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CANTELI, A. CASTILLO, E., DÍAZ-SALAMANCA, D., MUNIZ-CALVENTE, M., SEITL, S.
Original Title
Advances in low cycle fatigue probabilistic modeling
Type
journal article in Web of Science
Language
English
Original Abstract
New advances in the probabilistic S-N model proposed by Castillo and Canteli are presented. The requirements for a S-N model derivation to be valid are emphasized, in particular, that of the compatibility between the statistical distributions of lifetime and stress reference variable. The definition of the generalized reference variable (GRV) in the S-N field, as GRV = psi & sdot;sigma M, where psi is a non-dimensional factor derived from the sigma - epsilon law of the material, allows the former model to be applied to LCF data. The new model ensures the incontrovertible and unitary definition of the scatter band in the S-N field and the justification of an asymptotic lower limit of the lifetime, N0. As a result, the stress- and strain-based approaches can be envisaged as a unique probabilistic psi sigma M -N approach applicable in the three conventional, i.e., LCF, HCF and VHCF domains. An extension as the psi sigma M -R -N model that includes the stress ratio effect is presented. The utility of the model is confirmed with the assessment of LCF data from different external experimental campaigns.
Keywords
S -N model; Low cycle Fatigue (LCF); Probabilistic assessment; Generalized reference variable (GRV); psi non-dimensional factor; Experimental validation
Authors
CANTELI, A.; CASTILLO, E., DÍAZ-SALAMANCA, D., MUNIZ-CALVENTE, M., SEITL, S.
Released
7. 8. 2024
Publisher
ELSEVIER
Location
AMSTERDAM
ISBN
0167-8442
Periodical
Theoretical and Applied Fracture Mechanics
Year of study
133
Number
8
State
Kingdom of the Netherlands
Pages from
1
Pages to
16
Pages count
URL
https://www.sciencedirect.com/science/article/pii/S0167844224003616?via%3Dihub
BibTex
@article{BUT193654, author="Alfonso Fernández {Canteli} and Enrique {Castillo} and Diego {DÍAZ-SALAMANCA} and Miguel {Muňiz-Calvente} and Stanislav {Seitl}", title="Advances in low cycle fatigue probabilistic modeling", journal="Theoretical and Applied Fracture Mechanics", year="2024", volume="133", number="8", pages="1--16", doi="10.1016/j.tafmec.2024.104611", issn="0167-8442", url="https://www.sciencedirect.com/science/article/pii/S0167844224003616?via%3Dihub" }