NASA/TM–2013–Seedling Phase 1 Final Report

Adaptive Shape Parameterizationfor Aerodynamic Design

George R. Anderson

Stanford University, Stanford, CA

Michael J. Aftosmis

NASA Ames, NASA Advanced Supercomputing Division, Mo↵ett Field, CA

September 2013

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NASA/TM–2013–Seedling Phase 1 Final Report

Adaptive Shape Parameterizationfor Aerodynamic Design

George R. Anderson

Stanford University, Stanford, CA

Michael J. Aftosmis

NASA Ames, NASA Advanced Supercomputing Division, Mo↵ett Field, CA

National Aeronautics andSpace Administration

Ames Research CenterMo↵ett Field, CA 94035

September 2013

Acknowledgments

Funding for this project was fully provided by a NASA ARMD Seedling Fund grant.The authors also thank Marian Nemec for many helpful discussions and for hisdevelopment of the static-parameterization design framework used in this study.

The use of trademarks or names of manufacturers in this report is for accurate reporting anddoes not constitute an o�cal endorsement, either expressed or implied, of such products ormanufacturers by the National Aeronautics and Space Administration.

Available from:

NASA Center for AeroSpace Information7115 Standard Drive

Hanover, MD 21076-1320443-757-5802

Abstract

This report concerns research performed in fulfillment of Phase 1 of anongoing NASA Seedling Fund grant. The overall goal is to develop anaerodynamic shape optimization framework that supports automatedshape parameterization. The four objectives for the work were to maturea constraint-based deformation technique, to develop the basic frame-work necessary to perform automated parameter refinement, to deter-mine an importance indicator that prioritizes candidate design variables,and to develop an e�cient refinement strategy. All of these objectiveshave been met to the degree appropriate for Phase 1. We discuss eachin detail in this report.

Nomenclature

P Vector of design variable definitions/locationsX Vector of design variable valuesJ Objective function@J@X Objective gradients to design variablesC Computational cost� Ratio of cost of gradient computation to cost of PDE solution.d Optimizer search directionI Importance indicatorr Trigger reduction factorw Averaging window

PDE Solution

↵ Angle of attackM Discrete PDE meshQ Discrete PDE solutionS Discrete tesselated surfaceM Mach number Adjoint solution

Subscripts

cand Candidate design variablesdv Design variablesgrad Objective gradient computationiter Design iterationopt Optimizationref Refinement

1

1 Introduction

Aerodynamic design is gradually transitioning from point analysistowards simulation-guided shape optimization, which seeks to im-

prove an initial design, using numerical analysis to intelligently drive theshape changes. The key to e↵ective aerospace design optimization is theshape parameterization, the geometric design variables that control thevehicle’s shape and govern the range of possible configurations. Thiswork addresses the pressing need for shape parameterization techniquesthat give the designer more flexibility and control, while also o✏oadingand automating as much work as possible.

1.1 Traditional Shape Optimization

From the beginning, shape optimization has made great promises: fasterdesign improvement (in wall-clock time), more automation, reduced user-in-the-loop time, and minimal bias while exploring unfamiliar designs.Unfortunately, current approaches fall short of the mark in each of theseareas.

An aerospace designer’s task ought to be to “ask the right questions”,which entails choosing an objective and imposing design constraints. Inmost current approaches, however, the designer spends excessive time onthe mechanics of the tools: selecting shape design variables, establishingrobust bounds on them, and other tasks which are not directly relevant todesign. Despite the promise of reduced bias, it remains strongly presentin the designer’s choice of design variables. The optimizer can workonly within the range of reachable shapes. The design space may beexcessively restrictive (or excessively open-ended) and is highly unlikelyto include the optimal continuous shape.

User-supplied constraints

4 parameters

10 parameters

22 parameters

Parameter Refinement

Figure 1: Progressive shape parame-terization

Regarding the promise ofaccelerated design improvement,current methods meet with mixedsuccess. While at first, the de-sign can be quickly improved, thestatic design space prevents fur-ther improvement. When shapeparameters are chosen before de-sign begins, they can restrict thedesign space in irrelevant ways,needlessly hindering the discov-ery of optimal designs. A fixedparameterization cannot adapt tocapitalize on new insights as theyemerge during design.

2

1.2 Variable Shape Control

Most current methods use a static design space that does not evolveas the design progresses. If these parameters are too coarse, designimprovement is severely restricted. If they are too refined while far fromoptimal, then navigation of the nonlinear design space is ine�cient. Inthis work we examine the use of a variable design space that can beadapted as necessary, as illustrated in Figure 1. In practice, this typicallymeans that a coarse parameterization is used when far from the optimaldesign, and that the resolution is gradually increased when approachingthe optimum.

102

101

103

100 10020 40 60 80Cost (equivalent number of flow solves)

Shap

e M

atch

ing

Obj

ectiv

eFixed

(14 DVs)

Fixed(30 DVs)

Progressive (14 → 30)

refinement

Figure 2: Objective convergence for staticvs. progressive parameterizations

The primary benefit ofa variable shape control ap-proach is that the designer isno longer burdened with pre-dicting how many and whichdesign variables will be ap-propriate for a particular de-sign problem. It also radicallyreduces user setup time, asdesign variables are automat-ically created and bounded.Furthermore, it constitutes asingle hierarchical process thatprogressively drives the shapetowards the true continuousoptimal shape, instead of anapproximation in an arbitraryfixed design space.

