A Geometric Comparison of Aerofoil Shape Parameterisation Methods

By Dominic Masters, N. Taylor, Thomas C. S. Rendall, Christian B. Allen, Daniel J. Poole. 2016

162 citations recorded by the source corpus.

A comprehensive review of aerofoil shape parameterization methods that can be used for aerodynamic shape optimization is presented. Seven parameterization methods are considered for a range of desi...

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/179206c030c3b6fdb3b14fc48b9c4300623721d3

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

1. Daniel J. Poole
2. Christian B. Allen
3. Thomas C. S. Rendall
4. Ji-chao Li
5. Xiaosong Du
6. Joaquim R. Martins
7. Gaetan K. W. Kenway
8. A. Morris

In Statistical and Nonlinear Physics

1. Daniel J. Poole
2. Christian B. Allen
3. Thomas C. S. Rendall
4. Joaquim R. Martins
5. Ji-chao Li
6. Xiaosong Du

Citations in the corpus

• Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “[287] showed the outstanding efficiency of PCA-based airfoil modes by comparing seven different airfoil parameterization schemes (CST, Hicks–Henne bumps, B-splines, radial basis function domain elements, Bézier surfaces, and the parameterized sections method) in geometric shape recovery of over 2000”
• High-fidelity aerodynamic shape optimization using efficient orthogonal modal design variables with a constrained global optimizer (2017, methodology): “[21] demonstrated that when using RBF control points on the surface for geometric shape recovery, that 20 to 25 control points provided a good fidelity of surface representation.”