Mechanics of Solids (about journal) Mechanics of Solids
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IssuesArchive of Issues2021-3pp.326-342

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Gracheva N.A., Lekanov M.V., Mayer A.E., and Fomin E.V., "Application of neural networks for modeling shock-wave processes in aluminum," Mech. Solids. 56 (3), 326-342 (2021)
Year 2021 Volume 56 Number 3 Pages 326-342
DOI 10.3103/S0025654421030031
Title Application of neural networks for modeling shock-wave processes in aluminum
Author(s) Gracheva N.A. (Chelyabinsk State University, Chelyabinsk, 454001 Russia)
Lekanov M.V. (Chelyabinsk State University, Chelyabinsk, 454001 Russia)
Mayer A.E. (Chelyabinsk State University, Chelyabinsk, 454001 Russia, mayer@csu.ru)
Fomin E.V. (Chelyabinsk State University, Chelyabinsk, 454001 Russia)
Abstract A technique has been developed for the use of artificial neural networks to describe the nonlinear relationship between the components of stresses and strains (tensor equation of state) and the onset of plastic flow (homogeneous nucleation of dislocations) in metal single crystals by the example of aluminum. Datasets for training neural networks are generated using molecular dynamics (MD) modeling of uniform deformation of representative volumes of a single crystal. Axisymmetric deformed states are considered when the symmetry axis coincides with the [100] direction of the single crystal. The trained neural networks are used as approximating functions within the dislocation plasticity model generalized to the case of finite deformations. It is used to simulate the propagation of shock waves arising from the collision of plates. In the case of nanoscale plates, a comparison is made with direct MD simulation of the process. In an ideal single crystal, the elastic precursor retains a constant amplitude corresponding to the threshold of homogeneous nucleation of dislocations, while in a deformed single crystal it has a significantly lower amplitude and rapidly decays with distance.
Keywords shock waves, equation of state, plastic deformation, single crystal, molecular dynamics modeling, artificial neural networks, homogeneous nucleation of dislocations
Received 17 November 2020Revised 20 November 2020Accepted 24 November 2020
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