Drag minimization of an isolated airfoil in transonic inviscid flow by means of genetic algorithms

Fusi, Francesca; Congedo, Pietro Marco; Quaranta, Giuseppe; Guardone, Alberto

doi:10.2514/6.2015-1722

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…This case has previously been investigated with a variety of different parameterisation methods: Bèzier Curves [1,2]; B-Splines [3,4,5,6,7]; NURBS [8]; Class/Shape Transformations (CSTs) [9]; Hicks-Henne bump functions [10]; Bèzier Surface FFD [11]; PARSEC [12]; Radial basis function domain element (RBF-DE) deformation [13,14] and Singular Value Decompositions (SVD) [14,15]. Due to the large range of factors influencing the results it is difficult to isolate contribution of the parameterisation method from these previous studies.…”

Section: Introductionmentioning

confidence: 99%

Impact of Shape Parameterisation on Aerodynamic Optimisation of Benchmark Problem

Masters

Poole

Taylor

et al. 2016

54th AIAA Aerospace Sciences Meeting

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Department of Aerospace Engineering, University of BristolThis paper presents an investigation into the influence of shape parameterisation and dimensionality on the optimisation of a benchmark case described by the AIAA Aerodynamic Design Optimisation Discussion Group. This problem specifies the drag minimisation of a NACA0012 under inviscid flow conditions at M = 0.85 and α = 0 subject to the constraint that local thickness must only increase. The work presented here applies six different shape parameterisation schemes to this optimisation problem with between 4 and 40 design variables. The parameterisation methods used are: Bèzier Surface FFD; B-Splines; CSTs; Hicks-Henne bump functions; a Radial Basis Function domain element method (RBF-DE) and a Singular Value Decomposition (SVD) method. The optimisation framework used consists of a gradient based SQP optimiser coupled with the SU 2 adjoint Euler solver which enables the efficient calculation of the design variable gradients. Results for the all the parameterisation methods are presented with the best results for each method ranging between 25 and 56 drag counts from an initial value of 469. The optimal result was achieved with the B-Spline method with 16 design variables. A further validation of the results is then presented and the presence of hysteresis is explored.

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Section: Introductionmentioning

confidence: 99%

Impact of Shape Parameterisation on Aerodynamic Optimisation of Benchmark Problem

Masters

Poole

Taylor

et al. 2016

54th AIAA Aerospace Sciences Meeting

View full text Add to dashboard Cite

show abstract

“…The wing geometry has an initial volume of V = 0.24 cubed reference units and a reference area of S = 3.0 squared reference units. The flow analysis is performed at a Reynolds number of 20⇥10 6 and at both Mach 0.78 and Mach 0.85. Despite the fact that the wing chords are free to change, the final geometries are tapered such that the overall mean aerodynamic chord is close to one, meaning that the initial Reynolds number is still valid at the end of optimization.…”

Section: Optimization Problemmentioning

confidence: 99%

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

Koo

Zingg

2016

54th AIAA Aerospace Sciences Meeting

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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.

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“…Even with advanced techniques to balance the exploration and exploitation of the design space [4][5][6][7][8], this can still make optimization intractable for complex geometries.…”

Section: Introductionmentioning

confidence: 99%

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Chen¹,

Chiu²,

Fuge³

2020

Preprint

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Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design space. One approach to combat this problem is to reduce the design space dimension by obtaining a new representation. This requires a parametric function that compactly and sufficiently describes useful variation in shapes. We propose a deep generative model, Bézier-GAN, to parameterize aerodynamic designs by learning from shape variations in an existing database. The resulted new parameterization can accelerate design optimization convergence by improving the representation compactness while maintaining sufficient representation capacity. We use the airfoil design as an example to demonstrate the idea and analyze Bézier-GAN's representation capacity and compactness. Results show that Bézier-GAN both (1) learns smooth

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Drag minimization of an isolated airfoil in transonic inviscid flow by means of genetic algorithms

Cited by 6 publications

References 7 publications

Impact of Shape Parameterisation on Aerodynamic Optimisation of Benchmark Problem

Impact of Shape Parameterisation on Aerodynamic Optimisation of Benchmark Problem

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

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

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