bluuebunny/crossref_metadata_2025 huggingface dataset:
Topological properties of Sb(111) surface: A first-principles study
Authors: Wang Shuangxi , Zhang Ping
Published: December 2021

First-principles calculations based on the density functional theory were performed to systematically study the electronic properties of the thin film of antimony in (111) orientation. By considering the spin–orbit interaction, for stoichiometric surface, the topological states keep robust for six-bilayer case, and can be recovered in the three-bilayer film, which are guaranteed by time-reversal symmetry and inverse symmetry. For reduced surface doped by non-magnetic Bi or magnetic Mn atom, localized three-fold symmetric features can be identified. Moreover, band structures show that the non-trivial topological states stand for non-magnetic substitutional Bi atom, while can be eliminated by adsorbed or substitutional magnetic Mn atom.
Coupled flow and heat transfer of power-law nanofluids on non-isothermal rough rotary disk subjected to magnetic field
Authors: Pei Yun-Xian , Zhang Xue-Lan , Zheng Lian-Cun , Wang Xin-Zi
Published: December 2021

We study the coupled flow and heat transfer of power-law nanofluids on a non-isothermal rough rotating disk subjected to a magnetic field. The problem is formulated in terms of specified curvilinear orthogonal coordinate system. An improved BVP4C algorithm is proposed, and numerical solutions are obtained. The influence of volume fraction, types and shapes of nanoparticles, magnetic field and power-law index on the flow, and heat transfer behavior are discussed. The obtained results show that the power-law exponents (PLE), nanoparticle volume fraction (NVF), and magnetic field inclination angle (MFIA) have almost no effects on velocities in the wave surface direction, but have small or significant effects on the azimuth direction. The NVF has remarkable influences on local Nusselt number (LNN) and friction coefficients (FC) in the radial direction and the azimuth direction (AD). The LNN increases with NVF increasing while FC in AD decreases. The types of nanoparticles, magnetic field strength, and inclination have small effects on LNN, but they have remarkable influences on the friction coefficients with positively correlated heat transfer rate, while the inclination is negatively correlated with heat transfer rate. The size of the nanoparticle shape factor is positively correlated with LNN.
Nearfield acoustic holography in a moving medium based on particle velocity input using nonsingular propagator
Authors: Dong Bi-Chun , Zhang Run-Mei , Yuan Bin , Yu Chuan-Yang
Published: December 2021

Nearfield acoustic holography in a moving medium is a technique which is typically suitable for sound sources identification in a flow. In the process of sound field reconstruction, sound pressure is usually used as the input, but it may contain considerable background noise due to the interactions between microphones and flow moving at a high velocity. To avoid this problem, particle velocity is an alternative input, which can be obtained by using laser Doppler velocimetry in a non-intrusive way. However, there is a singular problem in the conventional propagator relating the particle velocity to the pressure, and it could lead to significant errors or even false results. In view of this, in this paper, nonsingular propagators are deduced to realize accurate reconstruction in both cases that the hologram is parallel to and perpendicular to the flow direction. The advantages of the proposed method are analyzed, and simulations are conducted to verify the validation. The results show that the method can overcome the singular problem effectively, and the reconstruction errors are at a low level for different flow velocities, frequencies, and signal-to-noise ratios.
Ferroelectric Ba0.75Sr0.25TiO3 tunable charge transfer in perovskite devices
Authors: Chen Zi-Xuan , Sun Jia-Lin , Zhang Qiang , Qian Chong-Xin , Wang Ming-Zi , Feng Hong-Jian
Published: December 2021

Interfacial charge recombination is a main issue causing the efficiency loss of the perovskite solar cells (PSCs). Here, ferroelectric Ba0.75Sr0.25TiO3 (BST) is introduced as a polarization tunable layer to promote the interfacial charge transfer of the PSCs. The coexistence of ferroelectric polarization and charge carriers in BST is confirmed by density functional theory (DFT) calculations. Experimental characterization demonstrates the polarization reversal and the existence of domain in BST film. The BST film conductivity is tested as 2.98 × 10−4 S/cm, which is comparable to the TiO2 being used as the electron transporting layer (ETL) in PSCs. The calculations results prove that BST can be introduced into the PSCs and the interfacial charge transfer can be tuned by ferroelectric polarization. Thus, we fabricated the BST-based PSCs with a champion power conversion efficiency (PCE) of 19.05% after poling.
Post-solitons and electron vortices generated by femtosecond intense laser interacting with uniform near-critical-density plasmas
Authors: Yue Dong-Ning , Chen Min , Zhao Yao , Geng Pan-Fei , Yuan Xiao-Hui , Dong Quan-Li , Sheng Zheng-Ming , Zhang Jie
Published: December 2021

