Fatigue Design Curve Construction for Test Data with Linear/Linearized and Universal Slope Characteristics

2015 ◽  
Author(s):  
Zhigang Wei ◽  
Limin Luo ◽  
Shengbin Lin
Keyword(s):  
Test Data ◽  
Design Curve ◽  
Fatigue Design

Characteristic S-N Curves for Fatigue Design of Titanium Risers

2002 ◽  
Author(s):  
Knut O. Ronold ◽  
Stig Wa¨stberg
Keyword(s):  
Fatigue Test ◽  
Test Data ◽  
Base Material ◽  
Offshore Structures ◽  
Defect Size ◽  
Statistical Analyses ◽  
Stress Range ◽  
Fatigue Design ◽  
Range Interval ◽  
Lower Tolerance

A recommended practice for design of titanium risers is currently being developed as part of Det Norske Veritas’ series of standards and recommended practices for offshore structures. A recommendation is given herein for characteristic S-N curves for use in design of titanium risers against fatigue failure. As a basis for this recommendation, a set of statistical analyses of available fatigue test data have been carried out. Separate analyses have been carried out for base material and welds. The analysis results have been interpreted with respect to mean S-N curves as well as 97.7% lower tolerance bounds with 95% confidence. Characteristic S-N curves for base material and welds, which are not non-conservative with respect to these tolerance bounds, have been proposed. The paper presents the assumptions, the test data, the statistical analyses and their results, and the proposed characteristic S-N curves. The areas of application of the proposed curves are discussed with a particular view to stress range interval, material grade, weld position, temperature, and defect size.

Design Curve Construction Based on Monte Carlo Simulation

2013 ◽  
Author(s):  
Zhigang Wei ◽  
Limin Luo ◽  
Burt Lin ◽  
Dmitri Konson ◽  
Kamran Nikbin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Test Data ◽  
Confidence Level ◽  
Small Sample Size ◽  
Test Sample ◽  
Small Sample ◽  
Tolerance Limit ◽  
Design Curve

Good durability/reliability performance of products can be achieved by properly constructing and implementing design curves, which are usually obtained by analyzing test data, such as fatigue S-N data. A good design curve construction approach should consider sample size, failure probability and confidence level, and these features are especially critical when test sample size is small. The authors have developed a design S-N curve construction method based on the tolerance limit concept. However, recent studies have shown that the analytical solutions based on the tolerance limit approach may not be accurate for very small sample size because of the assumptions and approximations introduced to the analytical approach. In this paper a Monte Carlo simulation approach is used to construct design curves for test data with an assumed underlining normal (or lognormal) distribution. The difference of factor K, which measures the confidence level of the test data, between the analytical solution and the Monte Carlo simulation solutions is compared. Finally, the design curves constructed based on these methods are demonstrated and compared using fatigue S-N data with small sample size.

ASME Code-Case Proposal to Explicitly Quantify the Interaction Between the PWR Environment and Component Surface Finish

2017 ◽  
Author(s):  
Thomas Métais ◽  
Stéphan Courtin ◽  
Laurent De Baglion ◽  
Cédric Gourdin ◽  
Jean-Christophe Le Roux
Keyword(s):  
Fatigue Life ◽  
Surface Finish ◽  
Polished Surface ◽  
Design Curve ◽  
Fatigue Design ◽  
Wide Range ◽  
Asme Code ◽  
The Difference ◽  
The Uk ◽  
Technical Basis

Fatigue rules from ASME have undergone a significant change over the past decade, especially with the inclusion of the effects of BWR and PWR environments on the fatigue life of components. The incorporation of the environmental effects into the calculations is performed via an environmental factor, Fen, which is introduced in ASME BPV code-case N-792 [5], and depends on factors such as the temperature, dissolved oxygen and strain rate. Nevertheless, a wide range of factors, such as surface finish, have a deleterious impact on fatigue life, but their contribution to fatigue life is typically taken through the transition factors to build the fatigue design curve [2] and not in an explicit way, such as the Fen factor. The testing supporting the rules pertaining to Environmental Fatigue Correction Factor (Fen) Method in ASME BPV was performed on specimens with a polished surface finish and on the basis that the Fen factor was applicable without alteration of the historical practice of building the design curve through transition factors. The extensive amount of testing conducted and reported in References [2] and [7] (technical basis for ASME BPV current EAF rules) was used to propose a set of transition coefficients from the mean air curve to the design curve on one hand, and on the other hand to build a Fen factor expression, defined as the difference between the life in air and in PWR environments. The work initiated by AREVA in 2005 [9] [10] [11] demonstrated that there is a clear interaction between the two aggravating effects of surface finish and PWR environment for fatigue damage, which was not experimentally tested in the References [2] and [7]. These results have clearly been supported by testing carried out independently in the UK by Rolls-Royce and AMEC FW [12]. These results are all the more relevant as most NPP components do not have a polished surface finish. Most surfaces are either industrially polished or installed as-manufactured. It was concluded that this proposal could potentially be applicable to a wide range of components and could be of interest to a wider community. EDF/Areva/CEA have therefore authored a code-case introducing the Fen-threshold, a factor which explicitly quantifies the interaction between PWR environment and surface finish. This paper summarizes this proposal and provides the technical background and experimental work to justify this proposal.

