Match!

Aerodynamic inverse design using multifidelity models and manifold mapping

Published on Feb 1, 2019in Aerospace Science and Technology2.83
· DOI :10.1016/j.ast.2018.12.008
Xiaosong Du3
Estimated H-index: 3
(Iowa State University),
Jie Ren3
Estimated H-index: 3
(Iowa State University),
Leifur Leifsson15
Estimated H-index: 15
(Iowa State University)
Abstract
Abstract Aerodynamic inverse design is proposed using multifidelity models and the manifold mapping (MM) technique. Aerodynamic inverse design aims at achieving a target performance characteristic, such as a pressure coefficient distribution of an airfoil or local lift distribution of a wing. Due to the high computational cost of accurate aerodynamic models and the large number of design variables, the overall cost of inverse design can be prohibitive. The MM-based optimization algorithm leverages the speed of the low-fidelity model to accelerate the optimization process, but refers back to the high-fidelity model to ensure an accurate solution. In this work, the MM technique is applied to the characteristic distribution under consideration in each application. In particular, the pressure coefficient distribution is modeled with the MM technique in the case of airfoil inverse design, and the sectional lift distribution in the case of wing design. The proposed method is tested and evaluated on six airfoil inverse design cases and one rectangular wing inverse design case. In the two-dimensional cases, parameterized with eight design variables, direct aerodynamic inverse design using pattern search required 700 to 1200 high-fidelity model evaluations, which took 300 to 700 hours in total. The MM-based design algorithm required less than 20 high-fidelity simulations and 1000 to 2000 low-fidelity evaluations, which took 30 to 90 hours. In the three-dimensional case, parameterized with three design variables, direct aerodynamic inverse design took around 50 hours, whereas the MM-based design needed around six hours.
  • References (70)
  • Citations (3)
References70
Newest
#1Anirban ChaudhuriH-Index: 7
#2Remi LamH-Index: 4
Last.Karen WillcoxH-Index: 32
view all 3 authors...
10 CitationsSource
#2Karen Willcox (MIT: Massachusetts Institute of Technology)H-Index: 32
Last.Max Gunzburger (FSU: Florida State University)H-Index: 58
view all 3 authors...
90 CitationsSource
#1Shahriar Khosravi (U of T: University of Toronto)H-Index: 4
#2David W. Zingg (U of T: University of Toronto)H-Index: 32
1 CitationsSource
#1Kenneth A. Hart (Georgia Institute of Technology)H-Index: 2
#2Kyle R. Simonis (Georgia Institute of Technology)H-Index: 2
Last.Robert D. Braun (Georgia Institute of Technology)H-Index: 31
view all 4 authors...
2 CitationsSource
#1Victor Singh (MIT: Massachusetts Institute of Technology)H-Index: 2
#2Karen Willcox (MIT: Massachusetts Institute of Technology)H-Index: 32
3 CitationsSource
#1Xiaosong DuH-Index: 3
#2Anand AmritH-Index: 3
Last.Slawomir KozielH-Index: 44
view all 7 authors...
6 CitationsSource
#1Thomas A. Reist (U of T: University of Toronto)H-Index: 4
#2David W. Zingg (U of T: University of Toronto)H-Index: 32
5 CitationsSource
5 CitationsSource
#1Janis Jatnieks (Humboldt University of Berlin)H-Index: 2
#2Marco De LuciaH-Index: 8
Last.Mike Sips (Humboldt University of Berlin)H-Index: 8
view all 4 authors...
7 CitationsSource
14 CitationsSource
Cited By3
Newest
#1Raj NangiaH-Index: 5
#2Mehdi Ghoreyshi (USAFA: United States Air Force Academy)H-Index: 16
Last.Russell M. Cummings (USAFA: United States Air Force Academy)H-Index: 28
view all 4 authors...
Source
#1Shao-Qiang Han (NPU: Northwestern Polytechnical University)
#2Wenping Song (NPU: Northwestern Polytechnical University)H-Index: 3
Last.Yong-Feng Lin (Aviation Industry Corporation of China)
view all 5 authors...
Source
#1Y. Volkan Pehlivanoglu (IUE: İzmir University of Economics)H-Index: 6
1 CitationsSource
View next paperAirfoil Shape Optimization Using Variable-Fidelity Modeling and Shape-Preserving Response Prediction