body Solver[C]//Proc of the 54th AIAA Aerospace Sciences Meeting, San Diego, California, USA. Reston, Virginia: AIAA, 2016: 1298. doi: 10.2514/6.2016-1298
He Xin, Zhang Laiping, Zhao Zhong, et al. Research of general large scale CFD software architecture and data structure[J]. Acta Aerodynamica Sinica, 2012, 30(5): 557−565(in Chinese).
He Lei, He Xin, Ma Rong, et al. Preliminary design and application of CFD software automatic testing platform[J]. Acta Aerodynamica Sinica, 2016, 34(4): 418−425(in Chinese). doi: 10.7638/kqdlxxb-2014.0102
Cai Zhiming, Lu Chuanfu, Li Lixia, et al. Proficient in Qt4 programming[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2011(in Chinese).
Yang Fuqing. Design and implementation of component-based software[M]. Beijing: Tsinghua University Press, 2008(in Chinese).
Morton S A, Meakin R E. HPCMP CREATE TM -AV kestrel architecture, capabilities, and long term plan for fixed-wing aircraft simulations[C ] //Proc of the 54th AIAA Aerospace Sciences Meeting, San Diego, California, USA. Reston, Virginia: AIAA, 2016: 0565. doi: 10.2514/6.2016-0565
Bai Hanli, Chen Xiaomeng, Pu Qiao. Development of integration framework system of National Numerical Windtunnel project[J]. Acta Aerodynamica Sinica, 2020, 38(6): 1149−1157(in Chinese). doi: 10.7638/kqdlxxb-2020.0120
Economon T D, Palacios F, Copeland S R, et al. SU2: an open-source suite for multiphysics simulation and design[J]. AIAA Journal, 2015, 54(3): 828−846. doi: 10.2514/1.J053813
Xie Bing. Asset and process management for software reuse[M]. Beijing: Tsinghua University Press, 2008 (in Chinese).
Zhao Z, Ma R, He L, et al. An efficient large-scale mesh deformation method based on MPI/OpenMP hybrid parallel radial basis function interpolation[J]. Chinese Journal of Aeronautics, 2020, 33(5): 1392−1404. doi: 10.1016/j.cja.2019.12.025
Zhao Z, Zhang Y, He L, et al. A large-scale parallel hybrid grid generation technique for realistic complex geometry[J]. International Journal for Numerical Methods in Fluids, 2020, 92(10): 1235−1255. doi: 10.1002/fld.4825
Yu F Y, Wan Z H, Hu Y S, et al. Effects of the wavy leading-edge wavelength on the reduction in the noise of the rod–airfoil configuration[J]. Justc, 2025, 55(2): 202. doi: 10.52396/justc-2024-0053
Yu F Y, Wan Z H, Hu Y S, et al. Effects of leading-edge serration shape on noise reduction in rod-airfoil interactions[J]. The Journal of the Acoustical Society of America, 2025, 157(1): 215−233. doi: 10.1121/10.0034845
Lei Yinghaonan, Lei Sanhui, Zhang Shenghao, et al. Numerical study on compressible gas-solid two-phase flow based on a particle solver implemented in NNW-PHengLEI[J]. Acta Aerodynamica Sinica, 2023, 41(12): 37−47(in Chinese). doi: 10.7638/kqdlxxb-2022.0161
Luo M Q, Lu S, Zhu Q Y. A two-phase density-based solver for simulating wet steam flows with non-equilibrium condensation. I. Approach and verification[J]. AIP Advances, 2023, 13(6): 065012. doi: 10.1063/5.0152134
Luo M Q, Lu S, Zhu Q Y. A two-phase density-based solver for simulating wet steam flows with non-equilibrium condensation. II. Effects of 3D surface topography through nozzles[J]. AIP Advances, 2023, 13(6): 065201. doi: 10.1063/5.0152145
Li Peng, Chen Jianqiang, Ding Mingsong, et al. Framework design of NNW-HyFLOW hypersonic flow simulation software[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(9): 625718(in Chinese). doi: 10.7527/S1000-6893.2021.25718
