scholarly journals Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Jae Phil Park ◽  
Subhasish Mohanty ◽  
Chi Bum Bahn ◽  
Saurin Majumdar ◽  
Krishnamurti Natesan
Keyword(s):  
Fatigue Life ◽  
Fatigue Test ◽  
Safety Factor ◽  
Test Data ◽  
Correction Factor ◽  
Data Analytics ◽  
Probabilistic Approach ◽  
Fatigue Curve ◽  
Pressurized Water ◽  
Best Fit

Abstract In general, the fatigue life of a safety critical pressure component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, reactor component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly reactor components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water reactor water on the reactor component. In the discussed approach, in addition to the probabilistic fatigue curves, we suggest using a probabilistic environment correction factor Fen. We assume the probabilistic fatigue curve and Fen would capture the S-N data scatter associated with the bulk effect of material grades, surface finish, strain rate, etc. on the material/component fatigue life.

Fatigue Strength Reduction Factors for Welded Joints: Another View

2007 ◽  
Author(s):  
Arturs Kalnins
Keyword(s):  
Standard Deviation ◽  
Fatigue Strength ◽  
Fatigue Test ◽  
Test Data ◽  
Welded Joints ◽  
Welded Joint ◽  
Fatigue Curve ◽  
Strength Reduction ◽  
Initial Estimate ◽  
Curve Fit

The paper distinguishes between FSRFs that are used for two different purposes. One is to serve as a guideline for an initial estimate of the fatigue strength of a welded joint. That is the purpose of the FSRFs that are given in the ASME B&PV Code and various accompanying documents. If that estimate renders the fatigue strength inadequate, an FSRF can be sought that is limited to the joint under consideration. The paper shows how such FSRFs can be determined from fatigue test data. In order to make it possible to read the allowable cycles from the same design fatigue curve as that used for the FSRFs of the guidelines, a Langer curve [defined by equation (2) in the paper] is used to curve fit the data. The appropriate FSRF is obtained by minimizing the standard deviation between this curve and the data. The procedure is illustrated for girth butt-welded pipes. The illustration shows that for the data used in the analysis, a constant FSRF is applicable to less than one million cycles but not to the high-cycle regime.

A Probabilistic Approach for Determining the Component’s Dimension Under Fatigue Loadings

2009 ◽  
Author(s):  
Xiaobin Le ◽  
Jahan Rasty
Keyword(s):  
Fatigue Test ◽  
Test Data ◽  
Probabilistic Approach ◽  
Random Variables ◽  
Random Variable ◽  
Design Equation ◽  
Probabilistic Design ◽  
Material Fatigue ◽  
Stress Levels ◽  
Design Scenario

Due to inherent scatters in fatigue test data, the P-S-N curves are normally used to describe material fatigue behaviors. For probabilistic component’s design under fatigue loadings, the component’s dimension should be treated as a random variable because every dimension is certainly with a dimension tolerance. In this design scenario, it is difficult to determine the component’s dimension under fatigue loadings by using the P-S-N curves because stress levels are unknown and random variables. In this paper, a probabilistic approach is presented to build a generic probabilistic design equation which is governed by random variables related to material fatigue behaviors, component conditions and fatigue loadings. The generic probabilistic design equation can be used to determine component’s dimension with a given reliability. One example is presented for explaining the approach in details.

Development of a New, Correlated FEA Method of Assessing Mooring Chain Fatigue

2019 ◽  
Author(s):  
Gary H. Farrow ◽  
Andrew E. Potts ◽  
Andrew A. Kilner ◽  
Phillip P. Kurts ◽  
Simon Dimopoulos ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Fatigue Life ◽  
Fatigue Test ◽  
Test Data ◽  
Linear Chain ◽  
Element Analysis ◽  
Chain Link ◽  
Non Linear ◽  
Mooring Chain

