Adaptive Reduction of Design Variables Using Global Sensitivity in Reliability-Based Optimization

2004 ◽  
Author(s):  
Nam Ho Kim ◽  
Haoyu Wang ◽  
Nestor Queipo
Keyword(s):  
Global Sensitivity ◽  
Design Variables ◽  
Reliability Based Optimization

Reliability Based Optimization of Submarine Pressure Hulls With Inelastic Interstiffener Buckling Failure

2006 ◽  
Author(s):  
P. Radha ◽  
K. Rajagopalan
Keyword(s):  
Unconstrained Minimization ◽  
Computer Code ◽  
Correction Method ◽  
Penalty Function Method ◽  
Simulation Design ◽  
Design Variables ◽  
Minimization Technique ◽  
Buckling Failure ◽  
Optimal Values ◽  
Reliability Based Optimization

Uncertainties that exist in modelling and simulation, design variables and parameters, manufacturing processes etc., may lead to large variations in the performance characteristics of the system. Optimized deterministic designs determined without considering uncertainties can be unreliable and may lead to catastrophic failure of the structure being designed. Reliability based optimization (RBO) is a methodology that addresses these problems. In this paper the reliability based optimization of submarine pressure hulls in which the failure gets governed by inelastic interstiffener buckling has been described. The problem has been formulated to minimize the ratio of weight of shell-stiffener geometry to the weight of liquid displaced, subjected to reliability based inelastic interstiffener buckling constraint. Since the methods of analysis of inelastic buckling failure of submarine pressure hulls are inadequate, in the present study the Johnson-Ostenfeld inelastic correction method has been adopted for formulating the constraint. By considering spacing of the stiffener, thickness of the plating and depth of the stiffener as the design variables, Sequential Unconstrained Minimization Technique (SUMT) has been used to solve the design problem. RBO has been carried out to get the optimal values of these design variables for a target reliability index using Interior Penalty Function Method for which an efficient computer code in C++ has been developed.

Reliability Based Optimization of Steel Monopod Offshore-Towers

2008 ◽  
Author(s):  
H. Karadeniz ◽  
V. Togan ◽  
T. Vrouwenvelder
Keyword(s):  
Natural Frequency ◽  
Cross Section ◽  
Sequential Quadratic Programming ◽  
Limit State ◽  
State Function ◽  
Wave Loading ◽  
Extreme Wave ◽  
Design Variables ◽  
Reliability Based Optimization ◽  
Numerical Strategy

In this work, the implementation of reliability-based optimization (RBO) of a circular steel monopod-offshore-tower with constant and variable diameters (represented by segmentations) and thicknesses is presented. The tower is subjected to the extreme wave loading. For this purpose, the deterministic optimization of the tower is performed with constraints including stress, buckling, and the lowest natural frequency firstly. Then, a reliability-based optimization of the tower is performed. The reliability index is calculated from FORM using a limit state function based on the lowest natural frequency. The mass of the tower is considered as being the objective function; the thickness and diameter of the cross-section of the tower are taken as being design variables of the optimization. The numerical strategy employed for performing the optimization uses the IMSL-Libraries routine that is based on the Sequential Quadratic Programming (SQP). In addition, to check the results obtained from aforementioned procedure, the RBO of the tower is also performed using the genetic algorithms (GA) tool of the MATLAB. Finally, a demonstration of an example monopod tower is presented.

Global Sensitivity Analysis for Stay Vane and Casing Design of Reaction Hydraulic Turbine

2019 ◽  
Author(s):  
Hyunkyoo Cho ◽  
Ujjwal Shrestha ◽  
Young-Do Choi ◽  
Jungwan Park
Keyword(s):  
Sensitivity Analysis ◽  
Design Process ◽  
Performance Measures ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Surrogate Models ◽  
Sensitivity Index ◽  
Global Sensitivity ◽  
Design Variables ◽  
Hydraulic Turbines

Abstract Global sensitivity analysis (GSA) estimates influence of design variables in the entire design domain on performance measures. Hence, using GSA, important design variables could be found for an engineering application with high dimension which require computationally expensive analyses. Then, similar engineering applications could use selected variables to carry out design process with smaller dimension and affordable computational cost. In this study, GSA has been carried out for the performance measures in design of stay vane and casing of reaction hydraulic turbines. Global sensitivity index method is used for GSA because it can fully capture the effect of interaction between the design variables. For efficiency, genetic aggregation surrogate models are constructed using the responses of computational fluid dynamic (CFD) analysis. Global sensitivity indices for the performance measures of stay vane and casing have been evaluated using the surrogate models. It is found that less than three design variables among 12 are effective in the design process of stay vane and casing in reaction hydraulic turbines.

