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International Journal for Multiscale Computational Engineering

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ISSN Печать: 1543-1649

ISSN Онлайн: 1940-4352

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: 1.4 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: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 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: 0.00034 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: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

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DEFINITION OF THE STIFFNESS MATRIX OF A HIERARCHICAL STRUCTURE BY USING VIRTUAL TESTING AND ARTIFICIAL NEURAL NETWORKS

Том 10, Выпуск 6, 2012, pp. 635-648
DOI: 10.1615/IntJMultCompEng.2012003776
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Краткое описание

In this paper, we consider structures characterized by a definite geometrical hierarchy, such as multilayer wire ropes. We investigate the mechanical behavior, namely, the influence of the hierarchical helix geometry on the stiffness of the cable. It is shown how the stiffness matrix of these structures is different from the usual stiffness matrix of Euler-Bernoulli beams. Furthermore, the dependence of the stiffness coefficients on the twist pitches of the multilevel helixes is also analyzed. A hybrid finite element{artificial neural network approach (ANN-FE) is proposed, suggesting that suitably trained ANNs can replace the module that usually provides the stiffness matrix in an FE code. Finally, a comparison is shown, where results obtained via the FE method are compared with those calculated by an ANN-FE procedure.

ЛИТЕРАТУРА
  1. Bellina, F., Boso, D., Schrefler, B. A., and Zavarise, G., Modeling a multistrand SC cable with an electrical DC lumped network. DOI: 10.1109/TASC.2002.1018666

  2. Boso, D., Pellegrino, C., Galvanetto, U., and Schrefler, B. A., Macroscopic damage in periodic composite materials. DOI: 10.1002/1099-0887(200009)16:9<615::AID-CNM355>3.0.CO;2-2

  3. Boso, D. P., Lefik, M., and Schrefler, B. A., Multiscale analysis of the influence of the triplet helicoidal geometry on the strain state of a Nb3Sn based strand for ITER coils. DOI: 10.1016/j.cryogenics.2005.06.002

  4. Boso, D. P., Lefik, M., and Schrefler, B. A., Thermal and bending strain on Nb3Sn strands.

  5. Boso, D. P. and Lefik, M., Numerical Phenomenology: Virtual testing of the hierarchical structure of a bundle of strands.

  6. Gawin, D., Lefik, M., and Schrefler, B. A., ANN approach to sorption hysteresis within a coupled hygro-thermo-mechanical FE analysis. DOI: 10.1002/1097-0207(20010120)50:2<299::AID-NME20>3.0.CO;2-Y

  7. Hain, M. and Wriggers, P., Numerical homogenization of hardened cement paste. DOI: 10.1007/s00466-007-0211-9

  8. Hertz, J., Krogh, A., and Palmer, G. R., Introduction to the Theory of Neural Computation.

  9. Hu, Y. H. and Hwang, J.-N., Handbook of Neural Network Signal Processing. DOI: 10.1121/1.1480419

  10. Lefik, M. and Schrefler, B. A., Artificial neural network for parameter identifcations for an elasto-plastic model of superconducting cable under cyclic loading. DOI: 10.1016/S0045-7949(02)00162-1

  11. Lefik, M. and Schrefler, B. A., Artificial neural network as an incremental non-linear constitutive model for a finite element code. DOI: 10.1016/S0045-7825(03)00350-5

  12. Lefk, M., Boso, D. P., and Schrefler, B. A., Artificial neural networks in numerical modelling of composites. DOI: 10.1016/j.cma.2008.12.036

  13. Liu, D. S. and Tsai, C. Y., Estimation of thermo-elasto-plastic properties of thin-film mechanical properties using MD nanoindentation simulations and an inverse FEM/ANN computational scheme.

  14. Miehe, C., Schroder, J., and Schotte, J., Computational homogenization analysis in finite plasticity. Simulation of texture development in polycrystalline materials. DOI: 10.1016/S0045-7825(98)00218-7

  15. Nemov, A. S., Boso, D. P., Voynov, I. B., Borovkov, A. I., and Schrefler, B. A., Generalized stiffness coefficients for ITER superconducting cables, direct FE modeling and initial configuration. DOI: 10.1016/j.cryogenics.2009.11.006

  16. Pellegrino, C., Galvanetto, U., and Schrefler, B. A., Numerical homogenisation of periodic composite materials with non-linear material components. DOI: 10.1002/(SICI)1097-0207(19991210)46:10<1609::AID-NME716>3.0.CO;2-Q

  17. Weiss, K. P., Cryogenic laboratory tests for V-I characterisation of subcable samples.

  18. Zanino, R., Boso, D. P., Lefik, M., Ribani P. L., Richard, L. S., and Schrefler, B. A., Analysis of bending effects on performance degradation of ITER-relevant Nb3Sn strand using the THELMA code. DOI: 10.1109/TASC.2008.921336

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