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Composites: Mechanics, Computations, Applications: An International Journal

Publication de 4  numéros par an

ISSN Imprimer: 2152-2057

ISSN En ligne: 2152-2073

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.2 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 0.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00004 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.08 SJR: 0.153 SNIP: 0.178 CiteScore™:: 1 H-Index: 12

Indexed in

PARTICLE SWARM OPTIMIZATION-BASED NEURAL NETWORK FOR PREDICTING FATIGUE STRENGTH IN COMPOSITE LAMINATES OF WIND TURBINE BLADES

Volume 6, Numéro 4, 2015, pp. 321-338
DOI: 10.1615/CompMechComputApplIntJ.v6.i4.50
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RÉSUMÉ

In this paper, the fatigue strength in multidirectional (MD)/unidirectional (UD) composite laminates of wind turbine blades is predicted by using particle swarm optimization-based artificial neural networks (PSO-ANN). In the PSO-ANN approach used in this work, the objective function was assessed using the mean square error (MSE) computed as the squared difference between the predicted values and the target values for a number of training set samples. Different materials based on different reinforcing fabrics and resins are compared in terms of the maximum tensile fatigue stress. Tension–tension constant amplitude fatigue loads were applied to thermoset materials including glass-fiber/epoxy, polyester and vinyl esters. All materials were treated in closed molds with resin infusion process, which were molded into their final dogbone shape without machining. The results show that the PSO-ANN can provide accurate fatigue strength prediction for different MD/UD composite laminates under different values of fiber orientation.

CITÉ PAR
  1. Ziane Khaled, Ilinca Adrian, Karganroudi Sasan Sattarpanah, Dimitrova Mariya, Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions, Eng, 2, 3, 2021. Crossref

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