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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2010, Vol. 31 ›› Issue (5): 940-945.

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Structural Finite Element Model Updating Based on Hybrid Artificial Fish Swarm Algorithm

Zhang Anping1,2; Chen Guoping1   

  1. 1.College of Aerospace Engineering, Nanjing University ofAeronautics and Astronautics2.Research Institute of Unmanned Aircraft, Nanjing University ofAeronautics and Astronautics
  • Received:2009-05-08 Revised:2009-06-15 Online:2010-05-25 Published:2010-05-25
  • Contact: Zhang Anping

Abstract: By combining the artificial fish swarm algorithm (AFSA) with crossover and Gauss mutation with the simulated annealing algorithm (SAA), a novel structural finite element model updating method based on the hybrid artificial fish swarm algorithm (HAFSA) is presented, and a facile and convenient interface module is designed to deal with the difficulty that an external finite element model updating program encounters when it is directly implanted to the Patran/Nastran software. An objective function is established by using the resi-duals between the measurement data vectors of the test model and the calculation value vectors of the finite element model, and crossover and Gauss mutation operators are added to the original AFSA to increase the global optimization search velocity. The bulletin is refreshed by the optimization objective function value continuously, and SAA is applied to carry out local refined search to greatly improve the precision of the optimization solution. The optimization values of design variables are obtained after the algorithm end condition is satisfied. Fortran language is combined with Visual Basic language to compile the interface module. The Patran/Nastran software is transferred iteratively when the model updating program is run. A GARTEUR aircraft model is performed as an example, and updating results show that the finite element model updating based on HAFSA is feasible and effective.

Key words: model updating, interface module, optimization, artificial fish swarm algorithm, genetic algorithm, simulated annealing

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