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

2022 ◽  
Vol 2148 (1) ◽  
pp. 012008
Author(s):  
Zenghui Wang ◽  
Hong Yin ◽  
Zhenrui Peng
Keyword(s):  
Simulated Annealing ◽  
Surrogate Model ◽  
Bayesian Model ◽  
Simulated Annealing Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Dynamic Responses ◽  
Monte Carlo Algorithm ◽  
Kriging Surrogate Model ◽  
Annealing Algorithm

Abstract 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.

Structural Parameters Optimization of Waveform Generator Based on LS-SVM and Simulated Annealing Algorithm

2012 ◽  
Vol 268-270 ◽  
pp. 871-874
Author(s):  
Yu Liang Yang ◽  
Jun Qi Qin ◽  
Chang Chun Di ◽  
Yan Feng Yang
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Regularization Parameter ◽  
Design Method ◽  
Orthogonal Design ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Waveform Generator ◽  
Sample Data ◽  
Annealing Algorithm

For the structural design problem of waveform generator, selected diameter of rubber block, hardness and thickness of block 1 and block 2 as five design variables. Firstly, adopted orthogonal design method, and built initial sample data. Secondly, adopted LS-SVM to exercise the sample data, and selected regularization parameter and kernel function width of LS-SVM based on QPSO algorithm. Finally, optimized the structural parameters of waveform generator based on simulated annealing algorithm. The research provided a theoretic basis for the design of waveform generator.

Experimental study of kriging-based crack identification in plate structure

2016 ◽  
Vol 231 (17) ◽  
pp. 3118-3129 ◽  
Author(s):  
Haiyang Gao ◽  
Xiaofei Hu ◽  
Fang Han ◽  
Xinming Li ◽  
Jungang Zhang
Keyword(s):  
Surrogate Model ◽  
Crack Detection ◽  
Model Updating ◽  
Computational Cost ◽  
Measurement Noise ◽  
Dynamic Responses ◽  
Detection Methods ◽  
Crack Identification ◽  
Kriging Surrogate Model ◽  
Plate Structure

One of the major issues that existing crack identification methods utilizing dynamic responses are facing is the limitation of engineering feasibility. How to suppress the effect of measurement noise and improve the identification accuracy is still challenging. In this work, an effective method is proposed to identify the size of an arbitrary internal crack in plate structure based on a Kriging surrogate model, and a series of laboratory tests are designed to verify the practicability of this strategy. The initial Kriging surrogate model is constructed by samples of crack parameters (tip locations) and corresponding root mean square (RMS) of random responses as the inputs and outputs, respectively. To further improve the surrogate accuracy and reduce computational cost during the inverse problem, an optimal point-adding process for Kriging model updating is then carried out. Experimental results of crack identification in a cantilever plate indicate that the proposed method can be an alternative to conventional crack detection methods even in the presence of measurement noise and modeling errors.

Optimization of tire tread pattern based on flow characteristics to improve hydroplaning resistance

2020 ◽  
Vol 234 (13) ◽  
pp. 2961-2974
Author(s):  
Haichao Zhou ◽  
Zhen Jiang ◽  
Baiyu Jiang ◽  
Hao Wang ◽  
Guolin Wang ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Simulated Annealing ◽  
Water Flow ◽  
Surrogate Model ◽  
Lift Force ◽  
Simulated Annealing Algorithm ◽  
Latin Hypercube Design ◽  
Water Flow Velocity ◽  
Design Variables ◽  
Annealing Algorithm

Tire tread pattern is a crucial parameter to prevent hydroplaning. In this study, numerical modeling was used to investigate tire hydroplaning based on flow–structure interaction. The empirical model of hydroplaning speed published in the literature was used to validate the computational model. Analysis of water flow velocity and turbulent flow energy revealed that lateral grooves of the tire significantly influenced water drainage capacity. Based on the relationship between water flow vector and lateral groove shape, a combination of Kriging surrogate model and simulated annealing algorithm was used to optimize lateral groove design to minimize hydrodynamic lift force. Four geometry parameters of lateral grooves were selected as the design variables. Based on design of experiment principle, 12 simulation cases based on the optimal Latin hypercube design method were used to analyze the influence of design variables on hydrodynamic lift force. The surrogate model was optimized by the simulated annealing algorithm to optimize tire tread pattern. The results indicated that at the same water flow speed, the optimized lateral grooves can reduce hydrodynamic lift force by 14.05% and thus greatly improve safety performance of the tire. This study proves the validity and applicability of using numerical modeling for solving the complex design of tire tread pattern and optimization problem.

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