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Vibration Fatigue-Life Prediction And Optimization For The Back Fuel Tank Of Aircraft

Posted on:2017-05-15Degree:MasterType:Thesis
Country:ChinaCandidate:H Y GuFull Text:PDF
GTID:2322330509962644Subject:General and Fundamental Mechanics
Abstract/Summary:PDF Full Text Request
As a thin-walled metal structure, the resonance phenomena often occur in the back fuel tank of aircraft, making for its resonance fatigue and flight safety. The paper studies the resonance fatigue and structural optimization of the back fuel tank via the modal analysis, fatigue analysis and structure optimization theory. The main work of this paper is as follows.The finite element model of the back fuel-tank by MSC.Patran is established, taking the coupling factor of fluid and tank into account via the virtual mass. Then, the modal analysis and frequency response are analyzed for both the empty and the one with fuel. According to the S-N curve of the material and the load spectrum of the tank, in conjugation with the Miner linear cumulative damage criterion and the Dirlik broadband random fatigue life analysis, the vibration fatigue life of the empty fuel tank and the tank with fuel are estimated by MSC.Fatigue platform, respectively. Afterwards, the parameterized model of fuel tank is given. By taking the thickness of each face of the tank as optimization variables, and the scope of the thickness and the natural frequency as constraint, as well as the total mass of the tank as optimization target, the tank is optimized by Matlab-Patran-Nastran combined simulation via genetic algorithm. The results suggest that the natural frequency of the optimized fuel tank is away from the resonant band, thereby the resonant vibration fatigue could be avoided.The research shows the finite element method in conjugation with the structural optimization method is an effective tool for vibration fatigue life prediction of back fuel-tank of aircraft.
Keywords/Search Tags:Back fuel-tank of aircraft, Finite element method, Vibration fatigue, Structural optimization, Genetic algorithm
PDF Full Text Request
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