Generation of nonlinear structures, such as stimulated Raman side scattering waves, post-solitons and electron vortices, during ultra-short intense laser pulse transportation in near-critical-density (NCD) plasmas is studied by using multi-dimensional particle-in-cell (PIC) simulations. In two-dimensional geometries, both P- and S-polarized laser pulses are used to drive these nonlinear structures and to check the polarization effects on them. In the S-polarized case, the scattered waves can be captured by surrounding plasmas leading to the generation of post-solitons, while the main pulse excites convective electric currents leading to the formation of electron vortices through Kelvin–Helmholtz instability (KHI). In the P-polarized case, the scattered waves dissipate their energy by heating surrounding plasmas. Electron vortices are excited due to the hosing instability of the drive laser. These polarization dependent physical processes are reproduced in two different planes perpendicular to the laser propagation direction in three-dimensional simulation with linearly polarized laser driver. The current work provides inspiration for future experiments of laser–NCD plasma interactions.
Computational design of ratiometric two-photon fluorescent Zn2+ probes based on quinoline and di-2-picolylamine moieties
Authors: Shao Zhe , Zhang Wen-Ying , Zhao Ke
Published: December 2021

To improve two-photon absorption (TPA) response of a newly synthesized probe, a series of ratiometric two-photon fluorescent Zn2+ sensors based on quinoline and DPA moieties have been designed. The one-photon absorption, TPA, and emission properties of the experimental and designed probes before and after coordination with Zn2+ are investigated employing the density functional theory in combination with response functions. The design consists of two levels. In the first level of design, five probes are constructed through using several electron acceptors or donors to increase accepting or donating ability of the fluorophores. It shows that all the designed probes have stronger TPA intensities at longer wavelengths with respect to the experimental probe because of the increased intra-molecular charge transfer. Moreover, it is found that the probe 4 built by adding an acyl unit has the largest TPA cross section among the designed structures due to the form of longer conjugated length and more linear backbone. One dimethylamino terminal attached along the skeleton can improve TPA intensity more efficiently than two side amino groups. Therefore, in the second level of design, a new probe 7 is formed by both an acyl unit and a dimethylamino terminal. It exhibits that the TPA cross sections of probe 7 and its zinc complex increase dramatically. Furthermore, the fluorescence quantum yields of the designed probes 4 and 7 are calculated in a new way, which makes use of the relation between the computed difference of dipole moment and the measured fluorescence quantum yield. The result shows that our design also improves the fluorescence quantum yield considerably. All in all, the designed probes 4 and 7 not only possess enhanced TPA intensities but also have large differences of emission wavelength upon Zn2+ coordination and strong fluorescence intensity, which demonstrates that they are potential ratiometric two-photon fluorescent probes.
First investigation of eclipsing binary KIC 9026766: analysis of light curve and periodic changes
Authors: Soomandar Somaye , Abedi Abbas
Published: December 2021

We investigate a short-period W UMa binary KIC 9026766 with an orbital period of 0.2721278d in the Kepler field of view. By applying an automated q-search for the folded light curve and producing a synthetic light curve for this object based on the PHOEBE code, we calculate the fundamental stellar parameters. We also analyze the O − C curve of the primary minima. The orbital period changes can be attributed to the combination of an upward quadratic function and light-travel time effect (LTTE) due to a possible third body with a minimum mass of 0.029 M
⊙ and an orbital period of 972.5866 ±0.0041d. The relative luminosity of the primary and secondary eclipses (Min I − Min II) is calculated. The periodogram of the residuals of the LTTE and Min I − Min II show peaks with the same period of 0.8566d. The background effect of two nearby stars on our target is the possible reason for this signal. By considering the amplitudes and periods of the remaining signals in the O − C curve of minima, spot motion is possible.
Investigating the energy distribution of the high-energy particles in the Crab nebula
Authors: Wen Lu , Wu Ke-Yao , Yu Huan , Fang Jun
Published: December 2021

The Crab nebula is a prominent pulsar wind nebula detected in multiband observations ranging from radio to very high-energy γ-rays. Recently, γ-rays with energies above 1 PeV have been detected by the Large High Altitude Air Shower Observatory, and the energy of the most energetic particles in the nebula can be constrained. In this paper, we investigate the broadest spectral energy distribution of the Crab nebula and the energy distribution of the electrons emitting the multiwavelength nonthermal emission based on a one-zone time-dependent model. The nebula is powered by the pulsar, and high-energy electrons/positrons with a broken power-law spectrum are continually injected in the nebula as the pulsar spins down. Multiwavelength nonthermal emission is generated by the leptons through synchrotron radiation and inverse Compton scattering. Using appropriate parameters, the detected fluxes for the nebula can be well reproduced, especially for the γ-rays from 102 MeV to 1 PeV. The results show that the detected γ-rays can be produced by the leptons via the inverse Compton scattering, and the lower limit of the Lorentz factor of the most energetic leptons is ∼ 8.5 × 109. It can be concluded that there exist electrons/positrons with energies higher than 4.3 PeV in the Crab nebula.
Active learning of continuous-time Bayesian networks through interventions*
Authors: Linzner Dominik , Koeppl Heinz
Published: December 2021

