Aerodynamic optimization using Adjoint methods and parametric CAD models
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1 Aerodynamic optimization using Adjoint methods and parametric CAD models ECCOMAS Congress 2016 P. Hewitt S. Marques T. Robinson D. School of Mechanical and Aerospace Engineering Queen s University Belfast
2 Contents Motivation CAD parameterisation Gradient Calculation Onera Wing NLR 7301 Conclusions D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 2/1
3 Outline D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 3/1
4 Motivation Perform high-fidelity aerodynamic optimisation D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 4/1
5 Motivation Perform high-fidelity aerodynamic optimisation Increase flexibility of Adjoint Based Optimisation D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 4/1
6 Motivation Perform high-fidelity aerodynamic optimisation Increase flexibility of Adjoint Based Optimisation Enable use of parametric CAD model in optimisation D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 4/1
7 Motivation Perform high-fidelity aerodynamic optimisation Increase flexibility of Adjoint Based Optimisation Enable use of parametric CAD model in optimisation Efficient calculation of parametric sensitivities for CAD based design variables D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 4/1
8 Motivation There are two main challenges to perform high-fidelity aerodynamic optimisation D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 5/1
9 Motivation There are two main challenges to perform high-fidelity aerodynamic optimisation Computational Cost Gradient Based Optimisation D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 5/1
10 Motivation There are two main challenges to perform high-fidelity aerodynamic optimisation Computational Cost Gradient Based Optimisation Large number of parameters Adjoint Methods D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 5/1
11 Motivation In CFD based optimisation, parameterisations are usual built in the software D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 6/1
12 Motivation The objective of this work is to integrate parameters used by CAD designers with high-fidelity analysis and optimisation. D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 7/1
13 Outline D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 8/1
14 SU 2 SU 2 is an open-source CFD/Adjoint optimisation framework 1 Developed at Stanford University 1 images taken from D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 9/1
15 SU 2 SU 2 is an open-source CFD/Adjoint optimisation framework 1 Developed at Stanford University General purpose PDE solution methods 1 images taken from D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 9/1
16 SU 2 SU 2 is an open-source CFD/Adjoint optimisation framework 1 Developed at Stanford University General purpose PDE solution methods Range of numerical schemes available (JST, ROE, MG, Euler-Implicit,...) 1 images taken from D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 9/1
17 SU 2 SU 2 is an open-source CFD/Adjoint optimisation framework 1 Developed at Stanford University General purpose PDE solution methods Range of numerical schemes available (JST, ROE, MG, Euler-Implicit,...) Independent Mesh deformation/adaptation modules 1 images taken from D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 9/1
18 SU 2 SU 2 is an open-source CFD/Adjoint optimisation framework 1 Developed at Stanford University General purpose PDE solution methods Range of numerical schemes available (JST, ROE, MG, Euler-Implicit,...) Independent Mesh deformation/adaptation modules Continuous Adjoint Solver 1 images taken from D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 9/1
19 SU 2 SU 2 is an open-source CFD/Adjoint optimisation framework 1 Developed at Stanford University General purpose PDE solution methods Range of numerical schemes available (JST, ROE, MG, Euler-Implicit,...) Independent Mesh deformation/adaptation modules Continuous Adjoint Solver Independent Gradient Calculation module 1 images taken from D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 9/1
20 Gradient Based Optimisation Gradient Calculation f A 1 f A 2 =. f } A {{ n } Gradients f A i - Gradient x j A i - Design Velocities f x j - Surface Sensitivities x 1 x m A 1 A x 1 x m A n A }{{ n } Design Velocities f x 1 f x 2. f x }{{ m } Surface Sensitivities D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 10/1
21 Gradient Based Optimisation Gradient Calculation f A 1 f A 2 =. f } A {{ n } Gradients f A i - Gradient x j A i - Design Velocities f x j - Surface Sensitivities x 1 x m A 1 A x 1 x m A n A }{{ n } Design Velocities f x 1 f x 2. f x }{{ m } Surface Sensitivities D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 10/1
22 Surface Sensitivities Flow sensitivity to surface obtained from adjoint solver (SU 2 ) D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 11/1
23 Design Velocities Gradient Calculation f A 1 f A 2 =. f } A {{ n } Gradients f A i - Gradient x j A i - Design Velocities f x j - Surface Sensitivities x 1 x m A 1 A x 1 x m A n A }{{ n } Design Velocities f x 1 f x 2. f x }{{ m } Surface Sensitivities D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 12/1
24 CAD parameterisation CATIA geometry 27 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 13/1
25 CAD parameterisation CATIA geometry 27 CATIA Parameters Design Velocities for Param.1 Design Velocities for Param.3 D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 13/1
26 Deform Surface Use design velocities and mesh deformation module (linear elasticity) to deform surface CFD mesh. D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 14/1
27 Outline D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 15/1
28 Flow and Adjoint Solutions Start the process by computing the flow and adjoint solution D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 16/1
29 Flow and Adjoint Solutions Start the process by computing the flow and adjoint solution and at the same time... D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 17/1
30 Geometric Sensitivities CATIA geometry 27 CATIA Parameters Geometry Sensitivity to Param.1 Geometry Sensitivity to Param.3 D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 18/1
31 Gradient Validation Compute gradient for optimiser: Optimiser returns updated parameter values, which is used to create new CAD model and new design velocities calculated... D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 19/1
32 Onera Wing Drag Minimization Transonic Test Case Inviscid Calculation M = ; α = 3.06 min C D subject to: C L > D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 20/1
33 Onera Wing Drag Minimization Transonic Test Case Inviscid Calculation M = ; α = 3.06 D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 21/1
34 Onera Wing Drag Minimization D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 22/1
35 Onera Wing Drag Minimization D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 23/1
36 Outline D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 24/1
37 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 25/1
38 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 26/1
39 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 27/1
40 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 28/1
41 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 29/1
42 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 30/1
43 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 31/1
44 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 32/1
45 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 33/1
46 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 34/1
47 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 35/1
48 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 36/1
49 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 37/1
50 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 38/1
51 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 39/1
52 NLR 7301 High-Lift Case High-Lift Test Case RANS Calculation (using SA) M = 0.185; α = 6 ; Re = ; Maximise L/D; 14 CATIA Parameters D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 40/1
53 NLR 7301 High-Lift Case Original Optimised D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 41/1
54 NLR 7301 High-Lift Case Original Optimised D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 42/1
55 Outline D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 43/1
56 Conclusions Conclusions: D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 44/1
57 Conclusions Conclusions: CAD parameterisations were coupled with a CFD/Adjoint optimisation framework, SU 2 D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 44/1
58 Conclusions Conclusions: CAD parameterisations were coupled with a CFD/Adjoint optimisation framework, SU 2 Model deformation and geometric sensitivities are calculated outside CFD solver D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 44/1
59 Conclusions Conclusions: CAD parameterisations were coupled with a CFD/Adjoint optimisation framework, SU 2 Model deformation and geometric sensitivities are calculated outside CFD solver Alternative approach does not compromise optimisation efficiency with respect to native parameterisations D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 44/1
60 Q & A Thank you for your attention Questions Welcome D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 45/1
61 CFD convergence D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 46/1
62 Backup D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 47/1
63 Backup Original Optimised D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 48/1
64 Backup Original Optimised D. Agarwal Aerodynamic optimization using Adjoint methods and parametric CAD models 49/1
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