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AEROSPACE COMPUTATIONAL DESIGN LABORATORY11
Gradient-based MultifidelityOptimisation for Aircraft Design using Bayesian Model Calibration
2nd Aircraft Structural Design ConferenceRoyal Aeronautical Society
October 28, 2010
Andrew March, Karen Willcox, & Qiqi Wang
AEROSPACE COMPUTATIONAL DESIGN LABORATORY22
Multifidelity Surrogates• Definition: High-Fidelity
– The best model of reality that is available and affordable, the analysis that is used to validate the design.
• Definition: Low(er)-Fidelity– A method with unknown accuracy that estimates metrics of interest
but requires lesser resources than the high-fidelity analysis.
Coarsened Mesh
Hierarchical Models
Reduced Physics
x
f(x)
x1
f(x1)
x1x2
f(x)
Regression ModelReduced Order Model
Approximation Models
AEROSPACE COMPUTATIONAL DESIGN LABORATORY3
Main Messages• Bayesian model calibration enables
synthesizing multifidelity sources of information to speed optimising designs with costly analyses.– Enables multiple low-fidelity models– Can benefit from gradient information when available– Can prove convergence to a high-fidelity optimum
• Adjoint solutions are common in engineering software and provide an inexpensive way of computing gradients.
AEROSPACE COMPUTATIONAL DESIGN LABORATORY44
Adjoint-based Gradient
• Optimisation problem:
• Implicit function theorem and adjoint:
• Everything expensive is in the objective function.
0uxrux
x=
ℑℜ∈
),(..),(min
high
high
tsn
))(,(min xuxx highf
nℜ∈ xr
xx ∂∂
−∂
∂ℑ= highThighhigh
ddf
ψ
Computational Model
xx
ddfhigh )(
)(xhighf
AEROSPACE COMPUTATIONAL DESIGN LABORATORY55
Trust-Region DemonstrationDemonstration
• Approximate high-fidelity function:
• Optimise the surrogate model:
• Evaluate Performance:
• Update trust region)()(
)()(
kkkkk
kkhighkhighk mm
ffsxx
sxx+−+−
=ρ
kk
kkk
ts
mn
k
∆≤
+
∞
ℜ∈
s
sxs
..
)(min
)()( xx highk fm ≈
AEROSPACE COMPUTATIONAL DESIGN LABORATORY66
Bayesian Model Calibration• Define a surrogate model of the
high-fidelity function:
• Cokriging error model, ek(x):– Interpolates fhigh(x)-flow(x) and
∇fhigh(x)-∇flow(x) exactly at all calibration points
• Trust-region algorithms are globally convergent to a stationary point of fhigh(x) if:(i) fhigh(xk)=mk(xk)(ii) ∇fhigh(xk)=∇mk(xk)(iii) ||∇2mk(x)||2≤κbhm– Cokriging models can satisfy
these requirements by construction.
)()()()( xxxx highklowk fefm ≈+≡
AEROSPACE COMPUTATIONAL DESIGN LABORATORY77
Combining Multiple Lower-Fidelities
• Calibrate all lower-fidelity models to the high-fidelity function using radial basis function error model
• Use a maximum likelihood estimator to predict the high-fidelity function value (Essentially a Kalman Filter)
AEROSPACE COMPUTATIONAL DESIGN LABORATORY8
Structural Design Problem• Minimize deflection of a 2-
Dimensional hook
• Linear Finite-Elements
• 26 Design variables– 25 shape parameters– Thickness
• 32 Constraints– 31 Geometric constraints– Maximum weight constraint
0)(0)(
)(..)(min
31
max
26
≤≤−
=
=ℑℜ∈
xx
fuxux
x
gww
KtsC
AEROSPACE COMPUTATIONAL DESIGN LABORATORY9
Multifidelity Structural Models
• Low-fidelity model is coarsely discretized– High-fidelity model: 1770 dof– Low-fidelity model: 276 dof
AEROSPACE COMPUTATIONAL DESIGN LABORATORY10
Adjoint-Based Derivative• Problem:
• Adjoint:
• Derivative:
– Satisfies constraint
• Derivative calculation:– Two forward solves or
one K-1 calculation
• Finite differences– Requires n+1 forward
solves
• Adjoint-based gradient nearly independent of the number of design variables.
