scholarly journals Weight Determination of Sustainable Development Indicators Using a Global Sensitivity Analysis Method

2017 ◽  
Vol 9 (2) ◽  
pp. 303 ◽  
Author(s):  
Wenfei Luan ◽  
Ling Lu ◽  
Xin Li ◽  
Chunfeng Ma
Keyword(s):  
Sensitivity Analysis ◽  
Sustainable Development ◽  
Global Sensitivity Analysis ◽  
Analysis Method ◽  
Development Indicators ◽  
Sustainable Development Indicators ◽  
Global Sensitivity ◽  
Sensitivity Analysis Method

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.

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 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.

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