Chen Jianqiang, Ma Yankai, Min Yaobing, et al. Design and development of homogeneous hybrid solvers on National Numerical Windtunnel (NNW)PHengLEI[J]. Acta Aerodynamica Sinica, 2020, 38(6): 1102−1110(in Chinese). doi: 10.7638/kqdlxxb-2020.0177
Kroll N, Fassbender J K. MEGAFLOW - Numerical Flow Simulation for Aircraft Design[M]. Berlin Heidelberg: Springer, 2005. doi: 10.1007/3-540-32382-1
Puigt G, Gazaix M, Montagnac M, et al. Development of a new hybrid compressible solver inside the CFD elsA software[C]//Proc of the 20th AIAA Computational Fluid Dynamics Conference, Honolulu, Hawaii. Reston, Virginia: AIAA, 2011: 3379. doi: 10.2514/6.2011-3379
Couaillier V. elsa-Hybrid: an all in one structured/unstructured solver for the simulation of internal and external flows[J]. Progress in Propulsion Physics, 2013, 4: 417−444. doi: 10.1051/eucass/201304417
He X, Zhao Z, Zhang L P. The Research and Development of Structured-Unstructured Hybrid CFD Software[J]. Transactions of Nanjing University of Aeronautics Astronautics, 2013, 30(sup): 116−126.
Du Yiming, Ma Tong, Qiu Fusheng, et al. Homogeneous computational performance of the structured/unstructured hybrid grid in NNW-PHengLEI[J]. Aerodynamic Research Experiment, 2024, 2(5): 22−39(in Chinese). doi: 10.20118/j.issn2097-258X.2024.05.003
Qiu H C, Yang Y G, Sun L B. Transverse jet interaction with hypersonic crossflow I: experimental and numerical investigations[J]. Journal of Spacecraft and Rockets, 2024, 62(3): 697−705. doi: 10.2514/1.A35890
Luo Jiamao, You Jin, Jiao Si, et al. Numerical study on heat exchange performance for three-dimensional methane pre-cooler[J]. Journal of Aerospace Power, 2024, 39(8): 39−49(in Chinese). doi: 10.13224/j/j.cnki.j.jasp.20220603
Chourdakis G, Davis K, Rodenberg B, et al. preCICE v2: A sustainable and user-friendly coupling library[J]. Open Research Europe, 2022, 2: 51. doi: 10.12688/openreseurope.14445.2
Dong Tao, Chen Jianing, Yu Haohao, et al. Development and validation of fluent adapter in preCICE multi-physics coupling framework[J]. Industrial Technology Innovation, 2023, 10(6): 36−43(in Chinese). doi: 10.14103/j.issn.2095-8412.2023.12.005
Wei Shiming, Kao Jiawei, Jin Yan, et al. Numerical simulation method of fully fluid-solid coupled fracture propagation based on COMSOL secondary development[J]. Chinese Journal of Computational Mechanics, 2025, 42(6): 997−1004(in Chinese). doi: 10.7511/jslx20240808001
Ma Rong, Chang Xinghua, He Xin, et al. Loose and strong coupling methods for flow/kinematics coupled simulations and stability analysis[J]. Physics of Gases, 2016, 1(6): 36−49(in Chinese). doi: 10.19527/j.cnki.2096-1642.2016.06.004
Wan Y B, Zhao Z, Liu J, et al. Large-scale homo- and heterogeneous parallel paradigm design based on CFD application PHengLEI[J]. Concurrency and Computation: Practice and Experience, 2024, 36(5): e7933. doi: 10.1002/cpe.7933
Wan Y B, He L, Zhang Y, et al. An efficient communication strategy for massively parallel computation in CFD[J]. The Journal of Supercomputing, 2023, 79(7): 7560−7583. doi: 10.1007/s11227-022-04940-3
Zhang Yong, Zhang Xi, Wan Yunbo, et al. Optimizations of mesh renumbering for unstructured finite-volume computational fluid dynamics[J]. Computer Engineering Science, 2022, 44(10): 1721−1729(in Chinese). doi: 10.11887/j.cn.202210012
Zhang Xi, Sun Xu, Guo Xiaohu, et al. Optimizations of graph coloring method for unstructured finite volume computational fluid dynamics on GPU[J]. Journal of National University of Defense Technology, 2022, 44(5): 24−34(in Chinese). doi: 10.11887/j.cn.202205003