Abstract The first phase of the Chain FEARS (Finite Element Analysis of Residual Strength) Joint Industry Project (JIP) aimed to develop guidance for the determination of a rational discard criteria for mooring chains subject to severe pitting corrosion which, based on current code requirements, would otherwise require immediate removal and replacement. Critical to the ability to evaluate the residual fatigue life of a degraded chain, is to have an accurate estimate of the chain in its as-new condition, thereby providing a benchmark for any loss in fatigue life associated with severe corrosion or wear. A large collection of fatigue test data was collated for comparison and to establish underlying trends in as-new mooring chain fatigue response. A non-linear multi-axial Finite Element Analysis (FEA) fatigue assessment method was developed to correlate against available as-new chain link fatigue test data and underlying failure trends as part of the JIP achieving this critical requirement. It was established that the linear FEA fatigue method currently employed in the industry is too simplistic and does not correlate with the fatigue test data, whereas an alternative method of assessing fatigue based on FEA, developed with respect to the DNV B1 material curve, correlates well with the available physical fatigue test data. The FEA method uses a non-linear chain link FEA and multi-axial stress fatigue calculation method to determine an equivalent Stress Magnification Factor (SMF). This method achieves good correlation of predicted utilisations and associated cycles-to-failure with fatigue test data and in respect of critical locations with evidenced failure locations. The method of equivalent SMF calculation accounted for the significant effects on fatigue performance including proof load induced residual stress, mean stress levels and the increase in material fatigue endurance associated with increased steel UTS (i.e. increased offshore mooring chain grade). The analytical method developed in this study achieved a high degree of correlation with as-new chain fatigue test data, and should enable the accurate prediction of fatigue stresses around a link and in particular for irregular geometry associated with corrosion degraded chain links.

Prevention of Pipeline Dent Failure Under Fatigue Loading Conditions

2014 ◽  
Author(s):  
Julien Fontanabona ◽  
Ky Dang Van ◽  
Vincent Gaffard ◽  
Zied Moumni ◽  
Paul Wiet
Keyword(s):  
Fatigue Crack ◽  
Fatigue Life ◽  
Crack Initiation ◽  
Fatigue Test ◽  
Test Data ◽  
Fatigue Crack Initiation ◽  
Full Scale ◽  
Loading Conditions ◽  
Stress Cycle ◽  
Dang Van Criterion

Pipeline dents fatigue life prediction is a subject of high interest for pipelines operating companies. Empreinte is an in-house developed pre and post processor to ABAQUS Finite Element Calculations dedicated to pipeline integrity assessment. Empreinte was first developed and experimentally validated for dents assessments under static loading conditions. As oil but also gas transmission pipelines are submitted to cyclic loading conditions (internal pressure variations, shutdowns, temperature variations …), it was decided to introduce a fatigue life criterion in Empreinte based on the Dang Van theory assuming that local mesoscopic stresses drive fatigue crack initiation. Full scale tests performed for PRCI projects PR-201-927, PR-201-9324 and MD-4-2 were used to validate the proposed fatigue assessment methodology: - the first full scale fatigue test was performed in 1994 on an X52 pipe. For this test, limited material and test data were available. - the second full scale fatigue test was performed in 2007 on an X52 pipe. For this test, material characterization (in particular tensile tests with full stress strain curves) and test data (strain gages measurements, indenter geometry …) were available. Fatigue life assessments were performed following three main steps: 1. using available data: non linear kinematic hardening constitutive laws were identified for the two pipes materials; 2. finite elements elastic-plastic modeling of the denting processes were carried out; 3. fatigue calculations were performed following a new approach using Dang Van criterion for which the parameters were determined from literature data. The elastic shakedown assumption allowed the determination of the local stress cycle from the macroscopic stress cycle. The fatigue criterion integrating the combined influences of shear and hydrostatic stresses was checked on all points of the pipe. Good agreement between experimental and calculated fatigue lives and fatigue crack initiation points was reached. This opens a promising way to assess pipeline defects fatigue life. Efforts are now focused on the standardization of a testing method to identify the Dang Van criterion of a pipeline material at least in air environment.

Comparison of Component Fatigue Test Data With Proposed EAF Methodologies for Austenitic Stainless Steel

2009 ◽  
Author(s):  
Rim Nayal ◽  
Hasan Charkas
Keyword(s):  
Stainless Steel ◽  
Fatigue Life ◽  
Fatigue Test ◽  
Low Cycle Fatigue ◽  
Nuclear Regulatory Commission ◽  
Fatigue Curve ◽  
Asme Code ◽  
Regulatory Commission ◽  
License Renewal ◽  
The Impact

