1 |
阎超. 航空CFD四十年的成就与困境[J]. 航空学报, 2022, 43(10): 526490.
|
|
YAN C. Achievements and predicaments of CFD in aeronautics in past forty years[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(10): 526490 (in Chinese).
|
2 |
MALIK M R, BUSHNELL D M. Role of computational fluid dynamics and wind tunnels in aeronautics R&D: NASA/TP-2012-217602[R]. Washington, D.C.: NASA, 2012.
|
3 |
HOLLAND J. Integrated field inversion and machine learning with embedded neural network training for turbulence modeling[D]. Maryland: University of Maryland, 2019.
|
4 |
朱文庆, 肖志祥, 符松. 使用IDDES方法预测飞行速度对喷流噪声的影响[J]. 空气动力学学报, 2018, 36(3): 463-469.
|
|
ZHU W Q, XIAO Z X, FU S. The effects of flight velocity on jet noise are simulated by improved delayed detached eddy simulation with the modification of grid scale definition[J]. Acta Aerodynamica Sinica, 2018, 36(3): 463-469 (in Chinese).
|
5 |
LOZANO-DURÁN A, BOSE S T, MOIN P. Performance of wall-modeled LES with boundary-layer-conforming grids for external aerodynamics[J]. AIAA Journal, 2021, 60(2): 747-766.
|
6 |
WILCOX D C. Turbulence modeling for CFD[M]. 3rd ed. La Cãnada: DCW Industries, Inc., 2006.
|
7 |
HEY T. The fourth paradigm-Data-intensive scientific discovery[M]∥KURBANOĞLU S, AL U, ERDOĞAN P L, et al. Communications in computer and information science. Berlin: Springer, 2012.
|
8 |
DURAISAMY K, SPALART P R, RUMSEY C L. Status, emerging ideas and future directions of turbulence modeling research in aeronautics: NASA/TM-2017-219682 [R]. Washington, D.C.: NASA, 2017.
|
9 |
YARLANKI S, RAJENDRAN B, HAMANN H. Estimation of turbulence closure coefficients for data centers using machine learning algorithms[C]∥13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems. Piscataway: IEEE Press, 2012: 38-42.
|
10 |
LING J, TEMPLETON J. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty[J]. Physics of Fluids, 2015, 27(8): 085103.
|
11 |
SINGH A P, MEDIDA S, DURAISAMY K. Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils[J]. AIAA Journal, 2017, 55(7): 2215-2227.
|
12 |
TRACEY B D, DURAISAMY K, ALONSO J J. A machine learning strategy to assist turbulence model development[C]∥53rd AIAA Aerospace Sciences Meeting. Reston: AIAA, 2015.
|
13 |
ZHANG Z J, DURAISAMY K. Machine learning methods for data-driven turbulence modeling[C]∥22nd AIAA Computational Fluid Dynamics Conference. Reston: AIAA, 2015.
|
14 |
WANG J X, WU J L, XIAO H. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data[J]. Physical Review Fluids, 2017, 2(3): 034603.
|
15 |
尹宇辉, 李浩然, 张宇飞, 等. 机器学习辅助湍流建模在分离流预测中的应用[J]. 空气动力学学报, 2021, 39(2): 23-32.
|
|
YIN Y H, LI H R, ZHANG Y F, et al. Application of machine learning assisted turbulence modeling in flow separation prediction[J]. Acta Aerodynamica Sinica, 2021, 39(2): 23-32 (in Chinese).
|
16 |
LING J L, KURZAWSKI A, TEMPLETON J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance[J]. Journal of Fluid Mechanics, 2016, 807: 155-166.
|
17 |
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(1): 015105.
|
18 |
王铎, 刘超群, 蔡小舒, 等. 基于流动仿真大数据应对旋涡-湍流的研究进展[J]. 力学季刊, 2022, 43(2): 197-216.
|
|
WANG D, LIU C Q, CAI X S, et al. Tackling vortex/turbulence challenges based on direct numerical simulation data in fluid science[J]. Chinese Quarterly of Mechanics, 2022, 43(2): 197-216 (in Chinese).
|
19 |
王述之, 战林浩, 曹博超, 等. 基于直接数值模拟数据和神经网络的湍流封闭模型构建[J]. 水动力学研究与进展(A辑), 2020, 35(2): 141-154.
|
|
WANG S Z, ZHAN L H, CAO B C, et al. Turbulence closure model based on neural network and direct numerical simulation data[J]. Chinese Journal of Hydrodynamics, 2020, 35(2): 141-154 (in Chinese).
|
20 |
张珍, 叶舒然, 岳杰顺, 等. 基于组合神经网络的雷诺平均湍流模型多次修正方法[J]. 力学学报, 2021, 53(6): 1532-1542.
