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.

scholarly journals Particle swarm optimization to produce optimal solution

2018 ◽  
Vol 7 (1.7) ◽  
pp. 210 ◽  
Author(s):  
C Saranya Jothi ◽  
V Usha ◽  
R Nithya
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Information ◽  
Fitness Value

Search-Based Software Testing is the utilization of a meta-heuristic improving scan procedure for the programmed age of test information. Particle Swarm Optimization (PSO) is one of those technique. It can be used in testing to generate optimal test data solution based on an objective function that utilises branch coverage as criteria. Software under test is given as input to the algorithm. The problem becomes a minimization problem where our aim is to obtain test data with minimum fitness value. This is called the ideal test information for the given programming under test. PSO algorithm is found to outperform most of the optimization techniques by finding least value for fitness function. The algorithm is applied to various software under tests and checked whether it can produce optimal test data. Parameters are tuned so as to obtain better results.

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.

An approach for test data generation using program slicing and particle swarm optimization

2014 ◽  
Vol 25 (7-8) ◽  
pp. 2047-2055 ◽  
Author(s):  
Shujuan Jiang ◽  
Dandan Yi ◽  
Xiaolin Ju ◽  
Lingsai Wang ◽  
Yingqi Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Particle Swarm ◽  
Program Slicing ◽  
Test Data Generation ◽  
Data Generation ◽  
Swarm Optimization

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.

Automated and Optimized Software Test Suite Generation Technique for Structural Testing

2016 ◽  
Vol 26 (01) ◽  
pp. 1-13 ◽  
Author(s):  
M. S. Geetha Devasena ◽  
G. Gopu ◽  
M. L. Valarmathi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Test Data ◽  
Structural Testing ◽  
Test Suite ◽  
Test Case ◽  
Hybrid Technique ◽  
Generation Process ◽  
Swarm Optimization ◽  
Test Suite Generation

Software testing consumes 50% of total software development cost. Test case design gains central importance in testing activity with respect to quality. The manual test suite generation is a time consuming and tedious task which needs automation. Unit testing is normally done in stringent time schedules by the developers or rarely by testers. In structural testing, it is not possible to check exhaustively all possible test data and the quality of test is dependent heavily on the performance of single developer or tester. Thus automation and optimization is required in generating test data to assist developer or tester with the selection of appropriate test data. A novel hybrid technique is developed to automate the test suite generation process for branch coverage criteria using evolutionary testing. The hybrid technique applies both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to automatically generate test data. This technique improves efficiency and effectiveness of test case generation process when compared to applying Genetic Algorithm or Particle Swarm Optimization alone. The performance of proposed technique is evaluated and is observed that hybrid technique reduces the number of iterations by 47% when compared to GA and PSO applied separately and it reduces the execution time by 52% than GA and 48% than PSO.

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