Alexander V. Grayver | Searcharxiv

arXiv2021

Time-Domain Modeling of Three-Dimensional Earth's and Planetary Electromagnetic Induction Effect in Ground and Satellite Observations

Electric currents induced in conductive planetary interiors by time-varying magnetospheric and ionospheric current systems have a significant effect on electromagnetic (EM) field observations. Complete characterization of EM induction effects is difficult owing to non-linear interactions between the three-dimensional (3-D) electrical structure of a planet and spatial complexity of inducing current systems. We present a general framework for time-domain modeling of 3-D EM induction effects in heterogeneous conducting planets. Our approach does not assume that the magnetic field is potential, allows for an arbitrary distribution of electrical conductivity within a planet, and can deal with spatially complex time-varying current systems. The method is applicable to both data measured at stationary observation sites and satellite platforms, and enables the calculation of 3-D EM induction effects in near real-time settings.

physics.geo-ph↗

arXiv2019

Particle streak velocimetry using Ensemble Convolutional Neural Networks

This study reports an approach and presents its open-source implementation for quantitative analysis of experimental flows using streak images and Convolutional Neural Networks (CNN). The latter are applied to retrieve a length and an angle from streaks, which can be used to deduce kinetic energy and directionality (up to 180$^{\circ}$ ambiguity) of an imaged flow. We developed a quick method for generating essentially unlimited number of training and validation images, which enabled efficient training. Additionally, we show how to apply an ensemble of CNNs to derive a formal uncertainty on the estimated quantities. The approach is validated on the numerical simulation of a convenctive turbulent flow and applied to a longitutidal libration flow experiment.

eess.IV↗