|
|
ZHANG Z, YE S R, YUE J S, et al. A combined neural network and multiple modification strategy for Reynolds-averaged Navier-Stokes turbulence modeling[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(6): 1532-1542 (in Chinese).
|
21 |
DURAISAMY K, ZHANG Z J, SINGH A P. New approaches in turbulence and transition modeling using data-driven techniques[C]∥53rd AIAA Aerospace Sciences Meeting. Reston: AIAA, 2015.
|
22 |
RUMSEY C L, COLEMAN G N, WANG L. In search of data-driven improvements to RANS models applied to separated flows[C]∥AIAA SciTech 2022 Forum. Reston: AIAA, 2022.
|
23 |
何创新, 邓志文, 刘应征. 湍流数据同化技术及应用[J]. 航空学报, 2021, 42(4): 524704.
|
|
HE C X, DENG Z W, LIU Y Z. Turbulent flow data assimilation and its applications[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524704 (in Chinese).
|
24 |
闫重阳, 张宇飞, 陈海昕. 基于离散伴随的流场反演在湍流模拟中的应用[J]. 航空学报, 2021, 42(4): 524695.
|
|
YAN C Y, ZHANG Y F, CHEN H X. Application of field inversion based on discrete adjoint method in turbulence modeling[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524695 (in Chinese).
|
25 |
刘超群. Liutex-涡定义和第三代涡识别方法[J]. 空气动力学学报, 2020, 38(3): 413-431, 478.
|
|
LIU C Q. Liutex-third generation of vortex definition and identification methods[J]. Acta Aerodynamica Sinica, 2020, 38(3): 413-431, 478 (in Chinese).
|
26 |
LIU C Q, GAO Y S, DONG X R, et al. Third generation of vortex identification methods: Omega and Liutex/Rortex based systems[J]. Journal of Hydrodynamics, 2019, 31(2): 205-223.
|
27 |
LIU C Q, GAO Y S, TIAN S L, et al. Rortex—A new vortex vector definition and vorticity tensor and vector decompositions[J]. Physics of Fluids, 2018, 30(3): 035103.
|
28 |
GAO Y S, LIU C Q. Rortex and comparison with eigenvalue-based vortex identification criteria[J]. Physics of Fluids, 2018, 30(8): 085107.
|
29 |
LIU C Q, XU H Y, CAI X S, et al. Liutex and its applications in turbulence research[M]. New York: Academic Press, 2021.
|
30 |
CHONG M S, PERRY A E, CANTWELL B J. A general classification of three-dimensional flow fields[J]. Physics of Fluids A: Fluid Dynamics, 1990, 2(5): 765-777.
|
31 |
EPPSB P. Review of vortex identification methods[C]∥55th AIAA Aerospace Sciences Meeting. Reston: AIAA, 2017.
|
32 |
WANG Y Q, GAO Y S, LIU J M, et al. Explicit formula for the Liutex vector and physical meaning of vorticity based on the Liutex-Shear decomposition[J]. Journal of Hydrodynamics, 2019, 31(3): 464-474.
|
33 |
YU Y F, SHRESTHA P, NOTTAGE C, et al. Principal coordinates and principal velocity gradient tensor decomposition[J]. Journal of Hydrodynamics, 2020, 32(3): 441-453.
|
34 |
SPALART P R, ALLMARAS S R. A one-equation turbulence model for aerodynamic flows[C]∥30th Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 1992.
|
35 |
SCHAEFER J A, CARY A W, MANI M, et al. Uncertainty quantification and sensitivity analysis of SA turbulence model coefficients in two and three dimensions[C]∥55th AIAA Aerospace Sciences Meeting. Reston: AIAA, 2017.
|
36 |
GILES M B, PIERCE N A. An introduction to the adjoint approach to design[J]. Flow, Turbulence and Combustion, 2000, 65(3): 393-415.
|
37 |
BYRD R H, LU P H, NOCEDAL J, et al. A limited memory algorithm for bound constrained optimization[J]. SIAM Journal on Scientific Computing, 1995, 16(5): 1190-1208.
|
38 |
SOMERS D M. Design and experimental results for the S809 airfoil[R]. Colorado: National Renewable Energy Laboratory, 1997.
|
39 |
ECONOMON T D, PALACIOS F, COPELAND S R, et al. SU2: An open-source suite for multiphysics simulation and design[J]. AIAA Journal, 2016, 54(3): 828-846.
|
40 |
VIRTANEN P, GOMMERS R, OLIPHANT T E, et al. SciPy 1.0: Fundamental algorithms for scientific computing in python[J]. Nature Methods, 2020, 17(3): 261-272.
|
41 |
PASZKE A, GROSS S, MASSA F, et al. PyTorch: An imperative style, high-performance deep learning library[EB/OL]. (2019-12-03)[2023-09-13].
|
42 |
SOMERS D M. Design and experimental results for the S814 airfoil[R/OL]. (1997-01)[2023-09-13]. .
|