scholarly journals Sensitivity Analysis of the Waterproof Performance of Elastic Rubber Gasket in Shield Tunnel

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zhiqiang Wang ◽  
Zhenyu Lei
Keyword(s):  
Sensitivity Analysis ◽  
Design System ◽  
Shield Tunnel ◽  
Random Parameters ◽  
Rubber Gasket ◽  
The Monte Carlo Method ◽  
Material Hardness ◽  
Sensitivity Analysis Method ◽  
Elastic Rubber ◽  
Input Variables

Abstract In order to study the waterproof performance of elastic rubber gasket in shield tunnel lining joints, an innovative sensitivity analysis method is proposed by combining the Monte Carlo method with the stochastic finite element method (FEM) in this paper. The sensitivity values of the waterproof performance respecting to elastic rubber gaskets are obtained via the ANSYS Probabilistic Design System (PDS) module, in which the parameters of material hardness, coordinates of the hole center, apertures are selected as random input variables. Meantime, the extent of the tolerance effect of the random parameters on the waterproof performance is explored.

Investigation of the uncertainty of the in-plane mechanical properties of composite laminates

2011 ◽  
Vol 226 (7) ◽  
pp. 1739-1750 ◽  
Author(s):  
Tian Longfei ◽  
Lu Zhenzhou ◽  
Hao Wenrui
Keyword(s):  
Mechanical Properties ◽  
Sensitivity Analysis ◽  
Composite Laminates ◽  
Global Sensitivity Analysis ◽  
Nonlinear Models ◽  
Analysis Method ◽  
Global Sensitivity ◽  
Response Variance ◽  
Sensitivity Analysis Method ◽  
Input Variables

The uncertainty of the in-plane mechanical properties of the laminate used in an aircraft wing structure is investigated. Global sensitivity analysis is used to identify the source of the uncertainties of the response performance. Due to the limitations of the existing global sensitivity analysis method for nonlinear models with correlated input variables, a new one using nonlinear regression is proposed. Furthermore, a contribution matrix is defined for engineering convenience. Two nonlinear numerical examples are employed in this article to demonstrate the ability of the proposed global sensitivity analysis method. After applying the proposed global sensitivity analysis method to the laminate model, the contribution matrices are obtained; from these matrices, researchers can identify the dominant variance contributions that contribute the most to the response variance. Factor analysis is then employed to analyze the global sensitivity analysis results and determine the most efficient methods to decrease the variances of the in-plane elastic constants. Monte Carlo simulation is used to demonstrate the efficiency of the methods in decreasing the variances.

scholarly journals A Study on the Application of Sensitivity Analysis for Ship Hydrodynamic Coefficients

2018 ◽  
Vol 7 (4.36) ◽  
pp. 953
Author(s):  
Khanh Toan Tran ◽  
. .
Keyword(s):  
Mathematical Model ◽  
Sensitivity Analysis ◽  
Mathematics Model ◽  
Hydrodynamic Coefficients ◽  
Ship Manoeuvring ◽  
Multiple Input ◽  
Sensitivity Analysis Method ◽  
Ship Hydrodynamic ◽  
Input Variables ◽  
The Mathematical Model

In the mathematical model with multiple input variables, the sensitivity analysis of the input variables is an important step to ensure the reliability of the mathematical model. In order to optimize the ship manoeuvring simulation, in particular the optimization of the trajectory ship, the sensitivity analysis should be performed in the mathematical model to select the group of the most sensitive hydrodynamic coefficients. In this paper, the author applied the sensitivity analysis method in mathematics model of ship manoeuvring programming in order to optimize the ship trajectory of Esso Bernicia 193000DWT tanker model.  

