Reliability of some axisymmetric shell structure by the response function method and the generalized stochastic perturbation technique

2016 ◽  
pp. 279-282
Author(s):  
M Kamiński ◽  
B Pokusiński
Keyword(s):  
Response Function ◽  
Shell Structure ◽  
Function Method ◽  
Perturbation Technique ◽  
Stochastic Perturbation ◽  
Axisymmetric Shell ◽  
Response Function Method ◽  
Stochastic Perturbation Technique

scholarly journals Symbolic Computing in Probabilistic and Stochastic Analysis

2015 ◽  
Vol 25 (4) ◽  
pp. 961-973 ◽  
Author(s):  
Marcin Kamiński
Keyword(s):  
Stochastic Analysis ◽  
Automatic Differentiation ◽  
Perturbation Technique ◽  
Finite Difference Methods ◽  
Stochastic Perturbation ◽  
Structural Response ◽  
Symbolic Computing ◽  
Response Function Method ◽  
Stochastic Perturbation Technique ◽  
Analytical Approaches

Abstract The main aim is to present recent developments in applications of symbolic computing in probabilistic and stochastic analysis, and this is done using the example of the well-known MAPLE system. The key theoretical methods discussed are (i) analytical derivations, (ii) the classical Monte-Carlo simulation approach, (iii) the stochastic perturbation technique, as well as (iv) some semi-analytical approaches. It is demonstrated in particular how to engage the basic symbolic tools implemented in any system to derive the basic equations for the stochastic perturbation technique and how to make an efficient implementation of the semi-analytical methods using an automatic differentiation and integration provided by the computer algebra program itself. The second important illustration is probabilistic extension of the finite element and finite difference methods coded in MAPLE, showing how to solve boundary value problems with random parameters in the environment of symbolic computing. The response function method belongs to the third group, where interference of classical deterministic software with the non-linear fitting numerical techniques available in various symbolic environments is displayed. We recover in this context the probabilistic structural response in engineering systems and show how to solve partial differential equations including Gaussian randomness in their coefficients.

scholarly journals Stochastic Sensitivity Analysis of Cylindrical Shell

2016 ◽  
Vol 16 (2) ◽  
pp. 35-42
Author(s):  
Maksym Grzywiński
Keyword(s):  
Sensitivity Analysis ◽  
Thin Shell ◽  
Taylor Expansion ◽  
Perturbation Technique ◽  
Stochastic Perturbation ◽  
Random Variable ◽  
Engineering Practice ◽  
Stochastic Sensitivity ◽  
Stochastic Sensitivity Analysis ◽  
Stochastic Perturbation Technique

Abstract The paper deals with some chosen aspects of stochastic sensitivity structural analysis and its application in the engineering practice. The main aim of the study is to provide the generalized stochastic perturbation technique based on classical Taylor expansion with a single random variable. The study is illustrated by numerical results concerning an industrial thin shell structure modeled as a 3-D structure.

Analysis of Heat Transfer by the Response Function Method

2020 ◽  
Vol 40 (1) ◽  
pp. 67-69
Author(s):  
M. G. Kosov ◽  
A. P. Kuznetsov ◽  
A. V. Kapitanov ◽  
A. V. Oleinik
Keyword(s):  
Heat Transfer ◽  
Response Function ◽  
Function Method ◽  
Response Function Method

Using an Integrated Response-Function Method to Explore Agro-Climatic Suitability for Spring Soybean Growth in North China

2011 ◽  
Vol 50 (6) ◽  
pp. 1354-1361 ◽  
Author(s):  
Yingbin He ◽  
Yanmin Yao ◽  
Huajun Tang ◽  
Youqi Chen ◽  
Jianping Li ◽  
...  
Keyword(s):  
Response Function ◽  
Function Method ◽  
North China ◽  
Coefficient Of Determination ◽  
Climatic Suitability ◽  
Crop Response ◽  
Weighting Method ◽  
Early Maturity ◽  
Response Function Method ◽  
Integrated Response

AbstractTo understand agro-climatic suitability for spring soybean growth in north China, an integrated crop-response-function method was developed. This method includes crop-response functions for temperature, precipitation, and sunshine and is assessed by a weighting method based on the coefficient of determination. The results show that the most suitable area (S1) for spring soybean growth occupied approximately 21.35% of the total area of north China. Among three types of spring soybeans of early maturity, middle maturity, and late maturity, middle maturity was the most suitable variety to grow in the study area, covering nearly 1.133 × 106 km2 or about 99.75% of the total area of S1. As a result of this study, the authors suggest that breeders pay more attention to middle-maturity cultivars in north China. The findings from this study may provide useful information for policy makers issuing guidelines for agricultural production.

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