Criteria for Fatigue Failure of Materials: Application in Fatigue Assessment of Structures

Petinov, Sergei V.; Guchinsky, Ruslan

doi:10.4028/www.scientific.net/aef.26.1

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stress concentration factor (SCF) is an important parameter in fatigue analysis of offshore joints [1]. The fatigue life of a tubular joint can be determined based on hot-spot stress (HSS) and S-N curve [2,3]. HSS can be easily calculated using nominal stress and stress concentration factor (SCF).…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load

Iqbal,

Karuppanan,

Perumal

et al. 2023

MSF

View full text Add to dashboard Cite

Stress concentration factor (SCF) is usually used to estimate the fatigue life of an offshore joint. Historically, parametric equations were used to estimate SCF based on a statistical analysis of experimental and finite element analysis (FEA) results, to reduce cost and time. These equations give the SCF at the saddle/crown position for simple joints and basic load cases. However, for modified or defective joints, the location of the maximum SCF can change. In such circumstances, the single-point SCF equation cannot be used to estimate the maximum value of SCF, as its location may have changed from saddle/crown. To our knowledge, there are no general expressions to estimate SCF around the brace axis accurately. As artificial neural networks (ANN) can approximate the trend of complex phenomena better than conventional data fitting, a mathematical model based on ANN is proposed to estimate SCF based on the weights and biases of trained ANN. Nine hundred thirty-seven finite element simulations were performed to generate SCF data for training the ANN. This ANN was used to model an empirical equation for SCF. The proposed empirical model can estimate SCF around the brace axis with less than 5% error. The current study provides a roadmap to using FEA and ANN for empirical modeling of SCF in tubular joints, and this approach can be applied to any joint type, with or without design modification or damage. Once a database of similar equations is available, it can be utilized for quickly estimating SCF instead of costly experimentation and FEA. Optimization of the ANN can further improve the accuracy of the developed mathematical model.

show abstract

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load

Iqbal,

Karuppanan,

Perumal

et al. 2023

MSF

View full text Add to dashboard Cite

show abstract

“…Currently in the rules for fatigue evaluation of structures exposed to intense alternating service loading the Stress-Life (S-N) criteria, versions of the nominal stress approach, hot-spot stress approach and notch-stress approach were suggested based on the use of the stress range as a representation of the present damage. Petinov and Guchinsky (2018) examined the criteria and procedures for assessing the fatigue properties of structures, accompanied by a series of approximations and uncertainties. According to these researchers, the strain-life and inelastic strain energy requirements for fatigue failure and approaches may provide a physically and mechanically more accurate procedures, specific with intrinsic sources of approximations.…”

Section: Introductionmentioning

confidence: 99%

Procedures of Fatigue Analysis by Supporting Direct Load Application on Midship Sections

Özgüç

2020

Trans. Marit. Sci.

View full text Add to dashboard Cite

Fatigue evaluation of ship structures using direct calculation methods to calculate fatigue loads are standard practice today. There are several numerical codes available for use in analyses of these fatigue loads. In addition to the varying degrees of computational complexity associated with fatigue prediction methods, the inherent uncertainties of these procedures are also large. This paper introduces the procedure for stochastic fatigue analysis of typical midship models with direct load transfer applied, where an oil tanker is considered. It also covers a comparison of the results with the component-based approach included in the DNVGL Class Note 30.7: “Fatigue Assessment of Ship Structures”. The "real" case analysis includes both internal pressure loads from tank fluids as well as external pressure adjusted for wet and dry surfaces in the waterline area, according to DNVGL Class Note 30.7. Local fine mesh models of fatigue details have been analysed using the sub-modelling technique. The procedure performs well on a typical midship model apart from the file sizes of the generated load transfer files. With 25 wave periods and 12 different headings, the analysed 3-cargo-hold model (1/2 + 1 + 1/2) in the midship area had to be split into four super elements in order to get the analysis through finite element analyses. The procedure is suitable for vessels where warping (torsion) is of less importance. The described procedures are supported by a developed tool to be used in the analysis procedures. Three details of local fine mesh models such as deck erection butt weld, longitudinal stiffener through web-frame, and bottom erection butt weld have been analysed. The results have been compared with the component-based approach. For some of the details there are comparable results, but for others the results vary significantly. The typical trend is that the details heavily influenced by the external pressure (side longitudinal) give less comparable results than e.g. a detail in the main deck mainly influenced by global loads. A comparison of the effect of reducing versus not reducing the pressure amplitude in the waterline on the fatigue life has also been performed and discussed.

show abstract

An artificial neural network model for determining stress concentration factors for fatigue design of tubular T-joint under compressive loads

Rasul,

Karuppanan,

Perumal

et al. 2024

IJSI

View full text Add to dashboard Cite

PurposeThe stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.Design/methodology/approachA mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.FindingsUsing real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.Practical implicationsEngineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.Originality/valuePrecisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

show abstract

Criteria for Fatigue Failure of Materials: Application in Fatigue Assessment of Structures

Cited by 4 publications

References 14 publications

An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load

An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load

Procedures of Fatigue Analysis by Supporting Direct Load Application on Midship Sections

An artificial neural network model for determining stress concentration factors for fatigue design of tubular T-joint under compressive loads

Contact Info

Product

Resources

About