A Global Sensitivity Analysis Method for Multi-Input Multi-Output System and its Application in Structural Design

2021 ◽  
pp. 2141005
Author(s):  
Qiming Liu ◽  
Nichen Tong ◽  
Xu Han
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Sensitivity Index ◽  
Response Functions ◽  
Analysis Method ◽  
Order Function ◽  
Global Sensitivity ◽  
Sensitivity Analysis Method ◽  
Output System ◽  
Input Variables

Commonly, variance-based global sensitivity analysis methods are popular and applicable to quantify the impact of a set of input variables on output response. However, for many engineering practical problems, the output response is not single but multiple, which makes some traditional sensitivity analysis methods difficult or unsuitable. Therefore, a novel global sensitivity analysis method is presented to evaluate the importance of multi-input variables to multi-output responses. First, assume that a multi-input multi-output system (MIMOS) includes [Formula: see text] variables and [Formula: see text] responses. A set of summatory functions [Formula: see text] and [Formula: see text] are constructed by the addition and subtraction of any two response functions. Naturally, each response function is represented using a set of summatory function. Subsequently, the summatory functions [Formula: see text] and [Formula: see text] are further decomposed based on the high dimensional model representation (HDMR), respectively. Due to the orthogonality of all the decomposed function sub-terms, the variance and covariance of each response function can be represented using the partial variances of all the decomposed function sub-terms on the corresponding summatory functions, respectively. The total fluctuation of MIMOS is calculated by the sum of the variances and covariances on all the response functions. Further, the fluctuation is represented as the sum of the total partial variances for all the [Formula: see text]-order function sub-terms, and the total partial variance is the sum of [Formula: see text] partial variances for the corresponding [Formula: see text]-order function sub-terms. Then, the function sensitivity index (FSI) [Formula: see text] for s-order function sub-terms is defined by the ratio of the total partial variance and total fluctuation, which includes first-order, second-order, and high-order FSI. The variable sensitivity index [Formula: see text] of variable [Formula: see text] is calculated by the sum of all the FSIs including the contribution of variable [Formula: see text]. Finally, numerical example and engineering application are employed to demonstrate the accuracy and practicality of the presented global sensitivity analysis method for MIMOS.

scholarly journals A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2489
Author(s):  
Zhiwei Bai ◽  
Hongkui Wei ◽  
Yingying Xiao ◽  
Shufang Song ◽  
Sergei Kucherenko
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Joint Probability ◽  
Copula Functions ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Sensitivity Analysis Method ◽  
Input Variables ◽  
Vine Copula ◽  
Bivariate Copula

For multidimensional dependent cases with incomplete probability information of random variables, global sensitivity analysis (GSA) theory is not yet mature. The joint probability density function (PDF) of multidimensional variables is usually unknown, meaning that the samples of multivariate variables cannot be easily obtained. Vine copula can decompose the joint PDF of multidimensional variables into the continuous product of marginal PDF and several bivariate copula functions. Based on Vine copula, multidimensional dependent problems can be transformed into two-dimensional dependent problems. A novel Vine copula-based approach for analyzing variance-based sensitivity measures is proposed, which can estimate the main and total sensitivity indices of dependent input variables. Five considered test cases and engineering examples show that the proposed methods are accurate and applicable.

scholarly journals Aerodynamic Design Based on Global Sensitivity Analysis Method

2018 ◽  
Vol 36 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Lu Xia ◽  
Meihua Yang ◽  
Lang Li ◽  
Xin Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Optimization Design ◽  
Global Sensitivity Analysis ◽  
Design System ◽  
Hierarchical Optimization ◽  
Analysis Method ◽  
Global Sensitivity ◽  
Design Variables ◽  
Sensitivity Analysis Method ◽  
Optimization Search

To deal with the problem of the difficult optimization search and expensive computational cost caused by large-scale design variables, the hierarchical optimization design system based on the global sensitivity analysis method is established in this paper. The M-OAT method is used to analyze the global sensitivity of the design variables, according to the sensitivity information to layer design variables, then optimize the design variables in each hierarchy. Through the study of the hierarchical optimization design of airfoils and wings, compared with the normal parameter optimization design system, the hierarchical optimization design system based on the global sensitivity analysis method can reduce effectively the number of design variables in a single optimization, reduce the difficulty of the optimization search, improve the convergence speed of the optimization, gain better optimization results at the same time. For optimization design with large-scale design variables, the hierarchical optimization design system based on the global sensitivity analysis method is a sort of effective ways of design.

