An Inverse Analysis Method for Design Optimization With Both Statistical and Fuzzy Uncertainties

2006 ◽  
Author(s):  
Liu Du ◽  
Kyung K. Choi
Keyword(s):  
Design Optimization ◽  
Inverse Analysis ◽  
Random Variables ◽  
Mean Value ◽  
Performance Measure ◽  
Distribution Functions ◽  
Analysis Method ◽  
Fuzzy Variables ◽  
Analysis And Design ◽  
Inverse Analysis Method

Structural analysis and design optimization have recently been extended to consider various uncertainties. If the statistical data for the uncertainties are sufficient to construct the input distribution function, the uncertainties can be treated as random variables and RBDO is used; otherwise, the uncertainties can be treated as fuzzy variables and PBDO is used. However, many structural design problems include both uncertainties with sufficient data and uncertainties with insufficient data. For these problems, RBDO will yield an unreliable design since the distribution functions of uncertainties are not believable. On the other hand, treating the random variables as fuzzy variables and invoking PBDO may yield too conservative design with a higher optimum cost. This paper proposes a new design formulation using the performance measure approach (PMA). For the inverse analysis, this paper proposes a new most probable/possible point (MPPP) search method called maximal failure search (MFS), which is an integration of the enhanced hybrid mean value method (HMV+) and maximal possibility search (MPS) method. Some mathematical and physical examples are used to demonstrate the proposed inverse analysis method and design formulation.

Possibility-Based Design Optimization Method for Design Problems With Both Statistical and Fuzzy Input Data

2005 ◽  
Vol 128 (4) ◽  
pp. 928-935 ◽  
Author(s):  
Liu Du ◽  
K. K. Choi ◽  
Byeng D. Youn ◽  
David Gorsich
Keyword(s):  
Design Optimization ◽  
Optimum Design ◽  
Membership Function ◽  
Input Data ◽  
Performance Measure ◽  
Sufficient Data ◽  
Design Problems ◽  
Fuzzy Variables ◽  
Fuzzy Input ◽  
The Membership Function

The reliability based design optimization (RBDO) method is prevailing in stochastic structural design optimization by assuming the amount of input data is sufficient enough to create accurate input statistical distribution. If the sufficient input data cannot be generated due to limitations in technical and/or facility resources, the possibility-based design optimization (PBDO) method can be used to obtain reliable designs by utilizing membership functions for epistemic uncertainties. For RBDO, the performance measure approach (PMA) is well established and accepted by many investigators. It is found that the same PMA is a very much desirable approach also for the PBDO problems. In many industry design problems, we have to deal with uncertainties with sufficient data and uncertainties with insufficient data simultaneously. For these design problems, it is not desirable to use RBDO since it could lead to an unreliable optimum design. This paper proposes to use PBDO for design optimization for such problems. In order to treat uncertainties as fuzzy variables, several methods for membership function generation are proposed. As less detailed information is available for the input data, the membership function that provides more conservative optimum design should be selected. For uncertainties with sufficient data, the membership function that yields the least conservative optimum design is proposed by using the possibility-probability consistency theory and the least conservative condition. The proposed approach for design problems with mixed type input uncertainties is applied to some example problems to demonstrate feasibility of the approach. It is shown that the proposed approach provides conservative optimum design.

Development of Inverse Analysis of Heat Conduction and Thermal Stress for Elbow: Part I

2013 ◽  
Author(s):  
Seiji Ioka ◽  
Shiro Kubo ◽  
Mayumi Ochi ◽  
Kiminobu Hojo
Keyword(s):  
Heat Conduction ◽  
Thermal Stress ◽  
Transfer Function ◽  
Surface Temperature ◽  
Outer Surface ◽  
Inverse Analysis ◽  
Temperature History ◽  
Analysis Method ◽  
Inner Surface ◽  
Inverse Analysis Method

Thermal fatigue may develop in piping elbow with high temperature stratified flow. To prevent the fatigue damage by stratified flow, it is important to know the distribution of thermal stress and temperature history in a pipe. In this study, heat conduction inverse analysis method for piping elbow was developed to estimate the temperature history and thermal stress distribution on the inner surface from the outer surface temperature history. In the inverse analysis method, the inner surface temperature was estimated by using the transfer function database which interrelates the inner surface temperature with the outer surface temperature. Transfer function database was calculated by FE analysis in advance. For some patterns of the temperature history, inverse analysis simulations were made. It was found that the inner surface temperature history was estimated with high accuracy.

Research on Interval Reversible Inverse Analysis Method Based on Interval Parameters

2013 ◽  
Vol 706-708 ◽  
pp. 556-559 ◽  
Author(s):  
Jing Bo Su ◽  
Hong Bing Liu ◽  
Hui De Zhao ◽  
Dong Zhang
Keyword(s):  
Inverse Analysis ◽  
Measured Data ◽  
Analysis Method ◽  
Analysis Model ◽  
Parameter Perturbation ◽  
Unknown Parameters ◽  
Interval Parameters ◽  
Linear Elastic ◽  
Interval Analysis Method ◽  
Inverse Analysis Method

In this paper, the interval analysis method is introduced and an uncertainty inverse analysis method is presented. The intervals of unknown parameters can be obtained by the input of measured data. Even for few measured data, the analysis results can be also obtained by the inverse analysis method. And the analysis results can be applied to appraise the uncertainty in interval. Based on parameter perturbation, the reversible inverse analysis model is proposed for linear-elastic problems. A numerical example is given to illustrate the validity of the present method. The influence is illustrated about different measured precisions and different numbers of analyzing parameters on the inverse analysis results. And the conditions of existence or convergence of the solution are given.

Sign in / Sign up

Export Citation Format

Share Document