progress report group 1

8/11/2019 Progress Report Group 1

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Progress Report

Qiang Chen, Derek Dalle,Chad Griep, Jingwei Hu,

Jahmario Williams, Zhenqiu Xie

Multiobjective Modeling andOptimization in Design


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Introduction Optimal design of

subsonic aircraft Study how changes in the shapeof aircraft affect aerodynamics.

More importantly, figureout what to optimize.

Apply this to quietsupersonic aircraft. Investigate intricacies anddifficulties inherent in designing

a cost-effective, efficient andquiet supersonic passengeraircraft.


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Configuration DesignVariables for Conceptual Design Reference wing area

Wing sweep angle Wing aspect ratio Wing taper ratio Wing-thickness chord ratio

Gross weight Thrust

ObjectiveFunctions

Minimum gross weight Minimum fuel burned Maximum range Minimum cost Minimum NOx emissions

sweep angle

ct


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MotivationTypical Engineers Method

Establish requirements.

Design an aircraft thatsuccessfully meets the

requirements.

Try to optimize bychanging one (or

several) design variableat a time.

Ad hoc stopping criteria

are used.


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MotivationProblems with Old Methods

This process is slow.

Optimization occurs toolate.

Engineers have beensuccessful, but design isbased on experience.

Some problems are toohard.

Real problems aremassively multiobjective.


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Flight Optimization SystemFLOPS (A. McCullers)

FLOPS analyzes acomplete aircraft given alarge set of designvariables and options.

FLOPS also doesnonlinear optimization byminimizing if i whereeach f i is a singleobjective function.

We are looking for betterdecision-making tools.


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Optimality forSingle Objective

Study sensitivity of single objective function to variations indesign variables

FLOPS aproach Enter parametrically varied design variables into input file and

chose objective function to study Run FLOPS to analyze the inputs Read values of objective function from (contour plot data)

output file


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FLOPS with Matlab approach Use Matlab to generate mesh of two design variables

Rewrite the input file with updated variables Call FLOPS to analyze the inputs Read output for objective function Write data file and plot results


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Optimality for MultipleObjectives

Analyze competing elements in supersonic aircraftshape optimization (i.e., low boom versus low drag).

Discuss condition where one objective cannot beimproved without hurting another.


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Pareto optimality

Pareto optimality (or efficiency) occurswhen one cannot decrease one objectivewithout increasing another.

Decision making playsan important role.

Choose proper weights

F1

F2

2211 FwFwF


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Not perfect curve. Objective functions have many local minima

(artifact of numerical procedures). The graph implies that we need more work on

optimization.


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Using other optimization codes To investigate alternative formulations, we need to use

tools that are external to FLOPS. NPSOL (Stanford Software, Gill et al. ) is a set of Fortran

subroutines for minimizing a smooth function subject to

bounds on variables, linear constraints and smoothnonlinear constraints.

It uses a sequential quadratic programming (SQP)algorithm.

Call previous Matlab codes to adjust input variables,perform analysis and read output results.

Use NPSOL to minimize the result (weighted objectivefunction)


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Used out of the box, NPSOL did not

provide better results than FLOPS itself Price of running FLOPS is quite high May not be efficient enough in handling this

special problem May need fine tuning

A bootstrapping strategy of the twocodes can do quite well


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Minimization ObtainedMinimum of Gross Weight

FLOPS 213554

NPSOL 221495

FLOPS + NPSOL 211920

2 * (FLOPS +

NPSOL)

210046


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Future Work

Investigate the effects ofmultiple objectives.

Model sound and energyproduced from sonicoverpressure signal.

Understand relationshipsbetween aircraft design andoverpressure signal.

The goal is an analysismethod that could be usedwith an optimizationalgorithm.

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