The U.S. Nuclear Regulatory Commission (NRC) currently requires evaluation of the effect of environmental fatigue for both license renewal and new plants. NRC required the use of methodology in EPRI MRP-47, Rev. 1 addressing NUREG/CR-5704, be used for license renewal of stainless steel (SS) components, and NUREG/CR-6909 for use in new plants. These two methodologies are based on applying an environmental correction factor (Fen) on the number of in-air design cycles. These factors are applied to the fatigue usage from each individual range of stress (or range of strain). The focus of this paper is to compare the two aforementioned methodologies; this includes comparison of the fatigue curve as well as the comparison of the environmental correction factors (Fen). Fatigue test results data reported by others are also compared with these two methodologies. It is important to evaluate the impact of using any of those methodologies on the design fatigue life of the components. It is concluded that NUREG/CR-5704 is more severe than NUREG/CR-6909 in the LCF (low-cycle fatigue) regime, while NUREG/CR-6909 is more severe elsewhere, and both NUREG’s extremely underestimate fatigue life in PWR environment. It is also concluded that the current ASME-code fatigue curve for stainless steel reasonably estimates fatigue life in an LWR environment with reasonable margins.

Investigations Into Fatigue Performance of Offshore Mooring Chains

2017 ◽  
Author(s):  
Gary H. Farrow ◽  
Andrew E. Potts ◽  
Daniel G. Washington
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Fatigue Life ◽  
Fatigue Test ◽  
Test Data ◽  
Assessment Method ◽  
Accurate Estimate ◽  
Element Analysis ◽  
Factors Influencing ◽  
Non Linear

The Chain Finite Element Analysis of Residual Strength Joint Industry Project (Chain FEARS JIP) aimed to develop guidance for the determination of a rational discard criteria for mooring chains subject to severe pitting corrosion which would otherwise require immediate removal and replacement. Critical to the ability to evaluate the residual fatigue life of a degraded chain, is to have an accurate estimate of the chain in its as-new condition, thereby providing a benchmark for any loss in fatigue life associated with severe corrosion or wear. A non-linear multi-axial Finite Element Analysis (FEA) fatigue assessment method was developed and correlated against available fatigue test data as part of the JIP achieving this critical requirement. The development of this correlated methodology necessitated a review of: • The available mooring chain fatigue test data, to identify the factors influencing chain fatigue life and failure location. • FEA fatigue methodologies currently employed in the industry. • Current Class Rules relating to fatigue estimation. • The influence of material, manufacturing and operational factors on chain fatigue life. It was established that while the linear FEA fatigue method currently employed in the industry does not correlate with the fatigue test data, the non-linear multi-axial FEA fatigue method developed in the JIP afforded good correlation with test data. It was also demonstrated that the magnitude of mean chain tension and inconsistency in proof loading, as a consequence of the inconsistency in Class Minimum Break Load (MBL) specification, and with respect to chain size and the varying material ductility of steel grades, effects fatigue life. The identified inconsistency in the proofing indicates a likely inconsistency in conservatism embodied in the Class Rules fatigue formulation. Consequently it is possible that chains of certain size and grade may have significantly less fatigue life than anticipated by Class. Further work is recommended to establish a more rational proof load specification and to develop an alternative Class Rules fatigue formulation accounting for the identified factors influencing fatigue.

New probabilistic S-N curves modeling method with small-sample test data of composite materials

2021 ◽  
pp. 095440622199007
Author(s):  
Qiang Ma ◽  
Zongwen An ◽  
Xuezong Bai ◽  
Huidong Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Fatigue Life ◽  
Composite Materials ◽  
Fatigue Test ◽  
Test Data ◽  
Small Sample ◽  
Swarm Optimization ◽  
Conversion Model ◽  
Large Dispersion ◽  
Sample Test

Considering the large dispersion when processing small-sample fatigue test data of composite materials, a new method for modeling probabilistic S- N curves is proposed in terms of the equivalent fatigue lives. An equivalent fatigue life conversion model is first established based on two fundamental assumptions to improve the small-sample information utilized. Subsequently, a backward statistical inference technique improved by particle swarm optimization is used to determine probabilistic S- N curves through the equivalent fatigue lives. Finally, the proposed method is verified in terms of precision and stability by the fatigue test data of carbon eight-harness-satin/epoxy laminate. The results indicate that the proposed method can offer an accurate description of the probabilistic behavior of composite materials with small-sample test data.

