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dc.contributor.authorKhandouch, Younes
dc.contributor.authorAassif, El Houcein
dc.contributor.authorAgounad, Said
dc.contributor.authorMaze, Gérard
dc.identifier.citationApplied and Computational Mechanics. 2020, vol. 14, no. 2, p. 163-178.en
dc.identifier.issn1802-680X (Print)
dc.identifier.issn2336-1182 (Online)
dc.format16 s.cs
dc.publisherUniversity of West Bohemiaen
dc.rights© University of West Bohemiaen
dc.subjectválcová skořepinacs
dc.subjectpoměr poloměrucs
dc.subjectobvodové vlnycs
dc.subjectsnížené mezní frekvencecs
dc.subjectmodální izolační pláncs
dc.subjectumělé neuronové sítěcs
dc.titleDevelopment of an artificial neural network model for estimating the radius ratio of a one-layered cylindrical shellen
dc.description.abstract-translatedThe results obtained from previous studies on the acoustic scattering of a plane wave by an elastic cylindrical shell, show that the acoustic resonances of the shell are related to its physical and geometrical properties. In order to estimate the radius ratio of an air-field immersed cylindrical shell, an approach based on artificial neural networks was proposed, which uses the reduced cutoff frequencies of circumferential waves that propagate around the cylindrical shell. The reduced cutoff frequencies of circumferential waves are extracted using modal isolation plan representation. The proposed approach allows us to estimate accurately the values of the radius ratio of the copper cylindrical shell, as well as it can help us to resolve other problems related to acoustic scattering. Furthermore, it can be used to estimate other parameters of the cylindrical shell starting from the characteristics of which it is disposed. The approach proposed in this study does not present any approximation as in the case of the proper mode theory.en
dc.subject.translatedcylindrical shellen
dc.subject.translatedradius ratioen
dc.subject.translatedcircumferential wavesen
dc.subject.translatedreduced cutoff frequenciesen
dc.subject.translatedmodal isolation planen
dc.subject.translatedartificial neural networksen
Appears in Collections:Volume 14, Number 2 (2020)
Volume 14, Number 2 (2020)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/42435

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