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Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark

TitleAerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark
Publication TypeJournal Articles
Year of Publication2015
AuthorsLyu, Z, Kenway, GKW, Martins, JRRA
JournalAIAA Journal
Volume53
Issue4
Pagination968--985
Date Published04/2015
Abstract

Despite considerable research on aerodynamic shape optimization, there is no standard benchmark problem allowing researchers to compare results. This work addresses this issue by solving a series of aerodynamic shape optimization problems based on the Common Research Model wing benchmark case defined by the Aerodynamic Design Optimization Discussion Group (ADODG). The aerodynamic model solves the Reynolds-averaged Navier--Stokes equations with a Spalart--Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with an adjoint method that computes the required derivatives. The drag coefficient is minimized subject to lift, pitching moment, and geometric constraints. A multilevel technique is used to reduce the computational cost of the optimization. A single-point optimization is solved with 720 shape variables using a 28.8-million-cell mesh, reducing the drag by 8.5%. A more realistic design is achieved through a multipoint optimization. Multiple local minima are found when starting from multiple randomly generated geometries, but the minimum drag values are within 0.1 drag counts of each other, and the geometries differ by only 0.4% of the mean aerodynamic chord. The effect of varying the number of shape design variables is examined. The Common Research Model wing benchmark problem proved to be useful for evaluating our design optimization framework, and the geometries and meshes for both the baseline and optimized wings are available as supplemental materials in this paper.

We made movies of the optimization histories for some of the cases presented in this paper; please click on the links below to watch them:

Baseline CRM wing optimization (Figure 5).

CRM wing optimization with random initial geometries (Figure 10).

CRM wing optimization with no thickness reduction (Figure 16).

Multipoint CRM wing optimization (Figure 18).

 

We have also made available the initial and optimized geometries and meshes for the main optimization case. 

Notes

(In press)

DOI10.2514/1.J053318.
Citation KeyLyu2014c