Hydrodynamic Study of a Water-Propelled Rocket: An Undergraduate Experiment in Fluid Mechanics

2005 ◽  
Author(s):  
Glen E. Thorncroft ◽  
Christopher C. Pascual
Keyword(s):  
Test Data ◽  
Pedagogical Approach ◽  
Two Phase ◽  
Experimental Conditions ◽  
Initial Water ◽  
Pressurized Water ◽  
A Value ◽  
Hydrodynamic Study ◽  
Best Fit ◽  
Polytropic Exponent

An undergraduate experiment has been developed to measure the performance of a pressurized water rocket and compare test data to an analytical model developed from fluid momentum. A rocket and test stand were developed to measure the net thrust of the rocket, as well the air pressure and temperature inside the rocket, as a function of time. The model compares well to test data from four conditions, combinations of two initial water heights and two initial air pressures. The air in the rocket was assumed to under go polytropic expansion, and a value of 1.1 for the polytropic exponent was found to best fit the model to all experimental conditions. Experimental observations also reveal that, at higher air pressures, the air mixes with the expelled water, resulting in a two-phase flow that reduces the net thrust of the rocket. A pedagogical approach is also developed for the experiment and is described indetail.

An Artificial-Intelligence and Machine-Learning Based Methodology to Conduct Seemingly-Strain-Controlled Fatigue Test in a Pressurized-Water-Reactor-Test-Loop-Autoclave, While Not Controlling the Strain

2021 ◽  
Author(s):  
Subhasish Mohanty ◽  
Joseph Listwan
Keyword(s):  
Fatigue Test ◽  
Test Data ◽  
Nuclear Reactor ◽  
Low Cycle Fatigue ◽  
Cyclic Strain ◽  
Cyclic Hardening ◽  
Test Method ◽  
Displacement Sensor ◽  
Pressurized Water ◽  
Test Loop

Abstract In general, low cycle fatigue evaluation of nuclear reactor components requires strain-controlled fatigue test data such as using strain versus life (e-N) curves. Conducting strain-controlled fatigue tests under in-air condition is not an issue. However, controlling strain in a PWR water test is a challenge, since an extensometer cannot be placed in a narrow autoclave (typically used in a high-temperature-pressure PWR-water loop). This is due to lack of space inside an autoclave that houses the test specimen. In addition, installing a contact-type extensometer in the path of a high-pressure flow can be a challenge. These difficulty of using an extensometer in a PWR-water loop led us to use an outside-autoclave displacement sensor which measures the displacement of pull-rod-specimen assembly. However, in our study (based on in-air fatigue test data), we found that a pull-rod-controlled based fatigue test can lead to substantial cyclic hardening/softening resulting substantially different cyclic strain amplitudes and its rates compared to the desired cyclic strain amplitudes and its rates. In this paper, we propose an AI/ML based technique such as using k-Mean clustering technique to improve the pull-rod-control based fatigue test method, such that the gauge-area strain amplitude and rates can reasonably be achieved. In support of this we present the fatigue test results for both 316 SS base and 81/182 dissimilar-metal-weld specimens.

On the Derivation of Design S-N Curves Based on Limited Fatigue Test Data

2011 ◽  
Author(s):  
Inge Lotsberg ◽  
Knut O. Ronold
Keyword(s):  
Fatigue Life ◽  
Fatigue Test ◽  
Test Data ◽  
Large Scale ◽  
Life Assessment ◽  
Small Scale ◽  
Probability Of Survival ◽  
Fatigue Life Assessment ◽  
Prototype Test ◽  
Material Factor

Qualification of new characteristic S-N curves for fatigue life assessment of structures is considered to be a significant engineering challenge. First, representative fatigue test data for the actual structural connections have to be derived. Then these test data have to be transferred into characteristic S-N curves that represent a predefined probability of survival. Characteristic S-N curves are also often denoted design S-N curves as these curves are often used directly for fatigue life assessment of structures without application of a material factor. A few large scale tests can add significant confidence to a design S-N curve dependent on the type of structural detail to be designed. The reason for this is that a prototype test specimen can be fabricated in a similar way as the actual connection and it is similar in geometry, material characteristics, residual stress, and fabrication tolerances. In addition it can likely be subjected to a more relevant loading and boundary conditions as compared with that of small scale test specimens. When a limited number of test data are available, it is questioned how a characteristic S-N curve can be derived with a well defined probability of survival. The mentioned issues are further considered in this paper together with some recommendations on how to derive design S-N curves based on limited data.

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