Zhang J M, Wu T, Yang H, et al. Parallel acceleration improvement of physical quantity gradient algorithm based on CFD[C]//Artificial Intelligence and Security. Singapore: Springer, 2020: 165-174. doi: 10.1007/978-981-15-8083-3_15
Zhao Zhong, He Lei, Zhang Jian, et al. MPI/OpenMP hybrid parallel computation of wall distance for turbulence flow simulations[J]. Acta Aerodynamica Sinica, 2019, 37(6): 883−892(in Chinese). doi: 10.7638/kqdlxxbG2017.0199
Guo Yongyan, Zeng Zhichun, He Lei, et al. Numerical simulation of DLR-F11 high-lift configuration[J]. Chinese Journal of Computational Physics, 2023, 40(4): 401−415(in Chinese). doi: 10.19596/j.cnki.1001-246x.8612
Zeng Zhichun, He Lei, Guo Yongyan, et al. Numerical simulation of high-lift configuration from HiLiftPW-4[J]. Computer Simulation, 2025, 42(6): 490−500(in Chinese). doi: 10.3969/j.issn.1006-9348.2025.06.082
Zhao Zhong, He Xin, Zhang Laiping, et al. Numerical research of NASA high-lift trap wing model based on HyperFLOW[J]. Acta Aerodynamica Sinica, 2015, 33(5): 594−602(in Chinese). doi: 10.7638/kqdlxxb-2014.0026
Zhang Miao, Liu Tiejun, Ma Tuliang, et al. High speed aerodynamic design of large civil transporter based on CFD method[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(1): 244−254(in Chinese). doi: 10.7527/S1000-6893.2015.0331
He X, Zhao Z, Ma R, et al. Validation of HyperFLOW in subsonic and transonic flows[J]. Acta Aerodynamica Sinica, 2016, 34(02): 267−275. doi: 10.7638/kqdlxxb-2016.0002
Wang Nianhua, Chang Xinghua, Zhao Zhong, et al. Aerodynamic performance prediction and credibility study of the transport model CHN-T1 based on HyperFLOW solver[J]. Acta Aerodynamica Sinica, 2019, 37(2): 301−309(in Chinese). doi: 10.7638/kqdlxxb-2018.0183
Ma Tong. Development of a new KDO turbulence model based on nnw-phenglei software and its application in transonic flow[D]. Shenyang: Shenyang Aerospace University, 2024 (in Chinese).
Lou Keqiang, Wang Xiaodong, Yu Jiaxin, et al. Numerical study on longitudinal static stability of large aircraft under uncertainty[J]. Journal of Engineering Thermophysics, 2023, 44(6): 1572−1582(in Chinese).
Du Yiming, Chen Zuchang, Qiu Fusheng, et al. Distributed rough surface boundary layer flow simulation using NNW-PHengLEI software[J]. Journal of Shenyang Aerospace Ace University, 2024, 41(4): 11−24(in Chinese). doi: 10.3969/j.issn.2095-1248.2024.04.002
Chen Zuchang. Distributed rough-surface boundary-layer flow simulation using NNW-PHengLEI software[D]. Shenyang: Shenyang Aerospace University, 2024 (in Chinese).
Deng X G, Zhang H X. Developing high-order weighted compact nonlinear schemes[J]. Journal of Computational Physics, 2000, 165(1): 22−44. doi: 10.1006/jcph.2000.6594
Wu Wenchang, Ma Yankai, Han Xingsi, et al. Smooth TENO nonlinear weighting for WCNS scheme[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(8): 129052(in Chinese). doi: 10.7527/S1000-6893.2023.29052
Wu Wenchang, Han Shengsi, Min Yaobing, et al. Sixth-order low-dissipation WCNS-CU6-ST scheme based on smooth TENO nonlinear weight[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(S1): 199−210(in Chinese). doi: 10.7527/S1000-6893.2024.30598
He Y W, Shi A M. Transition of shock reflection with downstream expansion fan interference[J]. Journal of Fluid Mechanics, 2025, 1007: A5. doi: 10.1017/jfm.2025.41
Shi A M, Dowell E H. A benchmark method for predicting oblique shock structure[C]// 32nd Congress of the International Council of the Aeronautical Sciences, 2021.
