RBF Model for the Mass Loss of a Brass in Cavitation Field
Keywords:
brass, cavitation, mass loss, mathematical model
Abstract
This article aims at presenting the model of the mass loss of a brass sample in ultrasonic cavitation field in saline water. The experiments done for data collecting was performed in three scenarios. In the first one, the high frequency generator worked at three power levels - 80 W, at the second one - at 120 W, and in the third one - at 180 W. The Model has been built using the series of the mass loss on surface.
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References
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[2]. Young F. E., Cavitation, Mac Graw-Hill, Maidenhead, UK, New York, 1989.
[3]. Rooney J. A., Ultrasound: its Chemical, Physical and Biological Effects, Suslick, VCH, New York, USA 1988.
[4]. Bai L., Yan J., Zeng Z., Ma Y., Cavitation in thin liquid layer: A review, Ultrason. Sonochem., vol. 66, 105092, 2020.
[5]. Bărbulescu A., Marza V., Dumitriu C. S., Patent no RO 123086-B1 (30.09.2010) Installation and method for measuring and determining the effects produced by cavitation in ultrasound field in stationary and circulating media, 2010.
[6]. Bărbulescu A., Models of the voltage induced by cavitation in hydrocarbons, Acta Phys. Pol. B, vol. 37 (10), p. 2919-2931, 2006.
[7]. Bărbulescu A., Dumitriu C. S., Mathematical aspects of the study of the cavitation in liquids, Mathematical Modelling of Environmental and Life Sciences, S. Ion, G. Marinoschi and C. Popa, Eds. București: Editura Academiei Române, p. 7-14, 2006.
[8]. Basumatary J., Nie M., Wood J. K., The synergistic effects of cavitation erosion-corrosion in ship propeller materials, J. Bio- Tribo-corros., 1, p. 1-12, 2015.
[9]. Petkovsek M., Dular M., Simultaneous observation of cavitation structures and cavitation erosion, Wear, vol. 300, p. 55-64, 2013.
[10]. Wharton J. A., Stokes K. R., The influence of nickel-aluminium bronze microstructure and crevice solution on the initiation of crevice corrosion, Electrochim. Acta, 53 (5), p. 2463-2473, 2008.
[11]. Schüssler A., Exner H. E., The corrosion of nickel-aluminium bronzes in seawater-I. Protective layer formation and the passivation mechanism, Corros. Sci., 3 (11), p. 1793-1802, 1993.
[12]. Fortes-Patella R., Choffat T., Reboud J. L., Archer A., Mass loss simulation in cavitation erosion: fatigue criterion approach, Wear, vol. 300, p. 205-215, 2013.
[13]. Bărbulescu A., Dumitriu C. S., Models of the mass loss of some copper alloys, Chem. Bull. Politehnica University (Timişoara), vol. 52 (66), 1-2, p. 120-123, 2007.
[14]. Dumitriu C. S., Bărbulescu A., Studies about the copper base alloys used in naval constructions – modeling the loss mass in different media, Sitech, Craiova, 2007.
[15]. Kruskal W. H., Wallis W. A., Use of ranks in one-criterion variance analysis, J. Am. Stat. Assoc., vol. 47 (260), p. 583-621, 1952.
[16]. Vandeginste B. G. M., Massart D. L., Buydens L. M. C., De Jong S., Lewi P. J., Smeyers-Verbeke J., Artificial Neural Networks. Handbook of Chemometrics and Qualimetrics: Part B, p. 649–699, doi:10.1016/s0922-3487(98)80054-3, 1998.
[17]. Hornik K., Stinchcombe M., White H., Multilayer feedforward networks are universal approximators. Neural Networ, vol. 2 (5), p. 359-366, 1989.
[18]. Bărbulescu A., Dani A., Statistical analysis and classification of the water parameters of Beas River (India), Rom. Rep. Phys., vol. 71, no. 4, art.716, 2019.
[19]. Bărbulescu A., Dumitriu C. S., On the Connection between the GEP Performances and the Time Series Properties, Mathematics, 9 (16), 1853, 2021.
[20]. Bărbulescu A., Dumitriu C. S., Artificial intelligence models for financial time series, Ovidius University Annals, Economic Sciences Series, vol. XXI, Issue 1, p. 685-690, 2021.
[21]. Bărbulescu A., Șerban C., Caramihai S., Assessing the soil pollution using a genetic algorithm, Rom. J. Phys., vol. 66 (3-4), 80, 2021.
[22]. Jia W., Zhao Ding D. L., An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample, Appl. Soft Comput., vol. 48, p. 373-384, 2016.
[23]. Meng K., Dong Z. Y. Wang D. H., Wong K. P., A self-adaptive RBF neural network classifier for transformer fault analysis, IEEE Trans. Power Syst., vol. 25 (3), p. 1350-1360, 2010.
[24]. Sheta A. F., De Jong K., Time-series forecasting using GA-tuned radial basis functions, Inform. Sci., vol. 133 (3), p. 221-228, 2001.
[25]. Al-Mahasneh A. J., Anavatti S., Garratt M., Pratama M., Applications of General Regression Neural Networks, Dynamic Systems, Digital Systems, Asadpour, V. Ed., IntechOpen, DOI: 10.5772/intechopen.80258, 2018.
[26]. Faris H., Aljarah I., Mirjalili S., Evolving Radial Basis Function Networks using moth-flame optimizer, Handbook of Neural Computation, p. 537-550, 2017.
[27]. Chen S., Hong X., Harris C. J., Orthogonal Forward Selection for Constructing the Radial Basis Function Network with Tunable Nodes, Available at: https://eprints.soton.ac.uk/ 261028/1/36440777.pdf, 2005.
[28]. Orr M. J. L., Optimizing the widths of radial basis functions, Proceedings of the 5th Brazilian Symposium on Neural Networks (Cat. No.98EX209), p. 26-29, doi: 10.1109/SBRN. 1998.730989, 1998.
[29]. Park J., Sandberg I. W., Universal approximation using radial basis function networks, Neural Comput., vol.3, p. 246-257, 1991.
[30]. Moody J., Darken C. J., Fast learning in networks of locally-tuned processing units, Neural Comput., vol. 1, p. 281-294, 1989.
Published
2021-12-15
How to Cite
1.
BĂRBULESCU A, DUMITRIU C Ștefan. RBF Model for the Mass Loss of a Brass in Cavitation Field. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2021 [cited 16Apr.2024];44(4):17-1. Available from: https://www.gup.ugal.ro/ugaljournals/index.php/mms/article/view/4971
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