Inverse Analysis Method for Concrete Shrinkage Prediction from Short-Term Tests

2012 ◽  
Vol 109 (3) ◽  
Keyword(s):  
Inverse Analysis ◽  
Analysis Method ◽  
Short Term ◽  
Concrete Shrinkage ◽  
Inverse Analysis Method

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.

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.

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.

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