Maple lets you minimize or maximize objective functions with respect to constraints.
>
|
|
| (1) |
The objective function can be a sum-of-squares error for parameter estimation, or the
•
|
weight of a mechanical device
|
•
|
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?
>
|
|
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
>
|
|
| (2) |
Check that the constraint on the pod volume is satisfied
>
|
|
| (3) |
|