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Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution
During the oil production process, sucker rods are subjected to cyclic alternating load. After a certain number of cycles, a sucker rod can experience fatigue failure. The number of cycles is called fatigue life (N), and the accurate relationship between maximum stress (S) and fatigue life (N) under...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780892/ https://www.ncbi.nlm.nih.gov/pubmed/35057276 http://dx.doi.org/10.3390/ma15020560 |
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author | Cai, Wenbin Li, Wen Xu, Jinze |
author_facet | Cai, Wenbin Li, Wen Xu, Jinze |
author_sort | Cai, Wenbin |
collection | PubMed |
description | During the oil production process, sucker rods are subjected to cyclic alternating load. After a certain number of cycles, a sucker rod can experience fatigue failure. The number of cycles is called fatigue life (N), and the accurate relationship between maximum stress (S) and fatigue life (N) under a certain reliability (P), namely the P-S-N curve, is an important basis for the reliability analysis and fatigue life prediction of sucker rods. The Basquin model, based on log-normal distribution, is widely used for fitting the P-S-N curves of sucker rods. Due to the limitation of this model, it is difficult to extrapolate the conclusion obtained from a finite fatigue region to the high-cycle or ultra-high-cycle fatigue region, which makes it impossible to estimate the fatigue limit of the sucker rod. Compared to the log-normal distribution, Weibull distribution causes the sucker rod to have a minimum safety life, namely the safety life at 100% survival rate, which complies with the fatigue characteristics of the sucker rod and is more in line with the actual situation. In this study, the fatigue data for ultra-high-strength HL and HY grade sucker rods were obtained through experimental fatigue tests. A new fatigue life model was established and the P-S-N curves of two types of ultra-high strength sucker rods were obtained. For HL- and HY-type ultra-high strength sucker rods, the average error between the fitting result and fatigue test value is 1.25% and 4.39%, respectively. Compared to the S-N curve fitting result obtained from the Basquin model commonly used for sucker rods, the new model based on three-parameter Weibull distribution provides better fitting precision and can estimate fatigue limit more accurately, so this model is more suitable for estimating fatigue life and can better guide the design of ultra-high strength sucker rod strings. |
format | Online Article Text |
id | pubmed-8780892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87808922022-01-22 Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution Cai, Wenbin Li, Wen Xu, Jinze Materials (Basel) Article During the oil production process, sucker rods are subjected to cyclic alternating load. After a certain number of cycles, a sucker rod can experience fatigue failure. The number of cycles is called fatigue life (N), and the accurate relationship between maximum stress (S) and fatigue life (N) under a certain reliability (P), namely the P-S-N curve, is an important basis for the reliability analysis and fatigue life prediction of sucker rods. The Basquin model, based on log-normal distribution, is widely used for fitting the P-S-N curves of sucker rods. Due to the limitation of this model, it is difficult to extrapolate the conclusion obtained from a finite fatigue region to the high-cycle or ultra-high-cycle fatigue region, which makes it impossible to estimate the fatigue limit of the sucker rod. Compared to the log-normal distribution, Weibull distribution causes the sucker rod to have a minimum safety life, namely the safety life at 100% survival rate, which complies with the fatigue characteristics of the sucker rod and is more in line with the actual situation. In this study, the fatigue data for ultra-high-strength HL and HY grade sucker rods were obtained through experimental fatigue tests. A new fatigue life model was established and the P-S-N curves of two types of ultra-high strength sucker rods were obtained. For HL- and HY-type ultra-high strength sucker rods, the average error between the fitting result and fatigue test value is 1.25% and 4.39%, respectively. Compared to the S-N curve fitting result obtained from the Basquin model commonly used for sucker rods, the new model based on three-parameter Weibull distribution provides better fitting precision and can estimate fatigue limit more accurately, so this model is more suitable for estimating fatigue life and can better guide the design of ultra-high strength sucker rod strings. MDPI 2022-01-12 /pmc/articles/PMC8780892/ /pubmed/35057276 http://dx.doi.org/10.3390/ma15020560 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cai, Wenbin Li, Wen Xu, Jinze Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution |
title | Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution |
title_full | Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution |
title_fullStr | Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution |
title_full_unstemmed | Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution |
title_short | Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution |
title_sort | study on the p-s-n curve of sucker rod based on three-parameter weibull distribution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780892/ https://www.ncbi.nlm.nih.gov/pubmed/35057276 http://dx.doi.org/10.3390/ma15020560 |
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