We consider the problem of learning structures and parameters of continuous-time Bayesian networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximation of the expected information gain. We show that for CTBNs, a semi-analytical expression for this criterion can be calculated for structure and parameter learning. By doing so, we can replace sampling over experimental outcomes by solving the CTBNs master-equation, for which scalable approximations exist. This alleviates the computational burden of integrating over possible experimental outcomes in high-dimensions. We employ this framework in order to recommend interventional sequences. In this context, we extend the CTBN model to conditional CTBNs in order to incorporate interventions. We demonstrate the performance of our criterion on synthetic and real-world data.
Triple descent and the two kinds of overfitting: where and why do they appear?*
Authors: d’Ascoli Stéphane , Sagun Levent , Biroli Giulio
Published: December 2021

A recent line of research has highlighted the existence of a ‘double descent’ phenomenon in deep learning, whereby increasing the number of training examples N causes the generalization error of neural networks (NNs) to peak when N is of the same order as the number of parameters P. In earlier works, a similar phenomenon was shown to exist in simpler models such as linear regression, where the peak instead occurs when N is equal to the input dimension D. Since both peaks coincide with the interpolation threshold, they are often conflated in the literature. In this paper, we show that despite their apparent similarity, these two scenarios are inherently different. In fact, both peaks can co-exist when NNs are applied to noisy regression tasks. The relative size of the peaks is then governed by the degree of nonlinearity of the activation function. Building on recent developments in the analysis of random feature models, we provide a theoretical ground for this sample-wise triple descent. As shown previously, the nonlinear peak at N = P is a true divergence caused by the extreme sensitivity of the output function to both the noise corrupting the labels and the initialization of the random features (or the weights in NNs). This peak survives in the absence of noise, but can be suppressed by regularization. In contrast, the linear peak at N = D is solely due to overfitting the noise in the labels, and forms earlier during training. We show that this peak is implicitly regularized by the nonlinearity, which is why it only becomes salient at high noise and is weakly affected by explicit regularization. Throughout the paper, we compare analytical results obtained in the random feature model with the outcomes of numerical experiments involving deep NNs.
An analytic theory of shallow networks dynamics for hinge loss classification*
Authors: Pellegrini Franco , Biroli Giulio
Published: December 2021

Neural networks have been shown to perform incredibly well in classification tasks over structured high-dimensional datasets. However, the learning dynamics of such networks is still poorly understood. In this paper we study in detail the training dynamics of a simple type of neural network: a single hidden layer trained to perform a classification task. We show that in a suitable mean-field limit this case maps to a single-node learning problem with a time-dependent dataset determined self-consistently from the average nodes population. We specialize our theory to the prototypical case of a linearly separable data and a linear hinge loss, for which the dynamics can be explicitly solved in the infinite dataset limit. This allows us to address in a simple setting several phenomena appearing in modern networks such as slowing down of training dynamics, crossover between rich and lazy learning, and overfitting. Finally, we assess the limitations of mean-field theory by studying the case of large but finite number of nodes and of training samples.
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent*
Authors: Liao Zhenyu , Couillet Romain , Mahoney Michael W
Published: December 2021

This article characterizes the exact asymptotics of random Fourier feature (RFF) regression, in the realistic setting where the number of data samples n, their dimension p, and the dimension of feature space N are all large and comparable. In this regime, the random RFF Gram matrix no longer converges to the well-known limiting Gaussian kernel matrix (as it does when N → ∞ alone), but it still has a tractable behavior that is captured by our analysis. This analysis also provides accurate estimates of training and test regression errors for large n, p, N. Based on these estimates, a precise characterization of two qualitatively different phases of learning, including the phase transition between them, is provided; and the corresponding double descent test error curve is derived from this phase transition behavior. These results do not depend on strong assumptions on the data distribution, and they perfectly match empirical results on real-world data sets.
Reconstruction of pairwise interactions using energy-based models*
Authors: Feinauer Christoph , Lucibello Carlo
Published: December 2021

Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics. Closely connected is the problem of inverse statistical mechanics, where the goal is to infer the parameters of such models given observed data. An open problem in this field is the question of how to train these models in the case where the data contain additional higher-order interactions that are not present in the pairwise model. In this work, we propose an approach based on energy-based models and pseudolikelihood maximization to address these complications: we show that hybrid models, which combine a pairwise model and a neural network, can lead to significant improvements in the reconstruction of pairwise interactions. We show these improvements to hold consistently when compared to a standard approach using only the pairwise model and to an approach using only a neural network. This is in line with the general idea that simple interpretable models and complex black-box models are not necessarily a dichotomy: interpolating these two classes of models can allow to keep some advantages of both.
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification*
Authors: Mignacco Francesca , Krzakala Florent , Urbani Pierfrancesco , Zdeborová Lenka
Published: December 2021

We analyze in a closed form the learning dynamics of the stochastic gradient descent (SGD) for a single-layer neural network classifying a high-dimensional Gaussian mixture where each cluster is assigned one of two labels. This problem provides a prototype of a non-convex loss landscape with interpolating regimes and a large generalization gap. We define a particular stochastic process for which SGD can be extended to a continuous-time limit that we call stochastic gradient flow. In the full-batch limit, we recover the standard gradient flow. We apply dynamical mean-field theory from statistical physics to track the dynamics of the algorithm in the high-dimensional limit via a self-consistent stochastic process. We explore the performance of the algorithm as a function of the control parameters shedding light on how it navigates the loss landscape.
When do neural networks outperform kernel methods?*
Authors: Ghorbani Behrooz , Mei Song , Misiakiewicz Theodor , Montanari Andrea
Published: December 2021

For a certain scaling of the initialization of stochastic gradient descent (SGD), wide neural networks (NN) have been shown to be well approximated by reproducing kernel Hilbert space (RKHS) methods. Recent empirical work showed that, for some classification tasks, RKHS methods can replace NNs without a large loss in performance. On the other hand, two-layers NNs are known to encode richer smoothness classes than RKHS and we know of special examples for which SGD-trained NN provably outperform RKHS. This is true even in the wide network limit, for a different scaling of the initialization. How can we reconcile the above claims? For which tasks do NNs outperform RKHS? If covariates are nearly isotropic, RKHS methods suffer from the curse of dimensionality, while NNs can overcome it by learning the best low-dimensional representation. Here we show that this curse of dimensionality becomes milder if the covariates display the same low-dimensional structure as the target function, and we precisely characterize this tradeoff. Building on these results, we present the spiked covariates model that can capture in a unified framework both behaviors observed in earlier work. We hypothesize that such a latent low-dimensional structure is present in image classification. We test numerically this hypothesis by showing that specific perturbations of the training distribution degrade the performances of RKHS methods much more significantly than NNs.
Understanding approximate Fisher information for fast convergence of natural gradient descent in wide neural networks*
Authors: Karakida Ryo , Osawa Kazuki
Published: December 2021

Natural gradient descent (NGD) helps to accelerate the convergence of gradient descent dynamics, but it requires approximations in large-scale deep neural networks because of its high computational cost. Empirical studies have confirmed that some NGD methods with approximate Fisher information converge sufficiently fast in practice. Nevertheless, it remains unclear from the theoretical perspective why and under what conditions such heuristic approximations work well. In this work, we reveal that, under specific conditions, NGD with approximate Fisher information achieves the same fast convergence to global minima as exact NGD. We consider deep neural networks in the infinite-width limit, and analyze the asymptotic training dynamics of NGD in function space via the neural tangent kernel. In the function space, the training dynamics with the approximate Fisher information are identical to those with the exact Fisher information, and they converge quickly. The fast convergence holds in layer-wise approximations; for instance, in block diagonal approximation where each block corresponds to a layer as well as in block tri-diagonal and K-FAC approximations. We also find that a unit-wise approximation achieves the same fast convergence under some assumptions. All of these different approximations have an isotropic gradient in the function space, and this plays a fundamental role in achieving the same convergence properties in training. Thus, the current study gives a novel and unified theoretical foundation with which to understand NGD methods in deep learning.
Generalisation error in learning with random features and the hidden manifold model*
Authors: Gerace Federica , Loureiro Bruno , Krzakala Florent , Mézard Marc , Zdeborová Lenka
Published: December 2021

