The electronic structures and optical properties of pure and Sr doped LaTiO 3 were studied using the firstprinciple density functional theory (DFT). The results show that the Fermi surface of LaTiO 3 lies in its conduction band, which makes its reflectivity (about 68.3% at the laser wavelength of 10.6 μm) much higher than other ceramic materials. Sr doping lowers the conduction band and reduces the band gap of La 1-x Sr x TiO 3 which is beneficial to the prompt of reflectivity. We also found that shifting of the conduction band is not linear with Sr dopant concentration, and a minimum energy level is reached when x=0.25. For the reflectivity of La 1-x Sr x TiO 3 , it first increases and then decreases with increasing Sr concentration; a maximum reflectivity (99.2%) is achieved when x=0.25.
The random formula on fatigue crack growth is deduced by the fatigue crack data and the improved Taguchi method, and the sample estimates of random variables are received by the least square method in the random formula. Fatigue fracture life and reliability of structure are analyzed by the random model. The result show the model is correct and practical, and get the same result with Monte Carlo simulation, moreover its calculation is very simple.
With the increasing for crane in the industrial production, its structural reliability has now been an important concept to guarantee stable performances. The structural safety for the traditional stochastic and probabilistic reliability method is both measured with the viewpoint of probability. But large crane structure with low fault rate is often unable to get necessary statistic data. The new developing crane also has not large amount of statistical data due to no precedent of use. Aiming to these problems, the reliability analysis based on possibility theory is supplied. The method abandons two value state hypotheses, and can avoid a large number of sample collection and the impact of human factors. Compared with the probability methods applied to the crane structure, the possibility reliability method is not only feasible, but also reduces the computational error.
The methods of the structural reliability mainly involve analytical approximate reliability index or numerical simulation, which using the finite element solver is time-consuming and large computation. Important sampling (IS) for structural reliability analysis based on radial basis functions neural network (RBFNN) is proposed in the paper, in which trained RBFNN can model the implicit function between the structure response and input random variables. And limit state function of structure is simulated with RBFNN model applied to calculate the design point. The results show that the RBFNN can simulate the limit state functions of structures. Besides, calculation procedure based on finite element solver for structural analysis is greatly reduced and the efficiency in structural reliability evaluation is improved.
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