Wyniki wyszukiwania - BazTech

1

Dynamic reliability calculation of random structures by conditional probability method

Kubica Jan, Ahmed Bilal, Muhammad Akbar, Usama Aslam Muhammad

Eksploatacja i Niezawodność

|

2024

|

Vol. 26, no. 2

art. no. 181133

EN

Reliability is sometimes computed as the likelihood of achieving an intended function in the presence of uncertainties, and this is known as dynamic reliability by the conditional probability approach. These techniques can produce incredibly accurate reliability estimates. This work uses the dynamic response spanning action Markov hypothesis for the composite random reliability problem. Two steps are needed to describe conditional probability: first, the Taylor expansion approach is used to derive a 2nd-order approximate formula for determining the dynamic reliability of the random structure. The second step is to come up with a mathematical sampling strategy based on the statistical analysis's Kriging model. The Kriging interpolation model's sampling process satisfies the nonlinear association between structural random boundaries and dynamic reliability. Consequently, the finite element results can be used immediately to anatomize the impact of random structural parameters on dynamic reliability, bypassing the arduous and time-consuming theoretical derivation. The numerical example results show that the sampling method based on the Kriging model is unconcerned about the ratio used to represent dispersion and provides extra benefits in computational verisimilitude and calculation productivity.

2

An efficient method for calculating system non-probabilistic reliability index

Liu Hui, Xiao Ning-Cong

Eksploatacja i Niezawodność

|

2021

|

Vol. 23, no. 3

498--504

EN

Collecting enough samples is difficult in real applications. Several interval-based non-probabilistic reliability methods have been reported. The key of these methods is to estimate system non-probabilistic reliability index. In this paper, a new method is proposed to calculate system non-probabilistic reliability index. Kriging model is used to replace time-consuming simulations, and the efficient global optimization is used to determine the new training samples. A refinement learning function is proposed to determine the best component (or performance function) during the iterative process. The proposed refinement learning function has considered two important factors: (1) the contributions of components to system nonprobabilistic reliability index, and (2) the accuracy of the Kriging model at current iteration. Two stopping criteria are given to terminate the algorithm. The system non-probabilistic index is finally calculated based on the Kriging model and Monte Carlo simulation. Two numerical examples are given to show the applicability of the proposed method.

3

Reliability optimization design method based on multi-level surrogate model

Li Yong-Hua, Liang Xiao-Jia, Dong Si-Hui

Eksploatacja i Niezawodność

|

2020

|

Vol. 22, no. 4

638--650

EN

In this work, a genetic-algorithm-based Kriging model with multi-point addition sequence optimization strategy is addressed to make up for the shortcomings of Kriging model with single point criterion. This approach combines the multi-point addition strategy with genetic algorithm to enable the Kriging model to efficiently capture the globally optimal solution. Based on this, a multi-level surrogate method is presented by employing a local surrogate model to modify the Kriging global surrogate model, and then applied to design optimization to improve the accuracy and efficiency of global optimization. Meanwhile, a reliability design optimization method based on multi-level surrogate model is studied by dealing with the reliability constraints with an adaptive reliability penalty function. Numerical examples show that the proposed method can find the optimal solution of the object problem with the least calculation cost under the condition of satisfying the reliability constraint.