A Bayesian Statistics Based Design Curve Construction Method for Test Data With Extremely Small Sample Sizes

2015 ◽  
Author(s):  
Zhigang Wei ◽  
Limin Luo ◽  
Fulun Yang ◽  
Robert Rebandt
Keyword(s):  
Standard Deviation ◽  
Bayesian Statistics ◽  
Test Data ◽  
Small Sample ◽  
Stress Recovery ◽  
Testing Time ◽  
Design Curve ◽  
Construction Methods ◽  
Recovery Technique ◽  
The Mean

Fatigue design curve construction is commonly used for durability and reliability assessment of engineering components subjected to cyclic loading. A wide variety of design curve construction methods have been developed over the last decades. Some of the methods have been adopted by engineering codes and widely used in industry. However, the traditional design curve construction methods usually require significant amounts of test data in order for the constructed design curves to be consistently and reliably used in product design and validation. In order to reduce the test sample size and associated testing time and cost, several Bayesian statistics based design curve construction methods have been recently successfully developed by several research groups. Among all of these methods, an efficient Monte Carlo simulation based resampling method developed by the authors of this paper is of particular importance. The method is based on a large amount of reliable historical fatigue test data, the associated probabilistic distributions of the mean and standard deviation of the failure cycles, and an advanced acceptance-rejection resampling algorithm. However, finite element analysis (FEA) methods and a special stress recovery technique are required to process the test data, which is usually a time-consuming process. A more straightforward approach that does not require these intermediate processes is strongly preferred. This study presents such an approach, in which the only historical information needed is the distribution of the standard deviation of the cycles to failure. The distribution of the mean is directly calculated from the current tested data and the Central Limit Theorem. Neither FEA nor stress recovery technique is required for this approach, and the effort put into design curve construction can be significantly reduced. This method can be used to complement the previously developed Bayesian methods.

Design Curve Construction Based on Two-Stress Level Test Data

2012 ◽  
Author(s):  
Zhigang Wei ◽  
Limin Luo ◽  
Shengbin Lin ◽  
Dmitri Konson ◽  
Fulun Yang
Keyword(s):  
Test Data ◽  
Stress Level ◽  
Design Curve

Assessment of Design S-N Curve for Umbilical Tubes

2009 ◽  
Author(s):  
Inge Lotsberg ◽  
Stein Fredheim
Keyword(s):  
Fatigue Test ◽  
Test Data ◽  
Design Methodology ◽  
Fatigue Analysis ◽  
Analysis Procedure ◽  
Fatigue Design ◽  
Reliable Design

A reliable design methodology for fatigue design of umbilical tubes is required by the industry. A documented design S-N curve is part of such a fatigue analysis procedure. During the last years a number of new fatigue test data have been derived for umbilical tubes. A design S-N curve for as welded and strained tubes during reeling has been debated. The present paper presents an overview of data presented in the literature. In addition it presents a significant number of fatigue test data of umbilical tubes performed at DNV test laboratories in Oslo. In addition it includes an assessment of recommended design S-N curve based on relevant available fatigue test data for umbilical tubes.

scholarly journals Development of a Water Environment Fatigue Design Curve for Austenitic Stainless Steels

2003 ◽  
Author(s):  
Thomas R. Leax
Keyword(s):  
Stainless Steels ◽  
Fatigue Behavior ◽  
Water Environment ◽  
Austenitic Stainless Steels ◽  
Fatigue Curve ◽  
Light Water Reactor ◽  
Design Curve ◽  
Fatigue Design ◽  
American Society ◽  
Design Factors

Technical support is provided for a fatigue curve that could potentially be incorporated into Section III of the American Society of Mechanical Engineers Boiler and Pressure Vessel Code. This fatigue curve conservatively accounts for the effects of light water reactor environments on the fatigue behavior of austenitic stainless steels. This paper presents the data, statistical methods, and basis for the design factors appropriate for Code applications. A discussion of the assumptions and methods used in design curve development is presented.

Sign in / Sign up

Export Citation Format

Share Document