Open Access
Issue |
JNWPU
Volume 41, Number 5, Octobre 2023
|
|
---|---|---|
Page(s) | 987 - 995 | |
DOI | https://doi.org/10.1051/jnwpu/20234150987 | |
Published online | 11 December 2023 |
- MARGHERI L, MELDI M, SALVETTI M V, et al. Epistemic uncertainties in rans model free coefficients[J]. Computers and Fluids, 2014, 102(10): 315–335 [CrossRef] [Google Scholar]
- XIAO H, CINNELLA P. Quantification of model uncertainty in RANS simulations: a review[J]. Progress in Aerospace Sciences, 2019, 108: 1–31. [Article] [CrossRef] [Google Scholar]
- XU H, QIN D, LIU C, et al. An improved dynamic model updating method for multistage gearbox based on surrogate model and sensitivity analysis[J]. IEEE Access, 2021, 9: 18527–18537. [Article] [CrossRef] [Google Scholar]
- WANG Jisen, JIA Qian, CHEN Chen, et al. Research of turbulence model parameters correction for oil flow of pipeline[J]. Journal of System Simulation, 2018, 30(5): 1665–1671. [Article] (in Chinese) [Google Scholar]
- ZHANG Zhen, YE Shuran, YUE Jieshun, 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. [Article] (in Chinese) [Google Scholar]
- CINNELLA P, DWIGHT R, EDELING W. Review of uncertainty quantification in turbulence modelling to date[C]//SIAM Uncertainty Quantification Conference, Switzerland, 2016 [Google Scholar]
- LIU D S, LITVINENKO A, SCHILLINGS C, et al. Quantification of airfoil geometry-induced aerodynamic uncertainties-comparison of approaches[J]. SIAM/ASA Journal on Uncertainty Quantification, 2017, 5(1): 334–352. [Article] [CrossRef] [Google Scholar]
- LOEVEN A, BIJL H. Airfoil analysis with uncertain geometry using the probabilistic collocation method[C]//49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Schaumburg, 2008 [Google Scholar]
- GENEVA N, ZABARAS N. Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks[J]. Journal of Computational Physics, 2019, 383: 125–147. [Article] [CrossRef] [Google Scholar]
- CHEN J, ZHANG C, ZHAO W, et al. A high-fidelity polynomial chaos modified method suitable for CFD uncertainty quantification[C]//Journal of Physics, 2021, 1985(1): 012042 [Google Scholar]
- SIMPSON T, TOROPOV V, BALABANOV V, et al. Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come-or not[C]//12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008 [Google Scholar]
- SHAN S Q, WANG G G. Metamodeling for high dimensional simulation-based design problems[J]. Journal of Mechanical Design, 2010, 132(5): 11. [Google Scholar]
- 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] [CrossRef] [Google Scholar]
- CHEN Hai, QIAN Weiqi, HE Lei. Aerodynamic coefficient prediction of airfoils based on deep learning[J]. Acta Aerodynamica Sinica, 2018, 36(2): 294–299. [Article] (in Chinese) [Google Scholar]
- XIONG Fenfen, YANG Shuxing, LIU Yu, et al. Engineering probabilistic uncertainty analysis method[M]. Beijing: Science Press, 2015 (in Chinese) [Google Scholar]
- XIONG Fenfen, CHEN Jiangtao, REN Chengkun, et al. Recent advances in polynomial chaos method for uncertainty propagation[J]. Chinese Journal of Ship Research, 2021, 16(4): 18. [Article] (in Chinese) [Google Scholar]
- WANG H, YEUNG D Y. A survey on Bayesian deep learning[J].ACM Computing Surveys, 2020, 53(5): 1–37 [Google Scholar]
- BLUNDELL C, CORNEBISE J, KAVUKCUOGLU K, et al. Weight uncertainty in neural network[C]//International Conference on Machine Learning, 2015: 1613–1622 [Google Scholar]
- SNOEK J, RIPPEL O, SWERSKY K, et al. Scalable Bayesian optimization using deep neural networks[C]//International Conference on Machine Learning, 2015: 2171–2180 [Google Scholar]
- LIU Y, CHEN W, ARENDT P, et al. Toward a better understanding of model validation metrics[J]. Journal of Mechanical Design, 2011, 133(7): 071005 [CrossRef] [Google Scholar]
- SUDRET B. Global sensitivity analysis using polynomial chaos expansions[J]. Reliability Engineering & System Safety, 2008, 93(7): 964–979 [CrossRef] [Google Scholar]
- HE X, ZHAO F, VAHDATI M. Uncertainty quantification of spalart-allmaras turbulence model coefficients for simplified compressor flow features[J]. Journal of Fluids Engineering, 2019, 142(9): 36–48 [Google Scholar]
- LADSON C L, HILL A S, JOHNSON JR W G. Pressure distributions from high Reynolds number transonic tests of an NACA 0012 airfoil in the Langley 0.3-meter transonic cryogenic tunnel[R]. NASA-TM-100527, 1987 [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.