We study generalised linear regression and classification for a synthetically generated dataset encompassing different problems of interest, such as learning with random features, neural networks in the lazy training regime, and the hidden manifold model. We consider the high-dimensional regime and using the replica method from statistical physics, we provide a closed-form expression for the asymptotic generalisation performance in these problems, valid in both the under- and over-parametrised regimes and for a broad choice of generalised linear model loss functions. In particular, we show how to obtain analytically the so-called double descent behaviour for logistic regression with a peak at the interpolation threshold, we illustrate the superiority of orthogonal against random Gaussian projections in learning with random features, and discuss the role played by correlations in the data generated by the hidden manifold model. Beyond the interest in these particular problems, the theoretical formalism introduced in this manuscript provides a path to further extensions to more complex tasks.
Entropic gradient descent algorithms and wide flat minima*
Authors: Pittorino Fabrizio , Lucibello Carlo , Feinauer Christoph , Perugini Gabriele , Baldassi Carlo , Demyanenko Elizaveta , Zecchina Riccardo
Published: December 2021

The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: the local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, entropy-stochastic gradient descent and replicated-stochastic gradient descent, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet).
On one method of numerical modeling of piezoconductive processes of a two-phase fluid system in a fractured-porous reservoir
Authors: Bobreneva Yu O , Rahimly P I , Poveshchenko Yu A , Podryga V O , Enikeeva L V
Published: December 2021

A method of numerical modeling based on splitting by physical processes of two-phase fluid transfer in a formation with fractured-porous reservoirs is described. Reservoirs of this type have a natural fracture system and are described by the dual porosity model. A four-block mathematical model of the fluid redistribution between a pore-type matrix and a natural fracturing pattern is proposed and studied. The resulting system is complex and entails a number of difficulties associated with a large number of variables and the absence of important properties of a linearized system of equations, such as self-adjointness and symmetry, which are present in the description of piezoconductive processes. The complete splitting by physical processes is carried out to solve this problem. The resulting split model is differentially equivalent to the discrete initial balance equations of the system (conservation of the mass components of the fluids and the total energy of the system), written in divergent form. This approach is associated with a nonlinear approximation of the grid functions in time, which depends on the fraction of the volume occupied by the fluids in the pores, and is easy to implement.
Numerical modeling of multiphase mass transfer processes in fractured-porous reservoirs
Authors: Bobreneva Yu O , Rahimly P I , Poveshchenko Yu A , Podryga V O , Enikeeva L V
Published: December 2021

The paper presents an algorithm for solving the problem of the process of mass transfer of a two-phase fluid in a fractured-porous reservoir in a one-dimensional formulation. The presence of natural fractures in such reservoirs impedes various types of exploration and field development. Fractured-porous reservoirs are characterized by intense exchange fluid flow between fractures and porous blocks. Each system has its own individual set of filtration-capacity parameters, and this fact complicates the problem under consideration. To study the mass transfer of a two-phase fluid in a medium with double porosity, a four-block mathematical model with splitting by physical processes is proposed. The model is described by a system of partial differential equations. The splitting method forms two functional blocks on the water saturation and the piezoconductivity. For the numerical solution of this system, an absolutely stable implicit finite-difference scheme is constructed in the one-dimensional case. On the basis of the proposed difference scheme, pressures and saturations in the fracture system and matrix are obtained.
Study of stiff differential equations of mathematical description of izomerization of pentane-hexane cut process
Authors: Faskhutdinova R I , Faskhutdinov A G , Enikeeva L V , Gubaydullin I M
Published: December 2021

This paper provides a brief overview of the existing definitions of a stiff system of differential equations. Further, on the example of the accepted scheme of chemical transformations of the catalytic isomerization process of the pentane-hexane fraction, the stiffness of the system of differential equations was studied. In the course of the work, a method for studying the direct kinetic problem for stiffness is presented. In the Matlab software, the results of solving a system of differential equations by five methods (solvers) were compared. The given method can be tried for solving other problems of chemical kinetics.
Parallel implementation of the primer search algorithm for loop-mediated isothermal amplification
Authors: Akhmetzianova L U , Davletkulov T M , Gubaidullin I M , Islamgulov A R
Published: December 2021

In the paper, the implementation of an algorithm of search for primers in a DNA sequence with a size varying between one nucleotide and multiple million nucleotides was discussed. This analysis was done within the objective of finding a set of six specific primers that are used for a conduction of the loop-mediated isothermal amplification (LAMP). For the fastest search result possible, a parallel search for the primer with the use of the Rabin-Karp Algorithm which enables the search for a primer’s entry in DNA sequence in each thread was proposed. A new software for search for primers was developed using Python with BioPython library which implements the algorithm.
Application of the genetic algorithm method in the problem of chemical kinetics of the oxidative regeneration process
Authors: Dubinets O V , Gubaidullin I M , Uzyanbaev R M , Vovdenko M K , Lapshin I G
Published: December 2021

