arXiv subjects

A. D. Morozov

Publications and source records attributed to A. D. Morozov.

4 recordsLinked to original sources

arXiv2025

VO2 films grown on TiO2 sub-layer: influence of thickness on structural, electrical and optical properties

Vanadium dioxide with metal-to-insulator transition (MIT) that is triggered by heat, current or light is a promising material for modern active THz/mid-IR metasurfaces and all-optical big data processing systems. Multilayer VO2-based active metasurfaces are urgently needed however several important issues related to VO2 properties in VO2/TiO2/Al2O3 films should be thoroughly examined first. We study electrical, optical and structural properties of VO2 films as well as their composition and switching characteristics as function of the VO2 layer thickness in VO2/TiO2 composites. XRD analysis revealed an epitaxial growth of films with deformation of the monoclinic VO2 lattice to hexagonal symmetry. Reduced VO2 layer thickness from 170 nm to 20 nm results in increased phase transition temperature while the width of the resistance versus temperature hysteresis loop R(T) remains constant at ~6C for all VO2 thicknesses in the range of 20-170 nm. The resistance alteration ratio is reduced from 4.2e3 to 2.7e2 in thinner films. Raman spectra reveal a significant shift of VO2 lattice vibration modes for films thinner than 30 nm claiming a great structural strain whereas modes position for thicker VO2 layers are similar to those in bulk structure. Composition of VO2 films has revealed only a minor alteration of VO2/V2O5 phases ratio from 1.6 to 1.8 when the film thickness has been increased from 20 nm to 50 nm. Investigation of surface elemental composition and valence states of VO2 films revealed that VO2/V2O5 ratio remains practically unchanged with thickness reduction. The study of electrical MIT dynamics revealed the switching time of a 50 nm VO2 film to be as low as 800 ns.

cond-mat.mtrl-sci↗

arXiv2020

Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast

Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractured completions, post-fracturing production analysis reveals that different stages produce very non-uniformly due to a combination of geomechanics and fracturing design factors. Hence, there is a significant room for improvement of current design practices. The workflow is essentially split into two stages. As a result of the first stage, the present paper summarizes the efforts into the creation of a digital database of field data from several thousands of multistage HF jobs on wells from circa 20 different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of a representative dataset of about 5000 data points. Each point in the data base contains the vector of 92 input variables (the reservoir, well and the frac design parameters) and the vector of production data, which is characterized by 16 parameters, including the target, cumulative oil production. Data preparation has been done using various ML techniques: the problem of missing values in the database is solved with collaborative filtering for data imputation; outliers are removed using visualisation of cluster data structure by t-SNE algorithm. The production forecast problem is solved via CatBoost algorithm. Prediction capability of the model is measured with the coefficient of determination (R^2) and reached 0.815. The inverse problem (selecting an optimum set of fracturing design parameters to maximize production) will be considered in the second part of the study to be published in another paper, along with a recommendation system for advising DESC and production stimulation engineers on an optimized fracturing design.

eess.SY↗

arXiv2020

Error estimation in the method of quasi-optimal weights

We examine the problem of construction of confidence intervals within the basic single-parameter, single-iteration variation of the method of quasi-optimal weights. Two kinds of distortions of such intervals due to insufficiently large samples are examined, both allowing an analytical investigation. First, a criterion is developed for validity of the assumption of asymptotic normality together with a recipe for the corresponding corrections. Second, a method is derived to take into account the systematic shift of the confidence interval due to the non-linearity of the theoretical mean of the weight as a function of the parameter to be estimated. A numerical example illustrates the two corrections.

physics.data-an↗

arXiv2014

On bifurcations in degenerate resonance zones

For Hamitonian systems with 3/2 degrees of freedom close to nonlinear integrable and for symplectic maps of the cylinder, bifurcations in degenerate resonance zones are discussed.

math.DS↗