Qin Z X, Shi A M, Dowell E H, et al. Analytical model of strong Mach reflection[J]. AIAA Journal, 2022, 60(9): 5187−5202. doi: 10.2514/1.j061701
Weng Y H, Li Q, Tan G Z, et al. Numerical investigations on interactions between 2D/3D conical shock wave and axisymmetric boundary layer at Ma = 2.2[J ] . Aerospace Science and Technology, 2024, 144: 108769. doi: 10.1016/j.ast.2023.108769
Li Q, Yan P, Huang X, et al. On developing piecewise rational mapping with fine regulation capability for WENO schemes[J]. Journal of Scientific Computing, 2021, 88(3): 75. doi: 10.1007/s10915-021-01559-z
Wu W, Xie N, Min Y, et al. Self-adaptive turbulence eddy simulation for complex flows on a high-order finite differencing PHengLEI-HyOrder solver[J]. The Aeronautical Journal, 2025, 129(1337): 2036−2054. doi: 10.1017/aer.2025.17
Wu W C, Han X S, Min Y B, et al. Weighted compact nonlinear scheme based on smooth scale separation for self-adaptive turbulence eddy simulations[J]. Physics of Fluids, 2024, 36(7): 076103. doi: 10.1063/5.0212847
Wu W C, Han X S, Min Y B, et al. Improved self-adaptive turbulence eddy simulation for complex flows and stall prediction using high-order schemes[J]. European Journal of Mechanics - B/Fluids, 2024, 106: 48−64. doi: 10.1016/j.euromechflu.2024.03.003
Wu W C, Min Y B, Han X S, et al. Self-adaptive turbulence eddy simulation with a high-order finite differencing method for high Reynolds number complex flows[J]. Aerospace Science and Technology, 2023, 141: 108562. doi: 10.1016/j.ast.2023.108562
Zhang Zipei, Zhao Zhong, Chen Jianqiang, et al. Development and verification of LES model in NNW-PHengLEI[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(6): 127171(in Chinese). doi: 10.7527/S1000-6893.2022.27171
Zhang W W, Shan X L, Liu Y L, et al. High-Reynolds-number turbulence database: AeroFlowData[J]. Scientific Data, 2025, 12: 1500. doi: 10.1038/s41597-025-05846-4
Zhang Biao, Li Chen, Sun Dong, et al. Improved sst turbulence model for supersonic shock wave-boundary layer interactions[J]. Chinese Journal of Theoretical and Applied Mechanics, 2025, 57(11): 2722−2732(in Chinese). doi: 10.6052/0459-1879-25-140
Zhu L Y , Sun D, Guo Q L, et al. Structure parameter modification of stress limiter in k– ω shear stress turbulence model for high-speed flows with shock-wave/turbulent boundary layer interaction[J ] . Physics of Fluids, 2025, 37: 015212. doi: 10.1063/5.0250145
Jiang H J, Liu Z J, Yan C. Algebraic local correlation-based transition model for wide-speed-range flows considering crossflow effects[J]. Aerospace Science and Technology, 2025, 167: 110692. doi: 10.1016/j.ast.2025.110692
Liu Z J, Huang H X, Tan H J. One-equation amplification-factor-transport transition model for high-speed flows[J]. AIAA Journal, 2024, 63(1): 42−54. doi: 10.2514/1.J064267
Huang H X, Liu Z J, Tan H J. Local correlation-based transition model for aerodynamic flows in wide speed range[J]. Engineering Applications of Computational Fluid Mechanics, 2025, 19(1): 2442738. doi: 10.1080/19942060.2024.2442738
Frey P J, George P L. Mesh Generation: Application to Finite Elements[M]. Wiley, 2008. doi: 10.1002/9780470611166
Zhao Ning, Liu Jianming, Tian Linlin, et al. Progress and prospects of Cartesian mesh simulation methods for compressible flow problems[J]. Chinese Journal of Theoretical and Applied Mechanics, 2025, 57(2): 285−314(in Chinese).
