Evolution based statistical optimization technique to design the smart structural system for large aerospace structures

摘要

A numerical approach for genetics based statistical optimization technique is used to design the smart structural system for aerospace structures. An evolutionary based optimization technique like genetic algorithm (GA) has come into prominence. The reason for developing evolution based algorithm for optimization is for its robustness and randomness. Other numerical tools that are used for optimization are generally gradient based algorithm, where there is possibility of occurrence for a local optimum value. The GA developed is a niche-micro GA, where termination criteria are set in order to restart the algorithm. Stage-wise multiple objective functions and multiple termination criteria are incorporated to improve the computational effort. The current approach is very much robust to design a smart structural system through optimization for its maximum structural performance. In order to achieve maximum structural performance for the smart structural system, it is necessary to appropriately position the active elements. Here the genetic algorithm is amalgamated with finite element to perform a statistical based optimization to locate the position and size of active structural elements i.e. actuators/sensors. Majorly, nowadays the actuators and sensors that are preferred for smart structures design (i.e. Piezo patches, Piezo composite, SMA wire, SMA composite etc) develop induced strain under an external applied field. It becomes necessary to optimize the smart structures using the following parameters such as static strains, modal dynamic strains, size of the actuators/sensors, induced strain etc. A scaled T-Tail model is taken as an illustration to carry out the GA analysis for the location and sizing of PZT actuator/sensor. The structural parameters such as static strains, modal dynamic strains and geometry details are taken from NASTRAN and then interfaced with MATLAB to perform the statistical optimization analysis.