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
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
obj≔12⋅4⋅π⋅R2+2⋅π⋅R⋅L+π⋅R⋅H2+R2:
Constraint on the volume area of pod
cons1≔1243π⋅R3+π R2⋅L+13⋅π⋅R2⋅H=3m3:
All dimensions must be greater than 0
cons2≔0≤R,0≤L,0≤H:
Hence the optimized dimensions are
dimensions≔Minimizeobj,cons1,cons2,initialpoint=H=1m,L=1m,R=1m
10.2533536615869920⁢m2,H=0.785093823049978⁢m,L=0.392546902492684⁢m,R=0.877761593519080⁢m
Check that the constraint on the pod volume is satisfied
evalcons1,dimensions2
3.00000000039170⁢m3=3⁢m3
Applications
Welded Beam Design Optimization
Helical Spring
Optimize a Rankine Cycle
Chemical Kinetics Parameter Estimation
PV Diode Parameter Estimation
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