Visualization analysis of the results of continual-atomistic modeling of a Coulomb explosion in metals under the influence of ultrashort (fs, ps) laser exposure
V.I. Mazhukin, A.V. Shapranov, M.M. Demin, O.N. Koroleva, A.V. Mazhukin
Accepted: 2023-01-24
Abstract
A continuum-atomistic model has been developed that describes nonequilibrium thermal, hydrodynamic, and electronic processes in metals that occur under the action of ultrashort (fs, ps) laser radiation. A detailed study of two mechanisms of ultrashort laser ablation of Cu was carried out: fast - Coulomb, determined by Coulomb forces, and slow - thermal, realized in the unloading wave after the end of the laser pulse. Modeling showed that the excess nonequilibrium pressure of collectivized electrons plays a leading role in the formation of a strong electric field at the metal-vacuum interface. This effect can be taken as the basis for the Coulomb explosion in metals. The main feature of the work is the widespread use of modern visualization tools for processing and presenting simulation results.
Visualization of liquids flows in microfluidics and plasma channels in nanosecond spark microdischarges by means of digital microscopy
V.A. Dekhtyar, A.E. Dubinov
Accepted: 2022-12-22
Abstract
Application of digital optical microscopes for visualization of single-pulse or pulsed-periodic processes in microfluidics and physics of spark microdischarges is studied. Multiple examples of coagulation processes of liquid microvolumes, nanosecond spark discharges near liquid drops and plant living tissues in a cell-size level are provided.
Computer and physical modeling for the estimation of the capability of application of convolutional neural networks in close-range photogrammetry
V.V. Pinchukov, A.Yu. Poroykov, E.V. Shmatko, N.Yu. Sivov
Accepted: 2022-12-05
Abstract
Close-range photogrammetry is widely used to measure the surface shape of various objects and its deformations. The classic approach for this is to use a stereo pair of images. Images of the pair are captured from different angles using two digital video cameras. The surface shape is measured by triangulating a set of corresponding two-dimensional points from these images using a predetermined location of cameras relative to each other. Various algorithms are used to find these points. Several photogrammetry methods use cross-correlation for this purpose. This paper discusses the possibility of replacing the correlation algorithm with neural networks to determine displacements of small areas in the images. They allow increasing the calculation speed and the spatial resolution of the measurement results. To verify the possibility of using convolutional networks for photogrammetry tasks computer and physical modeling were carried out. For first test, a set of synthetically generated images representing images of the Particle Image Velocimetry method was used. The displacements of particles in the images are known, it allows to estimate the accuracy of processing of such images. For second test, a series of experimental images with surfaces with different deformation was obtained. Computational experiments were performed to process synthetic and experimental images using selected neural networks and a classical cross-correlation algorithm. The limitations on the use of the compared algorithms were determined and their error in reconstructing the three-dimensional shape of the surface was evaluated. Computer and physical modeling have shown the operability and efficiency of neural networks for processing photogrammetry images.
