Probabilistic approach to free-form airfoil shape optimization under uncertainty
By L. Huyse, S. Padula, Robert Michael Lewis, Wu Li. 2002
117 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computational Theory and Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/50d4d8d3d688c4b7d848a2dc9507afe905a31b77
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Costas Papadimitriou
- D. Papadimitriou
- Ji-chao Li
- Xiaosong Du
- Joaquim R. Martins
- Gaetan K. W. Kenway
- Kyriakos C. Giannakoglou
In Computational Theory and Mathematics
- D. Papadimitriou
- Joaquim R. Martins
- Ji-chao Li
- Costas Papadimitriou
- Xiaosong Du
- Gaetan K. W. Kenway
- Andrew B. Lambe
- Martin P. Bendsøe
In Statistics and Probability
- D. Papadimitriou
- Costas Papadimitriou
- Ji-chao Li
- Joaquim R. Martins
- Xiaosong Du
- Gaetan K. W. Kenway
- Geert Lombaert
- Hao Sun
Citations in the corpus
- Machine Learning in Aerodynamic Shape Optimization (2022, background): “Therefore, most robust design [102] or reliability-based design [103] applications focus on two-dimensional airfoil shape design [104, 105] or three-dimensional configuration design considering the uncertainty of several operating parameters such as the Mach number and lift coefficient [106–110], wh”
- Aerodynamic shape optimization for minimum robust drag and lift reliability constraint (2016, background): “A multi-point optimization approach has been introduced to account for uncertainties by computing the performance in multiple points in the uncertain parameter space [34,16,23,35].”