Structural Fatigue Reliability Optimization Based On Ant Colony Algorithm

Structural optimization design is a principal aspect of structural design. It has been put a great emphasis on in engineering. As a novel category of bionic algorithm, Ant colony algorithm has strong robustness and is easy to combine with other methods. So it is valuable to apply this algorithm into the structural optimization. The thesis has summed up the principle and the implement technology of ant algorithm, combining the theory and the method of the reliability to do the research on the fatigue reliability optimization. The main work can be generalized as follows:1. Research on the new category of bionic algorithm-Ant algorithm. Analysis the models of ant algorithm and TACO-Ant algorithm detailed. And give the program of the two algorithms. As an example, some mathematic functions are optimized to prove the validity.2. Combining TACO-Ant algorithm with FEM, develop the method of structural optimization to do the optimization of the disc. The result proved that the weight of the disc has reduced 16%. Comparing with the result of ANSYS structural optimization, the method the thesis developed is validity.3. Based on the method of the structural optimization on TACO-Ant algorithm, combining the reliability theory, develop the method of structural reliability optimization. Choose the material's modulus of elasticity and the rotate speed as the random variable. Use the method just developed to do the reliability optimization of the disc. After the optimization, the reliability increased from 0.51 to 0.99, and the weight has reduced 6.5% as well. Comparing with the result of the ANSYS reliability optimization, the method just developed is validity.4. Based on the method of reliability optimization on the TACO-Ant algorithm, choose the Walker life model to forecast the life of the turbine disc and develop the method of fatigue reliability optimization on the TACO-Ant algorithm. Choose the material's modulus of elasticity and the rotate speed as the random variable. Then apply this method to do the fatigue reliability optimization of the turbine disc. After the fatigue reliability optimization, the fatigue has increased from 0.93 to 0.99, and the weight of the disc has decreased 5.2% as well. Comparing with the result of the ANSYS fatigue reliability optimization, the method just developed is validity.