Open Access
Volume 37, Number 5, October 2019
Page(s) 909 - 917
Published online 14 January 2020
  1. Busemann A. Aerodynamic Lift at Supersonic Speeds[J]. Luftfahrtforschung, 1935, 12(6): 210–220 [Google Scholar]
  2. Kusunose K, Matsushima K, Maruyama D. Supersonic Biplane-A Review[J]. Progress in Aerospace Sciences, 2011, 47(1): 53–87 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  3. Hua Ruhao, Ye Zhengyin. Drag Reduction Method for Supersonic Missile Based on Busemann Biplane Concept[J]. Chinese Journal of Applied Mechanics, 2012, 29(5): 535–540 [Article] [Google Scholar]
  4. Li W, Huyse L, Padula S. Robust Airfoil Optimization to Achieve Drag Reduction over a Range of Mach Numbers[J]. Structural & Multidisciplinary Optimization, 2002, 24(1): 38–50 [Article] [CrossRef] [Google Scholar]
  5. Gao Zhenghong, Wang Cao. Aerodynamic Shape Design Methods for Aircraft:Status and Trends[J]. Acta Aerodynamica Sinica, 2017, 35(4): 516–528 [Article] [Google Scholar]
  6. Slotnick J, Khodadoust A, Alonso J, et al. CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences[R]. NASA/CR-2014-218178 [Google Scholar]
  7. Doostan A, Ghanem R G, Red-Horse J. Stochastic Model Reduction for Chaos Representations[J]. Computer Methods in Applied Mechanics & Engineering, 2007, 196(37/38/39/40: 3951–3966 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  8. Matre O P L, Knio O M. Spectral Methods for Uncertainty Quantification[M]. Berlin, Springer Netherlands, 2010 [Google Scholar]
  9. Xiu D, Karniadakis G E. Modeling Uncertainty in Flow Simulations Via Generalized Polynomial Chaos[J]. Journal of Computational Physics, 2003, 187(1): 137–167 [Article] [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  10. Hosder S, Walters R. A Non-Intrusive Polynomial Chaos Methods for Uncertainty Propagation in CFD Simulation[C]//44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nedava, 2006: 1–19 [Google Scholar]
  11. Loeven G J A, Bijl H. Probabilistic Collocation Used in a Two-Step Approached for Efficient Uncertainty Quantification in Computational Fluid Dynamics[J]. Computer Modeling in Engineering & Sciences, 2008, 36(3): 193–212 [Article] [NASA ADS] [Google Scholar]
  12. Loeven G J A. Efficient Uncertainty Quantification in Computational Fluid Dynamics[D]. Holland, Delft University of Technology, 2010 [Google Scholar]
  13. Dinescu C, Smirnov S, Hirsch C. Assessment of Intrusive and Non-Intrusive Non-Deterministic CFD Methodologies Based on Polynomial Chaos Expansions[J]. International Journal of Engineering Systems Modelling & Simulation, 2010, 2(1): 87–98 [Article] [CrossRef] [Google Scholar]
  14. Liu Zhiyi, Wang Xiaodong, Kang Shun. CFD Simulations of Uncertain Tip Clearance Effect on Compressor Performace[J]. Journal of Engineering Thermophysics, 2013, V34(4): 628–631 [Article] [Google Scholar]
  15. Zhang Han, Wu Xin. Application of Non-Intrusive Probabilistic Collocation Method in Back-Step Flow Calcultion[J]. Water Resources and Power, 2017730–32 [Article] [Google Scholar]
  16. Song Fuqiang, Yan Chao, Ma Baofeng, et al. Uncertainty Analysis of Aerodynamic Characteristics for Cone-Derived Waverider Configure[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(2): 121519 [Article] [Google Scholar]
  17. Wu Xiaojing, Zhang Weiwei, Song Shufang, et al. Uncertainty Quantification and Global Sensitivity Analysis of Transonic Aerodynamics about Airfoil[J]. Chinese Journal of Theoretical and Applied Mechanics, 2015, 47(4): 587–595 [Article] [Google Scholar]
  18. Cai Yi, Xing Yan, Hu Dan. On Sensitivity Analysis[J]. Journal of Beijing Normal University, 2008, 44(1): 9–16 [Article] [Google Scholar]
  19. Sobol I M. Sensitivity Estimates for Nonlinear Mathematical Models[J]. Math Model Comput Exp, 1993, 1(1): 112–118 [Google Scholar]
  20. Sobol I M. Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates[J]. Mathematics & Computers in Simulation, 2014, 55(1): 271–280 [Article] [Google Scholar]
  21. Tatang M A, Pan W, Prinn R G, et al. An Efficient Method for Parametric Uncertainty Analysis of Numerical Geophysical Models[J]. Journal of Geophysical Research Atmospheres, 1997, 102(18): 21925–21932 [Article] [CrossRef] [Google Scholar]
  22. Saltelli. Global Sensitivity Analysis[M]. England, John Wiley, 2008 [Google Scholar]
  23. Wang Gang, Ye Zhengyin. Generation of Three Dimensional Mixed and Unstructured Grids and its Application in Solving NavierStokes Equations[J]. Acta Aeronautica et Astronautica Sinica, 2003, 24(5): 385–390 [Article] [Google Scholar]
  24. Wang G, Ye Z Y, Wang G, et al. Application of Mixed Element Type Unstructured Grid in Solving Navier-Stokes Equations[J]. Chinese Journal of Computation Physics, 2004, 21(2): 161–165 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.