Although our primary goal is to streamline and automate designtools, using a variable approach is also motivated by a growing body ofevidence that substantial design acceleration can be achieved by using ahierarchical parameterization approach.1–4 Figure 2 illustrates the po-tential computational acceleration. Each curve shows the evolution ofthe objective using a di↵erent parameterization technique (steeper slopesindicate more e�cient optimization). Using the coarse 14-DV parame-terization initially achieves the fastest design improvement, as the spaceis simpler to navigate, but the improvement quickly bottoms out. Thefiner parameterizations can reach superior designs, but do so ine�cientlybecause of the large number of design variables.

In progressive parameterization we start with a coarse design spacethat can be quickly evolved, and then add new degrees of freedom whennecessary. This captures the best of both worlds, allowing e�cient designimprovement early on, while still retaining the ability to ultimately reacha superior design that is accessible only in more refined design spaces.

3

1.3 Constraint-Based Deformation

Using adaptive parameterization places greater emphasis on the structureof the shape parameterization method. One key objective of this workwas to develop and mature a tool that allows design variables to beplaced anywhere, and automatically. Our approach is called constraint-

based deformation.Geometric constraints are truly the only thing known a priori in de-

sign. Constraint-based deformation treats constraints exactly like designvariables, so that only shapes satisfying the geometric constraints areconsidered during optimization. This view represents a fundamentallydi↵erent approach to shape parameterization that unifies the concepts ofdesign variables and geometric constraints. This di↵ers from the conven-tional approach where constraints are an afterthought, enforced approx-imately via penalty functions or by awkwardly linking design variablestogether to preclude undesirable shape changes. This new techniquedetermines both shape parameters and constraints at design time andguarantees exact satisfaction of geometric constraints. Flexibility can beadded to the model as the design evolves, enhancing its ability to achievethe designers objective. Some examples of constraint-based deformationare shown in Figure 3.

r1 h

b1

b2

r2

γ

θ

ctip

Λcroot

Down the span

Top-downFront view

Airfoil sections

Figure 3: Parametric wingtip deformation using constraint-based deformation.

1.4 A New Role for the Adjoint

The second key enabling technology for our approach is the incorporationof sensitivity information provided by the adjoint equations. Since itsintroduction to the aerodynamic community 25 years ago,5 the adjointmethod has revolutionized gradient-based shape optimization, renderingit computationally feasible to optimize on very large numbers of design

4

variables. The adjoint approach allows all of the objective gradients tobe computed for a fixed cost of roughly two PDE solutions, instead ofthe 2N solutions required under a finite di↵erence formulation.

In this work, we use the adjoint solution for a novel purpose. Theadjoint in fact encodes much more information than traditional para-metric shape optimization makes use of, and this information can beextracted at trivial cost. If used correctly, it can accelerate the rate ofdesign improvement.

Specifically, we use the adjoint to compute gradients not only ofexisting shape design variables, but also of potential design variables. Wecan then determine whether the potential design variables would drivethe design forward more e↵ectively, and if so, inject them into the activeset of design variables. Di↵erent design problems may call for di↵erentshape control. Our goal is to discover the necessary shape control inthe process of optimization. Thus we expand the traditional role of theadjoint from e�ciently computing objective gradients to designing the

design space itself.

1.5 Organization

The following section provides an algorithm for optimization with adap-tive shape parameterization. In Section 3 we discuss the shape param-eterization methods used for this study. We then discuss a mixed mod-eling/observational approach to cost-benefit analysis to assess the e�-ciency of di↵erent refinement strategies. In Sections 6-8 we examine eachof the essential components of the refinement strategy in detail, drawingconclusions from randomized numerical experiments.

2 Adaptive Parameterization Algorithm

2.1 Standard Shape Optimization Framework

Adjoint-based parametric shape optimization frameworks typically fol-low the iterative loop outlined in Function 1. First, a discrete tesselatedsurface S is generated by a geometry modeler or deformer, based on theinitial shape parameter values X. Next, the solution domain is meshedand the PDE is solved (for our purposes, the fluid flow equations), whichenables evaluation of the objective function J (e.g. drag or sonic boom).Next, the adjoint equations of the PDE are solved, which allows rapidcomputation of the objective gradients @J

@X to each design variable. Fi-nally a gradient-based optimizer determines an update to the designvariables X, and the loop is continued.

For this study, we use an existing adjoint-based aerodynamic designframework6 that uses an embedded-boundary Cartesian mesh methodfor inviscid flow solutions. We also leverage the framework’s adjoint for-mulation for computing gradients to prioritize candidate design variables

5

Function 1: OptimizeStatic(P,X0

, Stop(·))Parametric Geometry Engine

PDE Solver Functions

Input: Shape parameters P with initial values X0

,stopping condition Stop(J , @J

@X)Result: Optimized surface S, adjoint solution repeat

S � GenerateSurface(P,X)M � GeneratePDEMesh(S)Q � SolvePDE(M)J � ComputeObjective(Q,S) � SolveAdjoint(Q,M)foreach P

i

, Xi

in P,X do@S@Xi � ComputeShapeDerivative(P

i

, Xi

)@J@Xi � ProjectGradient( , @S

@Xi)

end

X � NextDesign(X, @J@X) // Gradient-based optimizer

until Stop(J , @J@X)

when refining the design space. Optimization can be handled with anyblack-box gradient-based optimizer; for this study we use SNOPT.7

2.2 Parametric Geometry Generation

The functionGenerateSurface(·) represents an arbitrary geometry mod-eler (e.g. a CAD engine or surface deformer) that generates a tesselatedsurface from a set of design parameters. The design framework describedabove interacts with arbitrary geometry modelers, using an XML-basedprotocol for communication.6 The geometry parameterization compo-nents are discussed in more detail in Section 3.