Annotation. One of the main problems in chemical kinetics is the establishment of the mechanisms of complex chemical reactions. The inverse problem of chemical kinetics is understood as the determination of the dependence of the concentration of the participating components on the basis of experimental data obtained from a laboratory installation for the oxidative regeneration of coked catalysts. One of the main methods used in inverse problems the genetic algorithm. The algorithms considered in the article make it possible to determine the values of the rate constants of the considered chemical stages.
Parallel algorithms for solving diffusion-convection problems on a multiprocessor computer system using hybrid MPI / OpenMP technology
Authors: Atayan A , Dolgov V
Published: December 2021

The paper deals with the mathematical models, algorithms and software for mathematical modeling of coastal systems’ water pollution spreading dynamics under various unfavorable phenomena of natural and artificial genesis, developed for high-performance cluster systems. Methods for partitioning the computational domain for solving diffusion-convection problems have been developed, which allow for efficient parallelization of a computationally complex modeling problem, taking into account the architecture of the multiprocessor system used. The developed mathematical models are based on high-precision models of hydrophysics and hydrobiology and take into account the peculiarities of water systems in the south of the Rostov region, as well as factors of hydrobiological dynamics such as microturbulent diffusion and advective transport in various directions, mechanisms of primary and secondary pollution of coastal systems, taking into account currents. The paper presents algorithms for solving a simulated problem based on MPI parallelization technology, as well as based on mixed MPI + OpenMP technology. Numerical experiments have been carried out and the two technologies efficiency comparison has been made in the conditions of computing cluster used.
Processing and assimilation of observation data for the hydrodynamics model of the Black Sea and the Sea of Azov
Authors: Zakharova N B , Sheloput T O , Lezina N R , Shutyaev V P , Parmuzin E I , Agoshkov V I
Published: December 2021

This work is aimed at using the marine data of the Shared Use Centre (SUC) “IKI-Monitoring” in the variational assimilation procedures of the Informational Computational System (ICS) “INM RAS - Black Sea”. SUC “IKI - Monitoring” is a tool for obtaining remote sensing observations on the Earth state. In the paper observation data information is given, data processing procedures are described, algorithms for the assimilation of the information received and several specific features of the numerical model used are presented. Results of the variational assimilation of two sets of observation data are presented and discussed. Numerical experiments have confirmed the possibility of using incomplete data from satellites in the problems of modelling the sea area.
Two-dimensional mathematical model of autowave process in thin surface layer of liquid dispersed nanosystem (magnetic fluid)
Authors: Chekanov V , Kovalenko A , Diskaeva E N , Kirillova E
Published: December 2021

In this work, a two-dimensional mathematical model of an autowave process taking place in a thin near-electrode layer of a magnetic fluid is constructed. The model is represented by a boundary value problem, which consists of a solution domain, a system of equations, initial and boundary conditions. The mathematical model is implemented in the COMSOL Multiphysics physical process modeling system. Comparison of the results of the numerical solution with experimental data prove the adequacy of the developed model.
Simulation of control and stabilization of the lubricant-coolant flow
Authors: Gladckih D , Studennikova S
Published: December 2021

The subject of the research is a control system of coolant supply. The results of system simulation in MathLab application program package for solving problems of technical calculations are presented and the analysis of transients is carried out. Reliability and productivity of a machine tool module depends on the quality of coolant supply control. This problem is actual for CNC machines, used for drilling holes with hard-alloy drills. of cutting fluid through the cutting zone depends on the state of chips in the drill hole channels. In the article the simulation of pump control as a function of changing flow rate is performed. The conclusion of the conducted research is the proposed model of the system of supply and flow of lubrication-cooling fluid, which allows analyzing the performance of the object under study.
Mathematical modeling and multiobjective optimization complex catalyst hydroalumination reaction of olefins with diisobutylaluminium hydride
Authors: Koledina K F , Gubaydullin I M , Koledin S N
Published: December 2021

A mathematical model for the catalyst hydroalumination reaction of olefins with diisobutylaluminium hydride has been developed. In solving the direct kinetic problem applies multi-step method Gere variable order. When solving systems of ordinary differential equations in chemical kinetics, it is necessary to fulfill the balance relations at each sampling point. That ensures the fulfillment of the law of conservation of matter and the convergence of the numerical method. For the catalytic reaction of hydroalumination olefins in the presence of the organoaluminum compound diisobutylaluminum hydride, the problem of multicriteria optimization reaction conditions was solved based on a detailed kinetic model. The solutions found make it possible to optimally select the reaction conditions to achieve the maximum yield of target products, which can be based on the subsequent introduction of the laboratory reaction into production.
Mathematical model of shallow water self-purification process
Authors: Sukhinov A I , Belova Y V , Nikitina A V , Chistyakov A E
Published: December 2021

