Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases

LeDoux, Stephen T.; Vassberg, John C.; Young, David P.; Fugal, Spencer R.; Kamenetskiy, Dmitry S.; Huffman, William P.; Melvin, Robin G.; Smith, Matthew F.

doi:10.2514/1.j053535

Cited by 51 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, the discrepancies in the solutions are quite localized. The double pressure recovery features are consistent with curves observed for this case by LeDoux et al 27 The presence of non-unique solutions in the design space explains the poor convergence of some of the optimizations. Analyses of other geometries using the different convergence paths were conducted, and while non-unique solutions were not always observed, they were found to be common.…”

Section: Optimization Resultssupporting

confidence: 89%

Aerodynamic Shape Optimization of Benchmark Problems Using Jetstream

Lee

Koo

Telidetzki

et al. 2015

53rd AIAA Aerospace Sciences Meeting

View full text Add to dashboard Cite

This work presents results from the application of an aerodynamic shape optimization code, Jetstream, to a suite of benchmark cases defined by the Aerodynamic Design Optimization Discussion Group. Geometry parameterization and mesh movement are integrated by fitting the multi-block structured grids with B-spline volumes and performing mesh movement based on a linear elastic model applied to the control points. Geometry control is achieved through two different approaches. Either the B-spline surface control points are taken as the design variables for optimization, or alternatively, the surface control points are embedded within free-form deformation (FFD) B-spline volumes, and the FFD control points are taken as the design variables. Spatial discretization of the Euler or Reynolds-averaged Navier-Stokes equations is performed using summation-by-parts operators with simultaneous approximation terms at boundaries and block interfaces. The governing equations are solved iteratively using a parallel Newton-Krylov-Schur algorithm. The discrete-adjoint method is used to calculate the gradients supplied to a sequential quadratic programming optimization algorithm. The first optimization problem studied is the drag minimization of a modified NACA 0012 airfoil at zero angle of attack in inviscid, transonic flow, with a minimum thickness constraint set to the initial thickness. The shock is weakened and moved downstream, reducing drag by 91%. The second problem is the liftconstrained drag minimization of the RAE 2822 airfoil in viscous, transonic flow. The shock is eliminated and drag is reduced by 48%. Both two-dimensional cases exhibit optimization convergence difficulties due to the presence of nonunique flow solutions. The third problem is the twist optimization for minimum induced drag at fixed lift of a rectangular wing in subsonic, inviscid flow. A span efficiency factor very close to unity and a near elliptical lift distribution are achieved. The final problem includes single-point and multi-point liftconstrained drag minimizations of the Common Research Model wing in transonic, viscous flow. Significant shape changes and performance improvements are achieved in all cases. Finally, two additional optimization problems are presented that demonstrate the capabilities of Jetstream and could be suitable additions to the Discussion Group problem suite. The first is a wing-fuselage-tail optimization with a prescribed spanwise load distribution on the wing. The second is an optimization of a box-wing geometry.

show abstract

Section: Optimization Resultssupporting

confidence: 89%

Aerodynamic Shape Optimization of Benchmark Problems Using Jetstream

Lee

Koo

Telidetzki

et al. 2015

53rd AIAA Aerospace Sciences Meeting

View full text Add to dashboard Cite

show abstract

“…The plot at y/b = 0.5024 shows that the optimizer is successful in eliminating the shock and and smoothing out the pressure recovery. The optimizer rotates the tail down one degree and reduces the overall drag by 2% relative to the baseline untrimmed geometry, a result which agrees well with Chen et al 9 and Ledoux et al 26 showed that geometries optimized for this problem can have non-unique flow solutions and poor o↵-design behaviour. A multi-point optimization is presented here for in an attempt to produce more robust airfoils.…”

Section: Resultssupporting

confidence: 82%

Progress in Aerodynamic Shape Optimization Based on the Reynolds-Averaged Navier-Stokes Equations

Koo

Zingg

2016

54th AIAA Aerospace Sciences Meeting

View full text Add to dashboard Cite

This paper presents the application of an aerodynamic shape optimization methodology, Jetstream, to several cases in order to characterize the methodology and demonstrate its ability to solve challenging problems. In Jetstream, geometry parameterization and mesh movement are integrated by fitting the multi-block structured grids with B-spline volumes and applying mesh movement based on linear elasticity to the control points. Geometry control is achieved through either of two di↵erent approaches: using the B-spline surface control points as design variables, or embedding them into within a free-form deformation volume. Spatial discretization of the Reynolds-averaged Navier-Stokes equations is performed using summation-by-parts operators with simultaneous approximation terms at boundaries and block interfaces. The discrete equations are solved iteratively using a parallel Newton-Krylov-Schur algorithm. The discrete-adjoint method is used to calculate the gradients, which are supplied to a sequential quadratic programming optimization algorithm. The first drag minimization problem revisits the single-point lift-constrained drag minimization of the NASA Common Research Model (CRM) wing. Multimodality is studied on the CRM wing, beginning the same problem with a variety of di↵erent starting geometries. Results are also presented for the single-point CRM optimization with a 75% and a 100% minimum thickness constraint. The CRM wing-body-tail configuration is then optimized with a trim constraint and including the rotation of the horizontal stabilizer as a design variable. The second problem is a multipoint optimization of the RAE 2822 airfoil in transonic flow. The final two problems test the geometric flexibility and robustness of the aerodynamic optimization method. The first is a planform optimization of a rectangular NACA00012 wing in transonic, viscous flow. The second is a wing tip optimization of a planform based on the Boeing 737-900 wing. Overall, the results demonstrate that the algorithms adopted in Jetstream provide a reliable and e↵ective aerodynamic shape optimization methodology capable of addressing challenging problems involving substantial geometric changes.

show abstract

“…As such, the AIAA Aerodynamic Design Optimization Discussion Group (ADODG) a was formed. As part of this, a number of inviscid and viscous aerofoil and wing benchmark optimization cases were suggested, with a number of research groups presenting results; see [13][14][15][16][17][18][19][20][21] for example.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Wing Aerodynamic Optimization Case Exhibiting Multimodality

Poole

Allen

Rendall

2018

Journal of Aircraft

View full text Add to dashboard Cite

An investigation into an aerodynamic optimization benchmark case that exhibits multimodality is presented using a global optimization approach. The recently suggested case 6 of the AIAA Aerodynamic Design Optimization Discussion Group involves the drag minimization of a rectangular NACA0012 wing subject to lift and root bending moment, as well as other geometric constraints. In this paper, optimization of that case using a state-of-the-art constrained population-based global optimization framework is presented.Shape control is achieved via a hierarchical application of the radial basis function domain element approach. Three different fidelity of optimization cases are performed to investigate the multimodality of various aspects of the full problem, with four independent runs of each case being performed. When considering chord optimization, the optimizer converges to two independently identifiable minima with very similar performance. When adding dihedral and sweep variation, further multimodality is introduced, though this multimodality is reasonably well defined, with minima including forward and rearward sweep, and an upward and downward winglet. Introducing thickness variables leads to a highly multimodal problem due to the coupling between the chord and thickness variables. The theoretical minimum drag for this case is 24.7 counts, which a number of the runs get close to achieving.

show abstract

Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases

Cited by 51 publications

References 27 publications

Aerodynamic Shape Optimization of Benchmark Problems Using Jetstream

Aerodynamic Shape Optimization of Benchmark Problems Using Jetstream

Progress in Aerodynamic Shape Optimization Based on the Reynolds-Averaged Navier-Stokes Equations

Global Optimization of Wing Aerodynamic Optimization Case Exhibiting Multimodality

Contact Info

Product

Resources

About