2.3 Adaptive Optimization

Feature/Constraint

Existing Parameter

Candidate Parameter

Refinement Indicator

1.5x growthTop Bottom

L0

L1

L2

Figure 4: Prioritized candidates forparameterization refinement

Function 1 gives the process foroptimizing in a static design spacedefined by a fixed set of designvariables. Our adaptive optimiza-tion algorithm invokes a series ofcalls to this design framework,refining the parameterization be-tween each call. Algorithm 2shows a basic strategy for opti-mization with adaptive shape pa-rameterization. Besides the callto the standard design framework,

6

there are a four new functions related to parameterization refinement.The first function, GetCandidateDesignV ariables(·) generates a list ofpossible new shape parameters. This function depends on the particularparameterization technique being used, and is discussed in more detailin Section 3.

The remaining three adaptation functions are independent of thegeometry modeler. The Trigger(·) determines when to initiate the nextrefinement level. ComputeImportanceIndicator(·) determines which ofthe design variables are most important and builds a “priority queue”of candidates as illustrated in Figure 4. Finally, SetPace(·) determineshow many of these candidates to add, e↵ectively governing the rate ofshape parameter growth. These three functions are discussed in detailin Sections 6-8.

Algorithm 2: Adaptive Parameterization OptimizationParametric Geometry Engine

Refinement Strategy

Input: Initial shape parameters P0

with values X0

Result: Optimized surface SP � P

0

,X � X0

repeatX, � OptimizeStatic(P,X, T rigger())P

cand

,Xcand

� GetCandidateDesignV ariables(P,X)foreach P

i

, Xi

in Pcand

,Xcand

doIi

� ComputeImportanceIndicator(Pi

, Xi

, )endSortByIndicator(P,X, I)n � SetPace(I)P � P [P

cand

[1 . . . n]X � X [X

cand

[1 . . . n]

until overall stopping condition met

2.4 Uniform Refinement

A uniform refinement strategy is a special simplified case of Algorithm 2,where all candidate design variables are added, rendering the indicatorand pacing irrelevant. To implement a uniform refinement strategy, wesimply bypass the computation of indicators in Algorithm 2 and add allof the new candidates. A uniform refinement strategy meets the goals ofautomation and consistency with the continuous optimal solution, andit is also easier to implement. However, it has e�ciency implications, asthe number of design variables will grow much faster than in an adaptivescheme.

7

3 Shape Parameterization

Adaptive parameterization is theoretically compatible with constructivemodelers (e.g. CAD or in-house geometry tools) or discrete geometry de-formers (e.g. FFD, bump functions, constraint-based deformation, etc.),and we strive for modularity with respect to the shape parameterization.To function with a standard design framework, a geometry modeler mustprovide a list of available design variables with minimum and maximumbounds, and subsequently generate surfaces on demand for any set ofparameter values requested by the optimizer.a

To function with an adaptive parameterization framework, the mod-eler must additionally have systematic methods for

• Generating and bounding new candidate design variables.

• Transferring constraints between refinement levels.

The method for generating candidates need not be strictly hierarchical(or “nested”), but it should at least allow substantial refinement depth.

Besides these strict requirements, several authors have discussed de-sirable qualities for a shape parameterization technique, such as automa-tion, consistency, smoothness, compactness, e↵ectiveness, robustness,and intuitiveness.8–12 Using an adaptive approach places greater em-phasis on the structure of the parameterization scheme, which can besummarized in the following desirable characteristics:

1. Hierarchical organization of parameters.

2. Refinement can be localized to particular regions of the shape

3. Unlimited refinement depth

4. Exact shape preservation when transferring between levels.

Many geometry modelers that are satisfactory for fixed parameteroptimization do not have all these characteristics. For example, with afew exceptions, most constructive modelers (such as CAD packages) donot preserve the shape exactly when changing parameterizations, whichmeans ground (and thus time) would be lost when refining the designspace. Discrete geometry deformation techniques, on the other hand,naturally preserve the shape exactly.

In the following sections we discuss the two naturally hierarchicaland shape-preserving parametric geometry deformation methods used inthis study.

aIdeally, shape derivatives are also provided, but this is not a strict necessity, asshape derivatives can be computed automatically by finite di↵erencing.

8

3.1 Rigid Wing Section Transformation

For wing planform design, we use an analytic deformer that applies rigidtransformations to arbitrary spanwise stations and lofts the deformationbetween the stations. Rigid transformations include translation (sweepand span), rotation (twist and dihedral) and scaling (thickness-to-chordratio). For this study, we restrict the deformation to twisting only andwe linearly interpolate the twist between stations, a deliberate choicethat simplifies the randomized testing discussed in Section 5.1.

