Maple lets you minimize or maximize objective functions with respect to constraints.
The objective function can be a sum-of-squares error for parameter estimation, or the
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weight of a mechanical device
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or energy required for a process
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The constraints can be limits on the
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dimensions of a mechanical device, or the allowable stresses
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minimum and maximum process temperatures
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or amount of base materials
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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
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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?
Objective function - surface area of pod
Constraint on the volume area of pod
All dimensions must be greater than 0
Hence the optimized dimensions are
Check that the constraint on the pod volume is satisfied
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