Structural optimization with approximate sensitivities
Abstract
Computational efficiency in structural optimization can be enhanced if the intensive computations associated with the calculation of the sensitivities, that is, gradients of the behavior constraints, are reduced. Approximation to gradients of the behavior constraints that can be generated with small amount of numerical calculations is proposed. Structural optimization with these approximate sensitivities produced correct optimum solution. Approximate gradients performed well for different nonlinear programming methods, such as the sequence of unconstrained minimization technique, method of feasible directions, sequence of quadratic programming, and sequence of linear programming. Structural optimization with approximate gradients can reduce by one third the CPU time that would otherwise be required to solve the problem with explicit closedform gradients. The proposed gradient approximation shows potential to reduce intensive computation that has been associated with traditional structural optimization.
 Publication:

NASA STI/Recon Technical Report N
 Pub Date:
 April 1994
 Bibcode:
 1994STIN...9517701P
 Keywords:

 Approximation;
 Linear Programming;
 Mathematical Models;
 Nonlinear Programming;
 Optimization;
 Quadratic Programming;
 Sequential Control;
 Algorithms;
 Central Processing Units;
 Constraints;
 Gradients;
 Structural Engineering;
 Structural Mechanics