Candidate design variables are generated by subdividing the spanwisestations. If there are N spanwise stations, we can generate N � 1 candi-dates at the midpoints between adjacent sections, as shown in Function3. However, we could also look “deeper” by generating 2i � 1 evenlyspaced candidates between each pair of adjacent stations. A yet moreadvanced approach could allow uneven spacing, but we defer this untila later study.

Function 3: GetCandidateDesignV ariables(·)Input: Current shape parameters P with values XResult: Candidate shape parameters P

cand

with values Xcand

Pcand

� ;,Xcand

� ;SpatiallySortParameters(P,X)foreach pair of adjacent parameters (P

i

, Pj

) in P doPnew

� midpoint of Pi

and Pj

Xnew

� Interpolate (Xi

, Xj

)P

cand

� Pcand

+ Pnew

Xcand

� Xcand

+Xnew

end

3.2 Constraint-Based Deformation

In references13,14 we developed a constraint-based approach to shapeparameterization for arbitrary aerospace configurations. Geometric con-straints are managed systematically, ensuring that they are satisfied atevery design iteration. The technique works on arbitrary 3D aerospaceconfigurations, as demonstrated in Figure 3, but for the purposes of thisstudy we use it to perform wing section design, as illustrated in Figure 1.Generation of new candidates is handled similarly to the wing planformparameterization, given in Function 3.

9

4 Designing an Adaptive Strategy

It is worth pausing for a moment to note that by simply using progres-sive parameterization in any form, we have already met several of ourgoals. Even a uniform refinement strategy without any notion of local-ized adaptation is automated and has the ability to reach the continuoussolution. It also accelerates design improvement by avoiding excessivelyfine parameterizations early in design.

The remainder of this study is focused on process e�ciency, whichrequires much closer attention to the details of the refinement strategy.Our goal is to find a strategy (consisting of a trigger, an importanceindicator, and rate of variable introduction) that maximizes the designimprovement�J relative to the cost of the optimization C

opt

. Naturally,no single refinement strategy will be perfect for all design problems. Weare looking for the essential ingredients of a robust strategy that worksconsistently and e�ciently over a broad spectrum of aerodynamic designproblems.

4.1 Measuring Design Improvement

The design improvement is simply the change in the objective function�J . The rate of design improvement cannot be rigorously modeled. Itmust be measured through numerical experiments, as it strongly dependson the e�cacy of the current shape parameters at reducing the arbitraryobjective function (e.g. drag vs. sonic boom vs. shape matching).

4.2 Cost-Benefit Trade

The total cost of shape optimization on a static parameterization (or onone level of a progressive parameterization) is

Copt

= Niter

Citer

+ Cref

(1)

where Niter

is the number of design iterations, Citer

is the cost per it-eration, and C

ref

is the cost of refining the parameterization. In thefollowing sections, we model the costs C

iter

and Cref

for typical aerody-namic problems. While we expect that N

iter

⇠ O (Ndv

) when using aquasi-Newton optimizer such as BFGS, we cannot accurately predict itdue to the nonlinearity of the design space. Our approach is thereforeto observe how N

iter

behaves statistically in numerical experiments.

4.2.1 Cost per Design Iteration, Citer

One design iteration is comprised of three significant actions: evaluat-ing the objective (typically by solving a PDE), computing adjoint so-lution(s), and projecting the surface objective gradient into each of thedesign variables. We will assume that there is only one adjoint solve

10

(i.e. one objective and no aerodynamic constraints) and that the costof the adjoint solution is equal to that of the flow solution, which is areasonable approximation in most cases. Then the cost C

iter

of a designiteration is

Citer

= 2CPDE

+Ndv

Cgrad

For simplicity, we assume perfect parallel e�ciency. We also as-sume that the cost of a flow or adjoint solution remains roughly constantthroughout the optimization.b Then we can divide out the constant PDEsolution cost to obtain the non-dimensional cost per design iteration

Citer

= 2 +Ndv

� (2)

where � =Cgrad

CPDEindicates the relative cost of a gradient projection to

a PDE solution. CPDE

depends on the PDE solution fidelity (scalingroughly linearly with the number of unknowns in the discrete solutionwhen using a multigrid solver). C

grad

depends on the speed of the geom-etry modeler and the cost of a gradient projection. In many situations,the PDE solution is at least an order of magnitude more expensive thanthe geometry generation and gradient projection, so � ⌧ 1. Neverthe-less, the second term in Equation 2 can still become significant whenthere are large numbers of design variables.

4.2.2 Cost of Refining the Parameterization, Cref

Function ComputeImportanceIndicator(·) in Algorithm 2 dominatesthe cost of design space refinement. In Section 7, we show that thisprocess involves the projection of the adjoint solution onto the shapesensitivities with respect to the design variable, which has a nondimen-sional cost of roughly �, making the total refinement cost for all N

cand

candidatesCref

= Ncand

� (3)

Equation 3 highlights the (intuitive) cost tradeo↵. Considering morecandidate design variables naturally increases the chances of finding ef-fective design variables, but it also linearly increases the overhead asso-ciated with refinement.

4.2.3 Total Optimization Cost, Copt

Substituting the cost models (Equations 2 and 3) into Equation 1, weobtain a model for the total nondimensional cost of an optimization ona fixed set of parameters:

Copt

= Niter

(2 +Ndv

�) +Ncand

� (4)

bThis would be inaccurate when using progressively refined flow meshes, wherecoarse mesh solutions are used early in design and fine mesh solutions only near theoptimum.