Chen Jie, Wang Jiasen, Lu Junyu, et al. Research progress on automated CFD technology for viscous flow[J]. Aerospace Technology, 2024(2): 39−53(in Chinese). doi: 10.16338/j.issn.2097-0714.20230370
Zhao Zhong, Zhang Laiping, He Xin. Hybrid grid generation technique for complex geometries based on agglomeration of anisotropic tetrahedrons[J]. Acta Aerodynamica Sinica, 2013, 31(1): 34−39,68(in Chinese).
Zhang Laiping, He Xin, Chang Xinghua, et al. Recent progress of static and dynamic hybrid grid generation techniques over complex geometries[J]. Physics of Gases, 2016, 1(1): 42−61(in Chinese). doi: 10.19527/j.cnki.2096-1642.2016.01.008
Zhang Laiping. Mesh generation techniques in computational fluid dynamics[M]. Beijing: Science Press, 2017(in Chinese).
Weiss J, Smith W. Preconditioning applied to variable and constant density time-accurate flows on unstructured meshes[C]//Proc of the Fluid Dynamics Conference, Colorado Springs, CO. Reston, Virginia: AIAA, 1994: 2209. doi: 10.2514/6.1994-2209
Patankar S V, Spalding D B. A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows[J]. International Journal of Heat and Mass Transfer, 1972, 15(10): 1787−1806. doi: 10.1016/0017-9310(72)90054-3
Van Doormaal J P, Raithby G D. Enhancements of the simple method for predicting incompressible fluid flows[J]. Numerical Heat Transfer, 1984, 7(2): 147−163. doi: 10.1080/01495728408961817
Patankar S V. A calculation procedure for two-dimensional elliptic situations[J]. Numerical Heat Transfer, 1981, 4(4): 409−425. doi: 10.1080/01495728108961801
Issa R I. Solution of the implicitly discretised fluid flow equations by operator-splitting[J]. Journal of Computational Physics, 1986, 62(1): 40−65. doi: 10.1016/0021-9991(86)90099-9
Zhao Zhong, Hui Zenghong, Zhang Yong, et al. Design and application of Northwestern Polytechnical University NF-3 digital wind tunnel for wind turbine airfoils[J]. Acta Energiae Solaris Sinica, 2025 (in Chinese) .
Xiang Jinwu. Preface of “special issue: low-altitude economy”[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 2(in Chinese).
Wang Juntong, Bao Danwen, Zhou Jiayi, et al. Low-altitude airspace planning: a review and prospect[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 75−100(in Chinese). doi: 10.7527/S1000-6893.2024.30879
Li H, Yan Z F, Dai X M, et al. Numerical simulation of spatial wind fields in Xumishan Grottoes over complex terrain[J]. npj Heritage Science, 2025, 13: 69. doi: 10.1038/s40494-025-01643-9
Wang Tongguang, Tian Linlin, Zhong Wei, et al. Aerodynamic research progress in wind energyⅠ: Wind turbine aerodynamic characteristics[J]. Acta Aerodynamica Sinica, 2022, 40(4): 1−21(in Chinese). doi: 10.7638/kqdlxxb-2021.0114
Wang Tongguang, Tian Linlin, Zhong Wei, et al. Aerodynamic research progress in wind energy Ⅱ: Inflow and wake characteristics[J]. Acta Aerodynamica Sinica, 2022, 40(4): 22−50(in Chinese). doi: 10.7638/kqdlxxb-2021.0390
Qian Xiaohang, Gao Zhiteng, Wang Tongguang, et al. Nonlinear aeroelastic response analysis of 100-meter-scale flexible wind turbine blades[J]. Acta Aerodynamica Sinica, 2022, 40(4): 220−230(in Chinese). doi: 10.7638/kqdlxxb-2021.0321
Zhou Yonghao, Wang Yuchen, Zhang Xinqi, et al. Hydrogen safety challenges and AI-enabled safety technology development[J]. Chemical Industry and Engineering, 2025, 42(6): 2−17(in Chinese). doi: 10.13353/j.issn.1004.9533.20252044
Zeng Tao, Wang Zhaoli, Xiong Huajie, et al. Research on a fast Lagrangian algorithm for water droplet collection efficiency in complex configurations[J]. Journal of Aerospace Power, 2025, 40(7): 20220539(in Chinese). doi: 10.13224/j.cnki.jasp.20220539
Zeng Tao. Research on numerical simulation algorithm of icing for complex configuration[D]. Chengdu: Sichuan University, 2022(in Chinese).
