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Reliability Based Design Optimization for Multiaxial Fatigue Damage Analysis Using Robust Hybrid Method

Yaich, A.; Kharmanda, G. LU ; El Hami, Abdelkhalak; Walha, L. and Haddar, M. (2017) In World Journal of Mechanics
Abstract

The purpose of the Reliability-Based Design Optimization (RBDO) is to find the best compromise between safety and cost. Therefore, several methods, such as the Hybrid Method (HM) and the Optimum Safety Factor (OSF) method, are developed to achieve this purpose. However, these methods have been applied only on static cases and some special dynamic ones. But, in real mechanical applications, structures are subject to random vibrations and these vibrations can cause a fatigue damage. So, in this paper, we propose an extension of these methods in the case of structures under random vibrations and then demonstrate their efficiency. Also, a Robust Hybrid Method (RHM) is then developed to overcome the difficulties of the classical one. A... (More)

The purpose of the Reliability-Based Design Optimization (RBDO) is to find the best compromise between safety and cost. Therefore, several methods, such as the Hybrid Method (HM) and the Optimum Safety Factor (OSF) method, are developed to achieve this purpose. However, these methods have been applied only on static cases and some special dynamic ones. But, in real mechanical applications, structures are subject to random vibrations and these vibrations can cause a fatigue damage. So, in this paper, we propose an extension of these methods in the case of structures under random vibrations and then demonstrate their efficiency. Also, a Robust Hybrid Method (RHM) is then developed to overcome the difficulties of the classical one. A numerical application is then used to present the advantages of the modified hybrid method for treating problem of structures subject to random vibration considering fatigue damage.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Multiaxial fatigue damage, Random vibrations, Reliability Based Design Optimization, Robust Hybrid Method
in
World Journal of Mechanics
pages
16 pages
publisher
Scientific Research
external identifiers
  • scopus:85021998000
ISSN
1727-7191
DOI
10.1017/jmech.2017.44
language
English
LU publication?
yes
id
d38658b1-01cf-4d5a-9cef-992d26459f8b
date added to LUP
2017-07-24 10:45:04
date last changed
2017-07-24 10:45:04
@article{d38658b1-01cf-4d5a-9cef-992d26459f8b,
  abstract     = {<p>The purpose of the Reliability-Based Design Optimization (RBDO) is to find the best compromise between safety and cost. Therefore, several methods, such as the Hybrid Method (HM) and the Optimum Safety Factor (OSF) method, are developed to achieve this purpose. However, these methods have been applied only on static cases and some special dynamic ones. But, in real mechanical applications, structures are subject to random vibrations and these vibrations can cause a fatigue damage. So, in this paper, we propose an extension of these methods in the case of structures under random vibrations and then demonstrate their efficiency. Also, a Robust Hybrid Method (RHM) is then developed to overcome the difficulties of the classical one. A numerical application is then used to present the advantages of the modified hybrid method for treating problem of structures subject to random vibration considering fatigue damage.</p>},
  author       = {Yaich, A. and Kharmanda, G. and El Hami, Abdelkhalak and Walha, L. and Haddar, M.},
  issn         = {1727-7191},
  keyword      = {Multiaxial fatigue damage,Random vibrations,Reliability Based Design Optimization,Robust Hybrid Method},
  language     = {eng},
  month        = {07},
  pages        = {16},
  publisher    = {Scientific Research},
  series       = {World Journal of Mechanics},
  title        = {Reliability Based Design Optimization for Multiaxial Fatigue Damage Analysis Using Robust Hybrid Method},
  url          = {http://dx.doi.org/10.1017/jmech.2017.44},
  year         = {2017},
}