11

By choosing a particular pacing strategy we are implicitly prescribingN

dv

and Ncand

, leaving Niter

to be determined experimentally.Finally, our goal is to maximize the design improvement relative to

the cost, at every parameter refinement level:

�JN

iter

(2 +Ndv

�) +Ncand

�(5)

Equation 5 allows us to make the following general statements. Forrelatively expensive PDE solutions (where � ⌧ 1), the most e�cientstrategy is the one that maximizes �J

Niter. This suggests that considering

more candidates and carefully deciding which to add is time well-spent.For relatively inexpensive PDE solutions (� 6⌧ 1), the most e�cientstrategy is the one that maximizes �J

NiterNdv+Ncand. In this case, the

refinement overhead is more significant, as is the total number of designvariables.

5 Numerical Experiments

Our objective for the remainder of this study is to evaluate various ap-proaches to each component of the refinement strategy (trigger, indicatorand pacing), drawing conclusions from numerical experiments. In mostprevious studies on adaptive refinement, broad conclusions are drawnfrom a handful of specific model problems, which may not be represen-tative even of that particular type of problem (and much less of a broadclass of problems). Single cases like these are more likely to lead to con-clusions that do not hold in general. In this work we examine large setsof randomized cases to reduce the possibility of error. The goal is to drawstatistically significant conclusions about mean performance. While ran-dom cases are not precisely representative of real design problems, theydo provide a notion of expected performance on aerodynamic problems.

5.1 Test Case

2

Baseline

Random Twist Stations

Initial generators

Target generators

22 24 26 28 302.4

2.6

2.8

3

3.2

3.4

3.6

Twist axis

θ

Figure 5: Example of a random set oftwist stations

Our test problem is wing twistmatching. The geometry is theswept and tapered transport wingin Figure 5. Twisting is performedby the rigid wing deformer de-scribed in Section 3.1. The goal isto add twist stations where neces-sary and gradually match a knowntarget design. For each case werandomize the initial and targetshapes, as well as the flight condi-tions, in order to evaluate the abil-

12

ity of various refinement strategies to drive an arbitrary starting shapeto an arbitrary target shape.c

Twist stations are initially placed at the root and tip of the wing.For each case, 8 more twist stations are then placed at randomly chosenspanwise locations and randomly perturbed, which generates an initialshape. This process is repeated to generate a target shape, using a dif-

ferent set of 8 random stations and perturbations. Because the spanwiseinterpolation for the rigid deformer is linear, the target is exactly at-tainable, but only if the adaptation process manages to capture all 16parameters (8 each) used to generate the initial and target shapes.

5.2 Objective Functionals

We consider two inverse design functionals. The first is a geometric shapematching functional defined as the sum of squared distances betweencorresponding vertices:

Jgeom

=nvertsX

i=1

(vi

� v⇤i

)2 (6)

where vi

are the current vertex coordinates and v⇤i

are the correspondingtarget vertex coordinates. Second, we consider an aerodynamic inversedesign functional defined as the squared deviation from a target pressureprofile:

Jaero

=nvertsX

i=1

�Cp

� C⇤p

�2

i

(7)

where the target pressure coe�cient C⇤p

is specified at each vertex on thediscrete surface.

Both functionals have a minimum value of zero when the matchingis exact. The purely geometric functional is a much simpler objective, asit eliminates the severe nonlinearities associated with the flow equationsand mesh discretization. The aerodynamic functional is more represen-tative of realistic aerodynamic shape optimization problems. However,it is much more expensive to compute, as it involves the solution of theflow and adjoint equations.

5.3 Flight Conditions

For the aerodynamic objective function, in addition to randomizing theparameterization, we also impose random subsonic flight conditions withinthe Mach range 0.3 M 0.4 and angle of attack range 0� ↵ 2�.The flow and adjoint solver settings are identical for all cases, includinggenerating the target pressure profiles.

cNaturally, arbitrary only within this class of shape parameterizations.

13

5.4 Statistics

Cost (equivalent number of flow solves)N

orm

aliz

ed O

bjec

tive

100

10-2

10-4

10-6

40 120 160800

Mean

1-σ

1 random case

Figure 6: Example of a randomized study withcomputed statistics.

Each random study pro-duces a series of indepen-dent objective histories,like the example shown inFigure 6. Each thin linecorresponds to a di↵erentdesign problem. The ob-jective functions are in-dependently normalized sothat each history starts ata value of 1. We do this tohighlight the order of mag-nitude decrease in the ob-jective.

The x-axis indicatesthe total computationalcost estimated by Equa-tion 4. We compute themean (bold line) and standard deviation (shaded region) of the objec-tive histories at regular cost intervals along the x-axisd. In all subsequentplots, the individual objective histories are not shown, except when di-rectly relevant.

6 Calibrating the Trigger

Cost (equivalent number of flow solves)

102

101

103

100 20 40 60 80

Shap

e M

atch

ing

Obj

ectiv

e 14 DVs

Late Trigger

Early Trigger

Refinement to 30 DVs

Figure 7: E↵ects of triggering at dif-ferent times.

The Trigger(·) function in Algo-rithm 2 is a stopping-conditionthat terminates the optimizationon the current set of design vari-ables and initiates a parameterrefinement. The trigger is criti-cal for e�ciency, as demonstratedin Figure 7. The two branchesshow the performance impact oftriggering at di↵erent times, forthe same setup used in Figure 2.Over-optimizing on an immatureparameterization leads to sluggishdesign improvement and is evi-dently a poor investment of re-sources. This observation has also

dFor attainable inverse design problems like this one, the statistics are computedin log-space.