Ren Jinghao, Wang Qiang, Li Weihao, et al. A prediction algorithm of collection efficiency based on gradient descent method[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(4): 126381(in Chinese). doi: 10.7527/S1000-6893.2021.26381
Ren Jinghao, Wang Qiang, Liu Yu, et al. Numerical simulation of droplet impingement characteristics on a high-lift configuration of a large commercial transport aircraft[J]. Acta Aerodynamica Sinica, 2021, 39(1): 52−58,72(in Chinese). doi: 10.7638/kqdlxxb-2020.0082
Ren Jinghao, Yi Xian, Wang Qiang, et al. Lagrangian simulation method of droplet collection efficiency for complex configuration[J]. Journal of Aerospace Power, 2020, 35(12): 2553−2561(in Chinese). doi: 10.13224/j.cnki.jasp.2020.12.009
Wang Zhaoli, Zeng Tao, Zhou Zhihong, et al. Application of TVD scheme in numerical simulation of water droplet field[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(12): 627010(in Chinese). doi: 10.7527/S1000-6893.2022.27010
Xiong H J, Wang Z L, Xu K R, et al. Treatment for the singularity problem in the phase segregation[J]. AIAA Journal, 2024, 62(12): 4701−4710. doi: 10.2514/1.J063904
Xiong H J, Wang N, Zeng T, et al. Adaptive momentum equation method for overcoming singularities of dispersed phases[J]. Physics of Fluids, 2024, 36(9): 093328. doi: 10.1063/5.0225332
Xiong Huajie, An Yijing, Wu Zhulong, et al. Extension of Eulerian wall film model in icing simulation[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S2): 729287(in Chinese).
Liu Zonghui, Bu Xueqin, Lin Guiping, et al. Simulation of unsteady electrothermal deicing process based on PHengLEI[J]. Acta Aerodynamica Sinica, 2023, 41(2): 53−63(in Chinese). doi: 10.7638/kqdlxxb-2021.0353
Liu Y L, Zhang W W, Xia Z H. A new data assimilation method of recovering turbulent mean flow field at high Reynolds numbers[J]. Aerospace Science and Technology, 2022, 126: 107328. doi: 10.1016/j.ast.2022.107328
Wang Z Y, Zhang W W. A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers[J]. Physics of Fluids, 2023, 35(2): 025124. doi: 10.1063/5.0136420
Shi Y X, Liu Y L, Zhang W W. A data assimilation method for recovering turbulent flows using heterogeneous experimental data[J]. Aerospace Science and Technology, 2025, 157: 109770. doi: 10.1016/j.ast.2024.109770
Zhu L Y, Zhang W W, Kou J Q, et al. Machine learning methods for turbulence modeling in subsonic flows around airfoils[J]. Physics of Fluids, 2019, 31: 015105. doi: 10.1063/1.5061693
Zhu L Y, Zhang W W, Sun X X, et al. Turbulence closure for high Reynolds number airfoil flows by deep neural networks[J]. Aerospace Science and Technology, 2021, 110: 106452. doi: 10.1016/j.ast.2020.106452
Zhu L Y, Zhang W W, Tu G H. Generalization enhancement of artificial neural network for turbulence closure by feature selection[J]. Advances in Aerodynamics, 2022, 4(1): 1. doi: 10.1186/s42774-021-00088-5
Sun X X, Cao W B, Liu Y L, et al. High Reynolds number airfoil turbulence modeling method based on machine learning technique[J]. Computers Fluids, 2022, 236: 105298. doi: 10.1016/j.compfluid.2021.105298
Shan X L, Liu Y L, Cao W B, et al. Turbulence modeling via data assimilation and machine learning for separated flows over airfoils[J ] . AIAA Journal, 2023, 61(9): 3883−3899. doi: 10.2514/1.J062711
Zhu L Y, Sun X X, Liu Y L, et al. One neural network approach for the surrogate turbulence model in transonic flows[J]. Acta Mechanica Sinica, 2022, 38(3): 321187. doi: 10.1007/s10409-021-09057-z