0)(0)(
)(..)(min
max
≤≤−
=
=ℑℜ∈
xx
fuxux
x
gww
KtsC
n
TT CK )()( xx =ψ
uxx
xx
x⎟⎠⎞
⎜⎝⎛
∂∂
−∂
∂=
)()( KCd
df Thigh ψ
fux =)(K
AEROSPACE COMPUTATIONAL DESIGN LABORATORY11
Adjoint Gradient Verification
• Compare the adjoint-based derivative:
• With finite difference derivative:
xxxx
x δδ )()( highhighhigh ff
ddf −+
≈
1.54%
0.00574
10-5
6.57%1.73%1.51%Relative Error
0.02450.006450.00562Max Error
10-810-710-6Finite-difference step, δx
uxx
xx
x⎟⎠⎞
⎜⎝⎛
∂∂
−∂
∂=
)()( KCd
df Thigh ψ
AEROSPACE COMPUTATIONAL DESIGN LABORATORY12
Optimisation Results
• 31.6% Error between high- and low-fidelity deflection estimate
10.212.7171Standard deviation27.5 (-88%)40 (-83%)232 (-)Average high-fidelity function calls
Calibration ApproachFirst-Order TRSQP
AEROSPACE COMPUTATIONAL DESIGN LABORATORY13
Total Effort
• Nastran solution effort– Conjugate gradient, incomplete Choleksy factorization
preconditioner– Solution: O(dof2)
• Multifidelity effort:– 30 high-fidelity evaluations– 3,200 low-fidelity evaluations, ~80 high-fidelity
evaluations– Total effort: ~110 High-fidelity evaluations
• SQP effort: 232– Multifidelity savings ~50%
(ignoring surrogate model construction)
AEROSPACE COMPUTATIONAL DESIGN LABORATORY14
Supersonic Airfoil Design
• Minimize drag of a supersonic airfoil– 11 Design variables
• Angle of attack• 5 upper-surface spline points• 5 lower-surface spline points
– M∞=1.5– Constraints
• t/cmin≥0• t/cmax≥5%
%5)(/0)(/
0),(..),(min
11
≤−≤−
=
=ℑℜ∈
xx
uxrux
x
ctct
tsDrag
Euler SolutionPanel Method
AEROSPACE COMPUTATIONAL DESIGN LABORATORY15
Adjoint Gradient Computation
• Using a mesh generator with a known derivative a gradient can be computed with:
– 1 flow solve– 1 adjoint solve– 1 flow iteration/design variable
(1/500 flow solve)– Total: 2+n/500 solutions
• Finite difference gradient requires n+1 flow solutions
⎟⎠⎞
⎜⎝⎛
∂∂
+∂∂
−∂∂ℑ
+∂∂ℑ
=x
rxr
xxx ddM
MddM
Mddf Thigh ψ
Gradient of objective
function with respect to design
variables
Change in objective due
to x at constant pressure
Change in objective
function due to mesh change
Adjointsolution to the Euler equations
Change in residual due to mesh change
Change in residual due to x
AEROSPACE COMPUTATIONAL DESIGN LABORATORY16
Adjoint Gradient Verification
• Simple diamond airfoil with 3 parameters.
• Compare with finite difference:
0.53%0.53%0.56%0.65%1.6%Relative Error
0.02070.02050.02180.02520.0617Max Error
10-810-710-610-510-4Finite-difference step, δx
;⎟⎠⎞
⎜⎝⎛
∂∂
+∂∂
−∂∂ℑ
+∂∂ℑ
=x
rxr
xxx ddM
MddM
Mddf Thigh ψ
xxxx
x δδ )()( highhighhigh ff
ddf −+
≈
AEROSPACE COMPUTATIONAL DESIGN LABORATORY17
Optimisation Results
• Multifidelity Results:
• Single-fidelity Results:
4.192.8614.0Standard deviation14.7 (-82%)12.9 (-84%)81.5 (-)Average high-fidelity function calls
Calibration ApproachFirst-Order TRSQPflow(x)=panel method
21.861.214.0Standard deviation64.2 (-21%)91.1 (+12%)81.5 (-)Average high-fidelity function calls
Calibration ApproachFirst-Order TRSQPflow(x)=0
Momentum AdjointPressure
AEROSPACE COMPUTATIONAL DESIGN LABORATORY18
Conclusions
• Shown that adjoint methods can provide inexpensive gradient estimates for aerodynamic and structural design problems.
• Presented a gradient-based multifidelityoptimisation method using Bayesian model calibration.– Proven convergence– Shown comparable or better performance than other
multifidelity methods– Recommended the method for problems with few
design variables and “poor” low-fidelity models
AEROSPACE COMPUTATIONAL DESIGN LABORATORY19
Acknowledgements
• The authors gratefully acknowledge support from NASA Langley Research Center contract NNL07AA33C technical monitor Natalia Alexandrov.
• A National Science Foundation graduate research fellowship.
AEROSPACE COMPUTATIONAL DESIGN LABORATORY20
Questions?