14

been made by other authors1 and similar ine�ciencies have been demon-strated when over-optimizing on progressive PDE meshes.15

In their work on adaptive refinement, Han and Zingg2 advocate theopposite approach of allowing the optimization to converge at each level,in order to maximize design improvement under the current set of designvariables. In this work, however, our goal is rapid design improvement,not to find a “minimal set” of design variables. As the cost of computinggradients for additional design variables is quite low under an adjointformulatione, it makes more sense to terminate the optimization as soonas the rate of design improvement starts to significantly taper o↵.

6.1 Detecting Tapering Design Improvement

The most direct approach to detecting diminishing design improvementis to continuously monitor the cost-slope of the objective convergence,given in Equation 5. The cost-slope must be evaluated only at majorsearch iterations, which is monotonically decreasing. Line searches aregenerally non-monotone and cannot be interpreted as reasonable infor-mation about slopes.

As shown in Function 4, we terminate the optimization when thecost-slopef falls below some fraction of the maximum cost-slope thathas occurred so far. This normalization by the maximum cost-slope isessential, as it accounts for the widely di↵ering scales that can be presentin di↵erent objective functions. (For example, a drag functional may beO �

10�2

�while a functional based on operating range may be O �

105�.)

Function 4: CostSlopeTrigger(·)Input: Objective history J (d) w.r.t. search direction d,window w, slope reduction factor r < 1Result: True = “Terminate” or False = “Continue”

s = ComputeAveragedSlopes(J (d), w)

if⇣

s[�1]

max(s)

⌘< r then return True

else return False

Function 5 outlines an alternative approach: monitoring the objectivegradients to the design variables, ||@J

@X ||, which converge to zero as thedesign approaches a local optimum. In convex regions of the designspace, the gradient roughly indicates distance from optimality. Better

eUnder a finite-di↵erence optimization framework (i.e. without the adjoint), thecost of each extra gradient is two flow solutions. In that case, allowing more conver-gence on fewer design variables might be more e�cient.

fFor attainable inverse design problems, we measure the cost-slope in log-space tobetter reflect the problem.

15

yet, low gradient values typically precede flattening design improvement,allowing pre-emptive termination.

Function 5: GradientTrigger(·)Input: Objective gradient history @J

@Xi(d) w.r.t. search direction d

and design variable i, window w, reduction factor r < 1Result: True = “Terminate” or False = “Continue”

G(d) � ComputeAveragedGradientNorms( @J@Xi

(d), w)

if⇣G[�1]

G[0]

< r⌘then return True

else return False

More aggressive triggering strategies could also be adopted, whereinrefinement is initiated well before the convergence begins to taper. Thiscould be done by refining after a predetermined number of search direc-tions, perhaps by linking the number of search directions to the numberof design variables to reflect the expected rate of convergence. This ap-proach, however, would require prior, problem-specific knowledge aboutthe rate of design improvement, which would defeat the purpose of ageneral and automated optimization algorithm.

6.2 Handling Non-smoothness

Gradient Triggerr = 10%r = 1%r = 0.1%

Cost (equivalent number of flow solves)

Geo

met

ric O

bjec

tive

(Nor

mal

ized

) 100

10-1

50 150 2001000

10-2

10-3

10-4

10-5

250

r = 5%

Slope Trigger

r = 10%r = 25%

Figure 8: Performance of di↵erenttriggering strategies on the geometricobjective function. Each curve showsmean behavior over 10 random trials.(� = 0.01, 1.5⇥ growth)

The objective-cost slopes in Func-tion 4 and the radients in Func-tion 5 can be non-smooth, whichcan cause false triggering. We al-leviate this problem by using run-ning averages over a small win-dow. This helps prevent prema-ture triggering, but it has the sidee↵ect of causing a lag the size ofthe window, which can delay thetrigger for a few search directions.Therefore the window should beas small as possible. A window ofwidth 2 proved robust enough forour studies so far.

6.3 Experimental Results

Figure 8 shows the performanceof the two triggering strategies(Functions 4 and 5) on the geometric objective functional given by Equa-tion 6. Three reduction factors are examined for each approach. There

16

are ten individual randomly generated cases (not shown), as explainedin Section 5. The same ten cases were used to test each triggering strat-egy. The slope-based triggering strategies show very little sensitivity tothe reduction factor r in Function 4, indicating a fairly robust approach.This is probably because the slope reduction tends to be sudden for thegeometric objective function. The gradient triggers are much more sen-sitive to the reduction factor, but still seemed to perform reasonably wellfor a good choice of r. Performance of the triggers on the aerodynamicinverse design functional is currently under investigation.

6.4 False Triggers

Both triggers assume that diminishing design improvement indicates thatthe optimizer has nearly fully exploited the design space, which shouldnow be expanded. But this assumption is not always correct: the op-timizer could be simply navigating a particularly di�cult region of thedesign space, after which faster design improvement would continue. Itis impossible to distinguish between these two cases without additionalinformation, such as prior knowledge that a superior design is attainable.But an automated system cannot depend on problem-dependent a priori

information. We therefore err on the side of occasionally triggering earlyin order to avoid the much more significant penalties associated withconsistently delaying refinement.