Sun X X, Liu Y L, Zhang W W, et al. Development and deployment of data-driven turbulence model for three-dimensional complex configurations[J]. Machine Learning: Science and Technology, 2024, 5(3): 035085. doi: 10.1088/2632-2153/ad7d60
Shan X L, Sun X X, Cao W B, et al. Modeling Reynolds stress anisotropy invariants via machine learning[J ] . Acta Mechanica Sinica, 2024, 40(6): 323629. doi: 10.1007/s10409-024-23629-x
Shan X L, Zhang W W. Improved quadratic constitu tive relation via turbulence anisotropy analysis and symbolic regression[J ] . Aerospace Science and Technology, 2025, 162: 110166. doi: 10.1016/j.ast.2025.110166
Shan X L, Zhang W W. Data-driven adverse pressure gradient correction for turbulence model[J]. AIAA Journal, 2025, 63(7): 2780−2796. doi: 10.2514/1.J064422
Yang Z X, Shan X L, Yang X I A, et al. Data-enabled discovery of specific and generalisable turbulence closures[J]. Journal of Fluid Mechanics, 2025, 1016: R1. doi: 10.1017/jfm.2025.10332
Liao H, Sun X X, Liu Y L, et al. Data-driven turbulence modeling: a mutually coupled framework for symbolic regression and data assimilation[J]. Physics of Fluids, 2025, 37(7): 075211. doi: 10.1063/5.0280752
Zuo K J, Ye Z Y, Bu S H, et al. Fast simulation of airfoil flow field via deep neural network[J ] . Aerospace Science and Technology, 2024, 150: 109207. doi: 10.1016/j.ast.2024.109207
Zuo K J, Ye Z Y, Yuan X X, et al. Flow3DNet: a deep learning framework for efficient simulation of three-dimensional wing flow fields[J]. Aerospace Science and Technology, 2025, 159: 109991. doi: 10.1016/j.ast.2025.109991
Liu Y L, Cao W B, Zhang W W, et al. Analysis on numerical stability and convergence of Reynolds averaged Navier–Stokes simulations from the perspective of coupling modes[J]. Physics of Fluids, 2022, 34: 015120. doi: 10.1063/5.0076273
Shan X L, Cao W B, Zhang W W. New interpretation for error propagation of data-driven Reynolds stress closures via global stability analysis[J ] . Physics of Fluids, 2025, 37(2): 025217. doi: 10.1063/5.0252753
Ning C J, Kou J Q, Zhang W W. An effective convergence accelerator of fluid simulations via generative diffusion probabilistic model[J ] . Aerospace Science and Technology, 2025, 158: 109917. doi: 10.1016/j.ast.2024.109917
Ning Chenjia, Wu Jifei, Li Guoshuai, et al. Research on few-shot aerodynamic modeling methods for civil aircraft model with wide-range high Reynolds number[J]. Acta Aerodynamica Sinica, 2024, 42(8): 60−76, 34 (in Chinese). doi: 10.7638/kqdlxxb-2023.0208
Zhao Shule, Zhang Weiwei. Machine learning of skin friction distribution based on surface inviscid flow feature[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(8): 2243−2258 (in Chinese). doi: 10.6052/0459-1879-23-615
Zhao S L, Zhang W W. Euler equation embedded machine learning method for wall pressure and skin friction distribution[J]. Engineering Applications of Computational Fluid Mechanics, 2025, 19(1): 2556450. doi: 10.1080/19942060.2025.2556450
Sun X X, Cao W B, Shan X L, et al. A generalized framework for integrating machine learning into computational fluid dynamics[J]. Journal of Computational Science, 2024, 82: 102404. doi: 10.1016/j.jocs.2024.102404
Zhao W, Yang X L, Wang J Y, et al. Evaluation of thermodynamic and chemical kinetic models for hypersonic and high-temperature flow simulation[J]. Applied Sciences, 2023, 13(17): 9991. doi: 10.3390/app13179991
Li Linfeng. Research of thermal environment prediction methods for the lifting vehicle based on data-driven[D]. Mianyang: Southwest University of Science and Technology, 2023 (in Chinese).