The paper covers the model of shallow water self-purification processes. The proposed mathematical model of biological kinetics is based on a system of non-stationary convection-diffusion-reaction equations with nonlinear terms, taking into account the water flow movement, gravitational sedimentation of impurities, microturbulent diffusion, and the detritus decomposition as a result of activity the aerobic and anaerobic bacteria. Discretization is performed on the basis of a linear combination of central and Upwind Leapfrog difference schemes, which makes it possible to increase the solution accuracy of biological kinetics problem at large values of the grid Péclet number (Peh > 2). To solve high-dimensional SLAEs, a modified alternating-triangular method was used.
Modelling vibrations of axial piezoelectric generator with active base
Authors: Haldkar R K , Parinov I A , Cherpakov A V , Shilyaeva O V
Published: December 2021

Modelling of an axial-type piezoelectric generator (PEG) is considered. PEG is an integral part of the system for converting mechanical vibration energy from the environment into electrical energy. The energy generator has an axial type of the configuration of elements, aimed on using bending and compressive loads simultaneously on piezoelectric elements. The base of the generator is made as an active pinching. A feature of PEG is that the generator has two types of piezoelectric elements: (1) elements located on the substrate in the form of a bimorph and (2) piezoelectric elements of a cylindrical shape, fixing the generator base, located on the same axis. PEG has a symmetrical structure about the center of proof mass. The results of modal and harmonic analysis of vibrations are given for vibration excitation of the PEG base in a certain frequency range. The analysis of the output characteristics is given.
First integrals of the system “platform with pendulum” and controlling of it
Authors: Bratishchev A V
Published: December 2021

The problem of controlling the movement of a movable platform with a hinged fixed pendulum is considered. This pair is interpreted as a system of stationary connected points one of which moves freely in a horizontal plane. In the first problem, a control is synthesized that ensures the movement of the platform in a limited area under any initial conditions. In the second problem, a control is synthesized that stabilizes the oscillations of the pendulum in a given fixed vertical plane relative to the suspension point. For the problem of control synthesis, the first integrals of a free system are found and used in the article.
Mathematical model of the influence of the rheology of lubricating compositions on the safety of rolling stock movement
Authors: Pinevich Elena , Lazarev Yuriy , Bolgarov Nikolay , Altynov Dmitry , Fatyushin Yuri
Published: December 2021

The article deals with the search for rational and balanced solutions to ensure maximum safety of rolling stock movement, taking into account the minimization of costs and technical measures. The paper presents a mathematical model for calculating the effect of rheological characteristics of lubricants on the service life of heavy-loaded friction units of rolling stock. The scheme of interaction of a wheel with a rail at a single-point contact is presented in the form of contacts of two cylinders of infinite length. The results of numerical analysis of the found analytical dependences of the influence of the plasticity parameter on the coefficient of friction, the influence of the plasticity parameter on the supporting force created by the lubricant layer during the movement of surfaces are presented. The influence of the coefficient of friction on the safety of rolling stock movement in the curved sections of the track is established.
Comparison of the design features of an electromechanical converter with a discrete secondary part with the classical installation options
Authors: Kuimov D N , Minkin M S
Published: December 2021

In the present article, devices with a discrete secondary part are considered, which act as means of intensifying many technological processes of mixing and dispersing materials. In the classic version, the device has a number of disadvantages. To eliminate the shortcomings, the design of an electromechanical converter with a discrete secondary part was developed, which differs from the classical version by a magnetic system capable of implementing the process of controlling the movement of a large set of ferromagnetic elements along a pre-developed trajectory.
Process fluid as a technological system element of vibro-impact parts processing
Authors: Prokopets G , Mordovtsev A
Published: December 2021

Vibro-impact processing refers to finishing processing methods, which largely determine the operational part properties. It is shown that the parameter optimization of the vibro-impact processing is a very promising area of improving the part quality and reducing their manufacturing cost.One of the most important technological system elements is the process fluid. As a result of the priory information analysis, factors that determine the parameters of the vibro-impact processing were identified.Because of theoretical studies, the process fluid parameters and flushing mode influence degree on the process parameters and treated part quality indicators was determined. The process fluid is presented as a set of unit volumes that form an elastically deformable matrix. The necessary list of initial data for determining the maximum allowable process fluid amount in the mass load volume has been identified.The dependence for determining the minimum required process fluid volume is presented. A complex parameter that characterizes the change in the load mass volume in one oscillations cycle, together with the working chamber oscillation frequency and the process fluid fluidity, which determine both the fluid flow process nature (turbulent) and the flow rate inside the load mass is proposed. Based on the complex parameter, the load mass flushing speed is determined. It is shown that at this stage, the numerical value of the parameter can only be determined experimentally. Its theoretical definition is a promising area of further research.
Investigation of the boundary layer zone with an aerodynamic flow around an axisymmetric spindle-shaped body for solving conjugate problems of heat and mass transfer
Authors: Novoselova T , Tolmacheva L , Palii A , Akopdjanyan J
Published: December 2021