7 The Importance Indicator

The refinement indicator is an estimate of the relative importance of eachcandidate design variable. Importance is di�cult to properly estimate,because of the inherent nonlinearity of aerodynamic design.g The im-portance estimate is necessarily only locally meaningful. Nevertheless,as there is no globally valid importance indicator until the problem issolved, local information is the best cue available.

The most significant information at hand is the local objective gradi-ent to each candidate design variable @J

@X , which we can compute for neg-ligible additional cost as we have already computed an adjoint solution(see Section 4.2.2). Function 6 gives a method for computing the refine-ment indicator that values high objective gradients. To compute gradi-ents to candidate design variables, we leverage the ProjectGradient(·)function, which is part of the static-parameterization design framework.

gStemming from nonlinearities in the geometry, in the shape deformation, in theflow mesh discretization, and in the flow equations themselves.

17

Function 6: ComputeImportanceIndicator(P,X, )

Input: Candidate shape parameter P with value X,adjoint solution Result: Importance indicator I@S@X

� ComputeShapeDerivative(P,X)@J@X

� ProjectGradient( , @S@X

)

I � || @J@X

||

8 Pacing Introduction of New Design Variables

After computing the importance indicators of the candidate parametersand sorting them, we have a prioritized list of design variables to add,as illustrated in Figure 4. The SetPace(·) function in Algorithm 2 thendetermines how many of the candidates to add to the optimization.

There are two factors that can slow down an optimization. If thedesign space is insu�ciently flexible, optimization will quickly stagnate.But if the design space has too much freedom, it is more di�cult tonavigate. Both situations cause ine�cient design improvement. Select-ing a pacing involves finding an e↵ective balance between flexibility andnavigability.

At each refinement, growth in the number of design variables can bespecified either in absolute terms (e.g. “add 10 design variables”) or inrelative terms (“increase the number of design variables by 50%”). Forthis initial study, we use preset growth ratios. We defer for future studiesthe evaluation of more sophisticated strategies, such as performing acost-benefit estimation, or even removing some design variables fromthe active set for e�ciency.

8.1 Experimental Results

Figure 9 compares the performance of various growth factors from 1.25�2⇥ using the geometric objective functional (Equation 6). The resultsclearly demonstrate that the growth rate has a critical impact on e�-ciency. There is a factor of two di↵erence in performance between growthrates of 1.25⇥ and 2⇥. There is also a clear trend favoring faster intro-duction of design variables, with 2⇥ refinement performing the best. Be-fore drawing sweeping conclusions, however, we note that at least threefactors could impact this result.

The first is that, although this is a 3D geometry, the shape parame-terization is only 1D: the spanwise direction is the only dimension for re-finement. In future work, we plan to consider 2D parameterizations thatcombine spanwise refinement of planform design variables with stream-wise refinement of airfoil design variables. In that case, the uniformgrowth rate would be approximately 4⇥ instead of 2⇥.

18

Cost (equivalent number of flow solves)

Geo

met

ric O

bjec

tive

(Nor

mal

ized

) 100

10-1

50 150 2001000

10-2

10-3

10-4

10-5

Growth Rate1.25x1.5x1.75x2x

Figure 9: Performance of di↵erent growth rates on the geometric objectivefunction. Each curve shows mean behavior over 10 random trials. (� = 0.01,slope-based trigger, r = 0.25)

Another possible factor is the relative simplicity of the geometricobjective functional, which allows rapid and reliable design improvement,regardless of the number of design variables. We can simulate this byassuming thatN

iter

in Equation 5 is proportional to the number of designvariables, which is the expected behavior for most problems. In this case,the design improvement slope would penalize the introduction of moredesign variables. A third consideration is that we are starting with aminimal number of design variables (1 in this case).

On the other hand, if fast growth rates do indeed prove to be gen-erally superior, then we are not limited even by uniform refinement. Ifmultiple candidate design variables are generated per interval (recall thediscussion in Section 3.1), then we can consider growth rates in excessof 2⇥ (in excess of 4⇥ in 2D). Experimental studies to better elucidatethe tradeo↵ are still ongoing.

19

9 Summary and Conclusions

We discussed how a uniform refinement strategy is an excellent stepping-stone towards fully adaptive parameterization. Uniform refinement pro-vides automation, consistency and substantial design acceleration, andimplementation is simpler than a fully adaptive framework. To high-light the savings in user time, consider the following changes to a typicaldesigner’s workflow in setting up an optimization:

1. Provide initial design.

2. Define objective function, constraints, PDE solver settings.

3. Providedetailedminimal

parameterization.

4. Set bounds onall detailed

minimal set of

parameters

5. Repeat steps 3 and 4 iteratively to incorporate new insights.

What was previously a user -driven iterative loop of optimizations canbe handled automatically using an progressive parameterization process,freeing the designer to focus on the essentials of posing the right problem.

We then focused on improving the process e�ciency by adopting anadaptive approach to parameterization. Although studies are still ongo-ing, the experimental results above enable us to make several statementsabout the e�ciency of adaptive schemes:

• A trigger is essential for e�ciency, and earlier triggers outperformlate triggers (see Figure 7).

• The growth rate is also critical for e�ciency, and faster growthrates tend to outperform slow ones (see Figure 9).