A global sensitivity analysis method based on ANOVA with convex set model and its state-dependent parameter solution

2018 ◽  
Vol 233 (11) ◽  
pp. 4039-4051
Author(s):  
Qing Guo ◽  
Yongshou Liu ◽  
Xiangyu Chen
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Convex Set ◽  
The State ◽  
Analysis Method ◽  
Global Sensitivity ◽  
State Dependent ◽  
Sensitivity Analysis Method ◽  
Dependent Parameter ◽  
Input Variables

Convex set model is most widely applied around nonprobabilistic uncertainty description. This paper combines the convex model with global sensitivity analysis theory of variance, and then proposes an index based on convex set model and variance-based global sensitivity analysis method to illustrate the effect of the nonprobability variables on the dangerous degree. The proposed index consists of two parts, including the main and total indices. The main index can quantitatively reflect the effect of uncertainties of input variables on the variance of output response, and the total index reflects the influence of interaction with other variables in addition to the individual influence of input variables. Furthermore, an efficient state-dependent parameter solution for solving the variance-based global sensitivity analysis of nonprobabilistic convex uncertainty is given in this paper. The state-dependent parameter solution not only greatly improves the efficiency but also guarantees the computational accuracy, and the times of performance functions evaluation decrease from [Formula: see text] in single-loop Monte Carlo solution to 2048 in the state-dependent parameter method. Finally, three numerical examples and a finite element example are used to verify the feasibility and rationality of the proposed method.

scholarly journals A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1095
Author(s):  
Xiang Peng ◽  
Xiaoqing Xu ◽  
Jiquan Li ◽  
Shaofei Jiang
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Method ◽  
Transformation Method ◽  
Unscented Transformation ◽  
Performance Function ◽  
Gaussian Integration ◽  
Sensitivity Indices ◽  
Sensitivity Analysis Method ◽  
Distribution Parameters ◽  
Input Variables

For engineering products with uncertain input variables and distribution parameters, a sampling-based sensitivity analysis methodology was investigated to efficiently determine the influences of these uncertainties. In the calculation of the sensitivity indices, the nonlinear degrees of the performance function in the subintervals were greatly reduced by using the integral whole domain segmentation method, while the mean and variance of the performance function were calculated using the unscented transformation method. Compared with the traditional Monte Carlo simulation method, the loop number and sampling number in every loop were decreased by using the multiplication approximation and Gaussian integration methods. The proposed algorithm also reduced the calculation complexity by reusing the sample points in the calculation of two sensitivity indices to measure the influence of input variables and their distribution parameters. The accuracy and efficiency of the proposed algorithm were verified with three numerical examples and one engineering example.

Design of Reinforced Concrete Segment for Cross River Shield Tunnel in Long Distance Gas Pipeline Project

2014 ◽  
Vol 488-489 ◽  
pp. 513-516 ◽  
Author(s):  
Xiao Feng Wang ◽  
Xiao Jun Zhou ◽  
Hong Yun Hu ◽  
Xiang Pu Zhang
Keyword(s):  
Reinforced Concrete ◽  
Gas Pipeline ◽  
Shield Tunnel ◽  
Long Distance ◽  
Rubber Gasket ◽  
Cross River ◽  
Pipeline Project ◽  
Elastic Rubber ◽  
Water Swelling ◽  
Water Proof

This paper summarizes the basic design of reinforced concrete segment for cross river shield tunnel in gas pipeline project, meanwhile discusses its assembly style and water-proof technique. One loop of shield tunnel is divided into six pieces of precast reinforced concrete segment which is assembled with screw bolt and stud. In addition, Waterproof technique such as self-waterproof of segment, elastic rubber gasket and water swelling rubber strip is used to ensure waterproof performance of shield tunnel. The presentation of this paper may provide guidance for the design of reinforced concrete segment for cross river shield tunnel.

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