Geuzaine C, Remacle J F. Gmsh: a 3-D finite element mesh generator with built-in pre- and post-processing facilities[J]. International Journal for Numerical Methods in Engineering, 2009, 79(11): 1309−1331. doi: 10.1002/nme.2579
Zhang S, Pu H B, Wang B Q, et al. Implementation of a Newton−Krylov Algorithm in the Open-source Solver of PHengLEI[C]//Proceedings of Global Power and Propulsion Society (GPPS-TC-2023-0124). Global Power and Propulsion Society(GPPS). https://www.gpps.global
Yang Jincao. CFD computation acceleration based on random forest and graph convolutional networks[D]. Chengdu: University of Electronic Science and Technology of China, 2024 (in Chinese).
Yuan F, Yang X J, Li S G, et al. Optimizing multi-grid preconditioned conjugate gradient method on multi-cores[J]. IEEE Transactions on Parallel and Distributed Systems, 2024, 35(5): 768−779. doi: 10.1109/TPDS.2024.3372473
Yang X J, Li S G, Yuan F, et al. DBSR: an efficient storage format for vectorizing sparse triangular solvers on structured grids[C]//Proc of the Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, ACM, 2024: 1-14. doi: 10.1109/SC41406.2024.00065
Johnson F T, Tinoco E N, Yu N J. Thirty years of development and application of CFD at Boeing Commercial Airplanes, Seattle[J]. Computers Fluids, 2005, 34(10): 1115−1151. doi: 10.1016/j.compfluid.2004.06.005
Yang Chunhui, Liu Mengyue, Chen Ping, et al. Development blueprint of industrial software from the perspective of the 14th Five-Year Plan[J]. Software Guide, 2022, 21(10): 21−25(in Chinese). doi: 10.11907/rjdk.222263
Wang Huaimin, Yu Yue, Wang Tao, et al. Crowd intelligence paradigm: a new paradigm shift in software development[J]. Science in China (Information Sciences), 2023, 53(8): 1490−1502(in Chinese). doi: 10.1360/SSI-2023-0064
Wang Tao, Yin Gang, Yu Yue, et al. Crowd-intelligence-based software development method and practices[J]. Scientia Sinica (Informationis), 2020, 50(3): 318−334(in Chinese).
He Lei, Guo Yongyan, Zeng Zhichun, et al. Design and development of software automated continuous integration and testing platform for National Numerical Windtunnel project[J]. Acta Aerodynamica Sinica, 2020, 38(6): 1158−1164(in Chinese). doi: 10.7638/kqdlxxb-2020.0126
Yuan Xianxu, Chen Jianqiang, Du Yanxia, et al. Research progress on fundamental CFD issues in National Numerical Windtunnel Project[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(9): 625733(in Chinese). doi: 10.7527/S1000-6893.2021.25733
Zhao Zhong, Zhang Tianjiao, Xie Yufei, et al. Exploration of new engineering teaching model for computational fluid dynamics course[C]//The Aeronautics and Astronautics Teaching Steering Committee of the Ministry of Education. Proceedings of the 6th National Higher Education Aeronautics and Astronautics Education and Teaching Symposium. China Aerodynamics Research and Development Center Computational Aerodynamics Institute, 2024: 609−618 (in Chinese).
0
Views
0
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621