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Engineering Optimization

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Introduction

Maple lets you minimize or maximize objective functions with respect to constraints.

>

Optimization:-Minimizex2+sinx+y,x+2 y=9

−0.980004457937920792,x=0.0469482872699630,y=4.47652585636502

(1)

The objective function can be a sum-of-squares error for parameter estimation, or the

weight of a mechanical device

cost of production

or energy required for a process

The constraints can be limits on the

dimensions of a mechanical device, or the allowable stresses

minimum and maximum process temperatures

or amount of base materials

Units can be employed in the objective function or the constraints.

You can use Maple's built-in linear, nonlinear, and quadratic optimizers , or the optional Global Optimization Toolbox .

Example - Fuel Pod Design Optimization

You are designing a fuel pod with a hemispherical cap, cylindrical mid-section and conical cap.

What are values of L, H and R that minimize the surface area while maintaining the volume V at 3 m3?

>

restart:withOptimization:

Objective function - surface area of pod

>

obj124πR2+2πRL+πRH2+R2:

Constraint on the volume area of pod

>

cons11243πR3+π R2L+13πR2H=3m3:


All dimensions must be greater than 0

>

cons20R,0L,0H:


Hence the optimized dimensions are

>

dimensionsMinimizeobj,cons1,cons2,initialpoint=H=1m,L=1m,R=1m

10.2533536615869920m2,H=0.785093823049978m,L=0.392546902492684m,R=0.877761593519080m

(2)


Check that the constraint on the pod volume is satisfied

>

evalcons1,dimensions2

3.00000000039170m3=3m3

(3)

Applications

Optimize a Rankine Cycle

PV Diode Parameter Estimation


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