The article discusses the possibility of calculating the thickness of the boundary layer when flowing around an axisymmetric spindle-shaped body without using empirical similarity coefficients. For this, the use of physical analogy of processes is proposed. The necessary flow conditions are described. The two-dimensional Laplace equation is solved for the near-surface region of the laminar flow around the body, obtained by rotating a curve of a given shape. When solving the problems of conjugate heat transfer, the regularities of the interaction of the flow with the body surface are taken into account, which, as a result, is reduced to the joint solution of the boundary layer equations describing the flow field and the heat conduction equations describing the propagation of temperature fields inside and outside the body. In view of the complexity or impossibility of the analytical solution of such problems, it is customary to resort to numerical methods for solving these equations. Even the numerical solution of the conjugate heat transfer problem is associated with a huge number of calculations, the availability of computing power and significant time costs. Therefore, it is customary to solve such problems in a quasi-stationary approximation, which imposes certain restrictions on the scope of application
On extending the life of nuclear reactors
Authors: Kramskoi A V , Lyudmirsky Y G , Zhidkov M E , Kramskaia M I
Published: December 2021

To extend the service life of nuclear reactors, witness samples from the shells of the core of the reactor vessel are placed in their core. According to the requirements in force in the industry, the reference samples are loaded into the reactor plant unloaded up to the design stresses. This can lead to a biased assessment of the possible extension of the reactor’s life. In connection with the above, in order to assess the mutual influence of operating factors and the stress-strain state of the base metal and welded joints on embrittlement, the reference specimens must be loaded with a force that causes the maximum possible stresses in the specimens during the operation of the reactor. On the basis of domestic and international experience, a test procedure, design and loading scheme for compact witness samples are proposed for modeling and assessing the mutual influence of operating factors and stress-strain state on the object under study (VVER power reactor vessel). For VVER RPVs, the duration of the additional service life should be confirmed by the justification that by the end of the additional service life, the fracture toughness values of the base metal and metal of the welded seams located in the irradiation zone will allow without destruction to withstand all operational and emergency loads, as well as loads at hydraulic tests.
Modeling of protective plasma spray ceramic coating made of clad powder mixture
Authors: Tsareva I , Krivina L , Berdnik O
Published: December 2021

To solve the problem of increasing the adhesive-cohesive strength of plasma multifunctional coatings used to protect parts of power and mechanical engineering equipment components from wear and corrosion, a ceramic coating of the “Al2O3 - Ni” system, obtained from a powder mixture based on corundum clad with nickel, is proposed for use and studied. The coating was applied by high-energy plasma powder deposition (on the “Thermoplasma-50” installation) to the intermetallic sublayer of the “Ni-Co-Cr-Al-Y” system. The aim of this work was to study the micro structure and phase composition of the powder mixture of oxide ceramics clad with a refractory metal component (nickel), as well as the plasma coating formed from this powder material. According to the results of the research, it was found that the powder mixture clad with nickel has a multiphase composition (Ni+l-Al2O3+r-Al2O3), a spherical morphology of particles. From this powder material, a coating with a phase composition (Ni+L -Al2O3+J ’-L J u Ll) is formed, characterized by a layered microstructure with a columnar structure of oxide grains and nickel interlayers. The coating has high hardness and adhesive-cohesive strength, low coefficient of friction and is recommended for protection against wear of energy and mechanical engineering parts.
Modeling features of the technological process of forestry machines parts strengthening by the method of gas-thermal spraying
Authors: Kretinin V , Teppoev A , Sokolova V
Published: December 2021

The efficiency of the working bodies of tillage forestry machines is determined by the sharpness of their soil-cutting elements. The purpose of the study is to substantiate the modes of the technological process of strengthening and recovery of cutting working forestry machines by the method of gas-flame spraying of wear-resistant coatings to increase their wear resistance and preserve the blade sharpness by implementing the self-sharpening effect, which allows increasing their life by 2-3 times. Technical and economic indicators of the gas-flame spraying process, as well as the scope of its application, depend on how well the technological modes of the spraying process are selected. The relevance of the topic is due to the need to increase the durability of tillage tools by strengthening their surfaces treatment. Such effective methods include the technology of recovery and strengthening of the working bodies of tillage machines using the technology of gas-flame spraying. In this regard, the issues of conducting research have become particularly relevant: to identify the relationships of technological parameters during the gas-flame application of wear-resistant coatings; to change the strength characteristics of the material of parts during their recovery, providing the necessary reliability and durability.