• In practice, we have found that it is more e�cient to start with amoderate degree of shape control, rather than the bare minimumnumber of design variables.h

We gave examples of using two di↵erent geometry modelers withthe adaptive framework. In Section 3 we discussed the developmentnecessary to integrate arbitrary geometry modelers with the framework:

• A system for generating candidate design variables

• A method for automatically setting bounds on parameters

hThis can still be handled automatically, by uniformly refining the parameterizationone or more times before beginning optimization.

20

9.1 Ongoing Research

Our next major goal is to apply a progressive approach to parameteriza-tions of higher dimensionality, such as simultaneous design (and refine-ment) of both planform design variables and airfoil design variables.

Regarding e�ciency, we plan to examine several modifications to thestrategy:

• Generating more candidate design variables by searching “deeper”,using multiple levels of binary refinement.

• Trigger based on cost-benefit estimate.

• Importance indicator:

– Examining candidate parameterizations, instead of single can-didate parameters.

– Using Hessian approximation to guide selection.

• Growth rate:

– Setting growth rate based on cost-benefit analysis

– Removing design parameters that have low continuing impor-tance.

• Transferring Hessian approximation between refinement levels tojumpstart next optimization.

In this study we exclusively focused on discrete parameter refinement,analogous to h-refinement in PDE solvers. In future work, we hopeto examine a continuous approach, where we seek to optimally set thelocation of each design parameter, paralleling the PDE solver techniqueof adaptation through re-distribution or r -refinement.

References1 Duvigneau, R., “Adaptive Parameterization using Free-Form Deformation for Aero-dynamic Shape Optimization,” Tech. Rep. 5949, INRIA, 2006.

2 Han, X. and Zingg, D., “An adaptive geometry parametrization for aerodynamicshape optimization,” 2013, pp. 1–23.

3 Olhofer, M., Jin, Y., and Sendho↵, B., “Adaptive Encoding for Aerodynamic ShapeOptimization using Evolution Strategies,” Congress on Evolutionary Computation,Korea, 2001, pp. 576–583.

4 Hwang, J. T. and Martins, J. R. R. A., “A Dynamic Parametrization Scheme forShape Optimization Using Quasi-Newton Methods,” Vol. AIAA Paper 2012-0962,Nashville, TN, January 2012.

5 Jameson, A., “Aerodynamic Design via Control Theory,” Journal of Scientific Com-puting , Vol. 3, No. 3, 1988.

6 Nemec, M. and Aftosmis, M. J., “Parallel Adjoint Framework for AerodynamicShape Optimization of Component-Based Geometry,” Vol. AIAA Paper 2011-1249,Orlando, FL, January 2011.

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7 Gill, P. E., Murray, W., and Saunders, M. A., “SNOPT: An SQP Algorithm forLarge-Scale Constrained Optimization,” SIAM Journal on Optimization, Vol. 12,1997, pp. 979–1006.

8 Samareh, J. A., “A Survey of Shape Parameterization Techniques,”CEAS/AIAA/ICASE/NASA Langley International Forum on Aeroelasticityand Structural Dynamics, Williamsburg, VA, June 1999.

9 Samareh, J. A., “Survey of Shape Parameterization Techniques for High-FidelityMultidisciplinary Shape Optimization,” AIAA Journal , Vol. 39, No. 5, May 2001.

10 Kulfan, B. M., “Universal Parametric Geometry Representation Method,” J. Air-craft , Vol. 45, No. 1, January 2008, pp. 142–158.

11 Berkenstock, D. C. and Aftosmis, M. J., “Structure-Preserving Parametric Defor-mation of Legacy Geometry,” 12th AIAA/ISSMO Multidisciplinary Analysis andOptimization Conference, No. 2008-6026, Victoria, BC, September 2008.

12 Straathof, M. H., Shape Parameterization in Aircraft Design: A Novel Method,Based on B-Splines, Ph.D. thesis, Technische Universiteit Delft, Netherlands,February 2012.

13 Anderson, G. R., Aftosmis, M. J., and Nemec, M., “Constraint-Based Shape Pa-rameterization for Aerodynamic Design,” 7th International Conference on Compu-tational Fluid Dynamics, Big Island, Hawaii, July 2012.

14 Anderson, G. R., Aftosmis, M. J., and Nemec, M., “Parametric Deformation ofDiscrete Geometry for Aerodynamic Shape Design,” Vol. AIAA Paper 2012-0965,Nashville, TN, January 2012.

15 Nemec, M. and Aftosmis, M. J., “Output Error Estimates and Mesh Refinement inAerodynamic Shape Optimization,” Vol. AIAA Paper 2013-0865, Grapevine, TX,January 2013.

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Adaptive Shape Parameterization for Aerodynamic Design5a. CONTRACT NUMBER

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George R. Anderson and Michael J. Aftosmis

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14. ABSTRACT

This report concerns research performed in fulfillment of Phase 1 of an ongoing NASA Seedling Fund grant. The overall goalis to develop an aerodynamic shape optimization framework that supports automated shape parameterization. The fourobjectives for the work were to mature a constraint-based deformation technique, to develop the basic framework necessary toperform automated parameter refinement, to determine an importance indicator that prioritizes candidate design variables,and to develop an e�cient refinement strategy. All of these objectives have been met to the degree appropriate for Phase 1.We discuss each in detail in this report.

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design,optimization,parametric,adaptive,refinement

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