Optimization of stiffened panel fatigue life by using finite element analysis

Aluminum Al 2024-T351 and titanium Ti–6Al–4V are widely used in aircraft components such as wings, fuselage, steam turbines and heat exchangers are prone to failure due to fatigue. In design, a range of operating temperatures, fluctuating working loads, duration and manufacturing processes are important and that due to these design factors the load-carrying capability of the structural members can be significantly affected. A comprehensive method of analysis combining experimental and simulation approaches in order to predict the fatigue life of structural members were carried out in this research. The experimental analyses were conducted for tensile and fatigue tests at several stress levels. The frequency of 10 Hz and the load ratio of 0.1 was selected during the fatigue tests. The tests were conducted in controlled elevated and low temperatures. The effect of temperature on the yield strength, ultimate strength, elastic modulus and deformation were discussed. Finite element analysis (FEA) was validated with the experimental work to verify the precision of the results. The overall data showed a good agreement between experimentally observed and computationally predicted data. The single edge notched tension specimen was used to calculate the fracture toughness of a material. The stress intensity factor and critical length were analytically calculated and compared with the numerical results. Several crack growth models such as NASGRO, Forman, Broek & Schijve and Paris were applied to the calculated data in order to predict the fatigue life and cycles to crack initiation. The Paris model was observed to be the closest results to the numerical model. A range of stiffened panels consisted of stiffeners that were fastened to the skin as used in aircraft wings and fuselage structures were simulated and analyzed. Three optimization methods: screening, multi-objective genetic algorithm and adaptive multi-objective algorithm were adopted in this study. The screening approach that is the random sampling method was able to select design points close to the objective. The multi-objective genetic algorithm which selects the design points based on Pareto optimal design combined with the adaptive multi-objective algorithm method which uses an optimal space-filling was shown to be efficient for time limitation and budget. The results of the multi-objective genetic algorithm method confirmed the possibility of archive improvement in fatigue life, even with the decrease in stress and mass simultaneously.

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