Direct Search Airfoil Optimization Using Far-Field Drag Decomposition Results

Gariépy, Martin; Trépanier, Jean‐Yves; Petro, Eddy; Malouin, Benoit; Audet, Charles; LeDigabel, Sébastien; Tribes, Christophe

doi:10.2514/6.2015-1720

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Nowadays, the FDD methods have been extended to the fields of unsteady flow, 11,17 aerodynamic optimization, [18][19][20][21] engine thrust and drag analysis, 10,[22][23][24] laminar aerodynamic analysis 25,26 and mesh refinement. 27 However, the problems on the robustness and convenience of the FDD method are still unfixed.…”

Section: Introductionmentioning

confidence: 99%

Far-field drag decomposition using hybrid formulas and vorticity based area sensors

Qiao

Sun

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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Numerical simulation of flow-field has become an indispensable tool for aerodynamic design. Usually, wall surface integration is a tool used to calculate values of pressure drag and skin friction drag, but the aerodynamic mechanism of drag production is still confusing. In present work, in order to decompose the total drag into viscous drag, wave drag, induced drag, and spurious drag, a far-field drag decomposition (FDD) method is developed. This method depends on axial velocity defect and area sensor functions. The present work proposes three hybrid formulas for velocity defect to tackle the negative square root issue by analyzing the existing axial velocity defect formulas. For dealing with the issue of detection failure for near-wall cells, a novel vorticity based viscous area sensor function is proposed. The new area sensor function is also independent of the turbulence model, which ensures easy application to general simulation methods for flow-field. Three tests cases are there to validate the proposed FDD method. The three dimensional transonic CRM test case shows that the present improvement is crucial for accurate drag decomposition. Excellent agreement between total decomposed drags and results from the near-field method or experimental data further confirms the correctness.

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Section: Introductionmentioning

confidence: 99%

Far-field drag decomposition using hybrid formulas and vorticity based area sensors

Qiao

Sun

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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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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“…This will imply a considerable advantage while facing aerodynamic optimisation problems since the use of coarser grids allows a significant reduction of the required computational time. This advantage for optimisation has already been shown in [7,12].…”

Section: Far-field Drag Coefficient Calculationmentioning

confidence: 65%

Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation

Morales

Köröndi

Quagliarella

et al. 2021

Advances in Uncertainty Quantification and Optimization Under Uncertainty With Aerospace Applications

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Uncertainty-based optimisation techniques provide optimal airfoil designs that are less vulnerable to the presence of uncertainty in the operational conditions (i.e., Mach number, angle-of-attack, etc.) at which an airfoil is functioning. These uncertainty-based techniques typically require numerous function evaluations to accurately calculate the statistical measure of the quantity of interest. To render the computational burden down, the design optimisation of the airfoil is performed by a multi-fidelity surrogate-based technique. The high-fidelity aerodynamic performance is calculated with a compressible RANS solver using a fine grid. At the low-fidelity level a coarser grid is used. To obtain accurate drag predictions despite the lower grid resolution the so-called far-field drag approximation is employed.

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Direct Search Airfoil Optimization Using Far-Field Drag Decomposition Results

Cited by 10 publications

References 9 publications

Far-field drag decomposition using hybrid formulas and vorticity based area sensors

Far-field drag decomposition using hybrid formulas and vorticity based area sensors

Impact of Shape Parameterisation on Aerodynamic Optimisation of Benchmark Problem

Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation

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