Application of Neural Networks for Modeling Shock-Wave Processes in Aluminum

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.

Automated summary

A technique has been developed using artificial neural networks to describe the relationship between stresses, strains, and the onset of plastic flow in metal single crystals. The datasets for training are generated using molecular dynamics modeling, showing promising results in simulating shock wave propagation and studying dislocation nucleation.