Bayesian Model Updating Based on Kriging Surrogate Model and Simulated Annealing Algorithm

Aiming at the problem of difficulty in selecting the proposal distribution and low computational efficiency in the traditional Markov chain Monte Carlo algorithm, a Bayesian model updating method using surrogate model technology and simulated annealing algorithm is proposed. Firstly, the Kriging surrogate model is used to mine the implicit relationship between the structural parameters to be updated and the corresponding dynamic responses, and the Kriging model that meets the accuracy requirement is used to replace the complex finite element model to participate in the iterative calculation to improve the model updating efficiency. Then, the simulated annealing algorithm is introduced to reorganize the Markov chains from different proposal distributions to obtain high-quality posterior samples, which are used to estimate the parameters posterior distributions. Finally, a space truss structure is used to verify the effectiveness of the proposed method.

Optimal location of cutoff walls for seawater intrusion

In this paper, the simulation–optimization method is used to study the optimal location of cutoff walls for seawater intrusion. The optimization model is based on minimizing the chlorine concentration of two water sources after 50 years. In order to reduce the computational complexity, a Kriging surrogate model simulation is coupled with the optimization model. Finally, a hypothetical case is used to evaluate the accuracy of the surrogate model and the performance of the optimization model. The results show that the outputs of the Kriging surrogate model and the variable density groundwater simulation model for the same cutoff wall design fit well, and the average relative error of the two outputs is only 2.2% which proves that it is feasible to apply the Kriging surrogate model to this problem. By solving the optimization model, the location of the cutoff wall which minimizes the sum of chlorine concentration of the two water sources after 50 years is obtained. This provides a stable and reliable method for the site selection of cutoff walls for future projects intended to prevent and control seawater intrusion.

Thermal Fatigue Life Prediction under Temperature Uncertainty for Shot Sleeve of Squeeze Casting Machine

To quantify the influence of temperature uncertainty on thermal fatigue life prediction of a shot sleeve in an injection mechanism, an uncertainty analysis method based on a Kriging surrogate model and Monte Carlo simulation (MCS) was proposed. The training samples of the surrogate model were obtained by a finite element simulation, and the response relationships between input variables, such as pouring and preheating temperature, and target variables, such as strain and stress, were constructed by the Kriging surrogate model. The input variables were sampled by the MCS, and the predicted stress and strain parameters were combined with the modified universal slope equation to predict the thermal fatigue life of the shot sleeve. The statistical characteristics of the predicted life were obtained. The comparative analysis results indicate that the predicted life considering temperature uncertainty is more accurate than the deterministically predicted value.