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Since the railway vehicle structure has lots of parameters and several complex constraints, this study establishes a method for structural parameter optimization based on sensitivity analysis and surrogate models. Fatigue crack problem of the equipment cabin bottom cover of the EMU is taken as an example to optimize its structural parameters. First...
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In parameter uncertainty analysis, traditional Monte Carlo simulation (MC) requires double sampling to calculate the importance index based on the moment-independent method, which takes a long time, especially when the failure probability is small. The Latin hypercube sampling method is used to simulate the uncertainty of the random variables in th...
Citations
... Whether the inversion can achieve good results or not mainly depends on the performance of the surrogate model. Over the past few decades, several surrogate models have been most widely used, including support vector regression (SVR) [31], kriging (KRG) [32], and radial basis function (RBF) [33]. By comparing diferent models, the results show that SVR has some advantages in terms of sparsity, accuracy, and fexibility, especially in dealing with small samples and nonlinear problems. ...
Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones.
The equipment cabins of high‐speed trains encountered alternating pressure loads during operation. A fatigue evaluation methodology based on sub‐zone aerodynamic loading was proposed. Theoretical model was given between pressure load at each zone and the accompanying six structural stress components. Stress recovery matrix was formulated by finite element analysis (FEA) in sub‐zone loading cases to relate the stress to the measured pressure loads. Vehicle test gathered the pressure time‐history data of cabin sub‐zones at the head car in different ambient conditions, from which the two‐dimensional stress spectrum was derived by rain‐flow counting. The fatigue damage of welds was then assessed utilizing the Miner's and Corten Dolan's rules, yielding maximum equivalent damages of 0.72 and 0.23, respectively. The structural fatigue damage under high environmental wind conditions was 1–3 times greater than in calm weather. Nonetheless, the fatigue damage incurred by the metal matrix was significantly lower, demonstrating a substantial safety margin.