A Global Sensitivity Approach for the Dynamic Response of Printed Wiring Boards

1995 ◽  
Vol 117 (1) ◽  
pp. 7-13 ◽  
Author(s):  
H. Jensen ◽  
A. O. Cifuentes
Keyword(s):  
Dynamic Response ◽  
Probabilistic Approach ◽  
Printed Wiring Boards ◽  
Global Sensitivity ◽  
Probabilistic Distribution ◽  
Design Variables ◽  
Sensitivity Approach ◽  
General Method

This paper is concerned with the sensitivity of the dynamic response of printed wiring boards (PWB). A general method to study the sensitivity of the response of the board as a function of the variability of the design variables is presented. The method, which is based on a probabilistic approach, assumes that the design variables belong to a given interval and follow a known probabilistic distribution.

scholarly journals Evaluation of capabilities of different global sensitivity analysis techniques for building energy simulation: experiment on design variables

2021 ◽  
Vol 21 (2) ◽  
pp. 89-111
Author(s):  
Arthur Santos Silva ◽  
Enedir Ghisi
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Building Performance ◽  
Commercial Building ◽  
Building Performance Simulation ◽  
Global Sensitivity ◽  
Heating And Cooling ◽  
Design Variables ◽  
Cooling Loads

Abstract The objective of this study is to investigate the capabilities of different global sensitivity analysis methods applied to building performance simulation, i.e. Morris, Monte Carlo, Design of Experiments, and Sobol methods. A single-zone commercial building located in Florianópolis, southern Brazil, was used as a case study. Fifteen inputs related to design variables were considered, such as thermal properties of the construction envelope, solar orientation, and fenestration characteristics. The performance measures were the annual heating and cooling loads. It was found that each method can provide different visual capabilities and measures of interpretation, but, in general, there was little difference in showing the most influent and least influent variables. For the heating loads, the thermal transmittances were the most influent variables, while for the cooling loads, the solar absorptances stood out. The Morris method showed to be the most feasible method due to its simplicity and low computational cost. However, as the building simulation model is still complex and non-linear, the variance-based method such as the Sobol is still necessary for general purposes.

scholarly journals Enhance Derivative Design Considering Global Sensitivity of Design Parameters

2013 ◽  
Author(s):  
Hyeong-UK Park ◽  
Kamran Behdinan ◽  
Joon Chung ◽  
Jae-Woo Lee
Keyword(s):  
Sensitivity Analysis ◽  
Design Process ◽  
Life Cycle Cost ◽  
Global Sensitivity Analysis ◽  
Collaborative Optimization ◽  
Design Parameters ◽  
Fast Method ◽  
Engineering Product ◽  
Global Sensitivity ◽  
Design Variables

An engineering product design considers derivatives to reduce the life cycle cost and to increase the efficiency on operation when it has new demands. The proposed design process in this study obtains derivative designs based on sensitivity of design variable. The efficiency and accuracy of the derivative design process can be enhanced by implementing global sensitivity analysis. Sensitivity analysis sensors the design variables accordingly and variables with low sensitivity for objective function can be neglected, since computational effort and time is not necessary for a design with less priority. In this research, e-FAST method code for global sensitivity analysis module was developed and implemented on Multidisciplinary Design Optimization (MDO) problem. The wing design was considered for MDO problem that used aerodynamics and structural disciplines. The global sensitivity analysis method was applied to reduce the number of design variables and Collaborative Optimization (CO) was used as MDO method. This research shows the efficiency of reduction of dimensionality of complex MDO problem by using global sensitivity analysis. In addition, this result shows important design variables for design requirement to student when they solving design problem.

scholarly journals Improved Artificial Bee Algorithm for Reliability-based Optimization of Truss Structures

2017 ◽  
Vol 11 (1) ◽  
pp. 235-243
Author(s):  
Li Yancang ◽  
Wang Jie ◽  
Liu Libo ◽  
Zhao Jie
Keyword(s):  
Truss Structure ◽  
Truss Structures ◽  
Engineering Practice ◽  
Discrete Variable ◽  
Small Interval ◽  
Bee Colony ◽  
Design Variables ◽  
Reliability Based Optimization ◽  
Set Up ◽  
Discrete Variable Optimization

Introduction: In order to optimize the reliability of the truss structure more effectively, an improved artificial bee colony algorithm based on small interval was proposed and employed to the engineering practice. Method: First, the optimization model based on the reliability was set up. In the model, the bars were treated as design variables, and the total weight was the object function. Then, the comparisons with other methods in solving the truss structure discrete variable optimization demonstrate the feasibility and effectiveness of the improved algorithm. Conclusion: This work provides a new method for the reliability-based optimization of truss structures.

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