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The study of the development of π-periodic perturbations of a converging spherical shock wave leading to cumulation limitation is performed. The study is based on 3D hydrodynamic calculations with the Carnahan – Starling equation of state for hard sphere fluid. The method of solving the Euler equations on moving (compressing) grids allows one to trace the evolution of the converging shock wave front with high accuracy in a wide range of its radius. The compression rate of the computational grid is adapted to the motion of the shock wave front, while the motion of the boundaries of the computational domain satisfy the condition of its supersonic velocity relative to the medium. This leads to the fact that the solution is determined only by the initial data at the grid compression stage. The second order TVD scheme is used to reconstruct the vector of conservative variables at the boundaries of the computational cells in combination with the Rusanov scheme for calculating the numerical vector of flows. The choice is due to a strong tendency for the manifestation of carbuncle-type numerical instability in the calculations, which is known for other classes of flows. In the three-dimensional case of the observed force, the carbuncle effect was obtained for the first time, which is explained by the specific nature of the flow: the concavity of the shock wave front in the direction of motion, the unlimited (in the symmetric case) growth of the Mach number, and the stationarity of the front on the computational grid. The applied numerical method made it possible to study the detailed flow pattern on the scale of cumulation termination, which is impossible within the framework of the Whitham method of geometric shock wave dynamics, which was previously used to calculate converging shock waves. The study showed that the limitation of cumulation is associated with the transition from the Mach interaction of converging shock wave segments to a regular one due to the progressive increase in the ratio of the azimuthal velocity at the shock wave front to the radial velocity with a decrease in its radius. It was found that this ratio is represented as a product of a limited oscillating function of the radius and a power function of the radius with an exponent depending on the initial packing density in the hard sphere model. It is shown that increasing the packing density parameter in the hard sphere model leads to a significant increase in the pressures achieved in a shock wave with broken symmetry. For the first time in the calculation, it is shown that at the scale of cumulation termination, the flow is accompanied by the formation of high-energy vortices, which involve the substance that has undergone the greatest shock-wave compression. Influencing heat and mass transfer in the region of greatest compression, this circumstance is important for current practical applications of converging shock waves for the purpose of initiating reactions (detonation, phase transitions, controlled thermonuclear fusion).

In traditional CNC systems, each segment of a piecewise linear trajectory is described by a separate block of the control program. In this case, a trapezoidal trajectory of movement is formed, and the stitching of individual sections is carried out at zero values of speed and acceleration. Increased productivity is associated with continuous processing, which in modern CNC systems is achieved through the use of spline interpolation. For a piecewise linear trajectory, which is basic for most products, the most appropriate is a first-degree spline. However, even in the simplest case of spline interpolation, the closed nature of the basic software from leading manufacturers of CNC systems limits the capabilities of not only developers, but also users. Taking this into account, the purpose of this work is a detailed study of the structural organization and operation algorithms of the simulation model of piecewise linear spline interpolation. Limitations on jerk and acceleration are considered as the main measure to reduce dynamic processing errors. In this case, special attention is paid to the S-shaped shape of the speed curve in the acceleration and deceleration sections. This is due to the conditions for the implementation of spline interpolation, one of which is the continuity of movement, which is ensured by the equality of the first and second derivatives when joining sections of the trajectory. Such a statement corresponds to the principles of implementing combined control systems of a servo electric drive, which provide partial invariance to control and disturbing effects. The reference model of a spline interpolator is adopted as the basis of the structural organization. The issues of processing scaling, which are based on a decrease in the vector speed in relation to the base value, are also considered. This allows increasing the accuracy of movements. It is shown that the range of changes in the speed of movements can be more than ten thousand, and is limited only by the speed control capabilities of the actuators.

The objective of this article is to develop a model for a realistic description of a mixed flow of two types of vehicles (cars and trucks) on multi-lane highways, taking into account differences not only in the technical characteristics of vehicles (dimensions, maximum speed), but also differences in driving strategies. The article includes a literature review, including publications of recent years, confirming the relevance of modeling heterogeneous traffic flows.

The new model takes into account that trucks have a lower maximum speed compared to cars and are slower to start. They are less maneuverable, so it is more difficult for them to change lanes. In addition, the movement of trucks can be regulated by some restrictive rules, for example, a ban on driving in left lanes.

The model is based on the cellular automata theory, which allows for a comprehensive description of the features of individual flow components. At each time step, the state of the automaton cells is updated in two stages — changing lanes and moving forward. The algorithms of both substeps for cars and trucks differ. Each vehicle is assigned a number of parameters: vehicle type, length, maximum speed, lane change strategy, in-lane movement strategy.

The model is implemented as a software package that allows simulating traffic on various sections of the road network — intersections, sections with narrowing and widening of the road, entrances and exits from the highway. In this work, a road section with a varying number of lanes and a straight multi-lane section with a virtual detector were selected for testing the model. The results are presented in the form of local speed-density and flow-density diagrams, as well as spatiotemporal speed diagrams.

To test the model, a number of problems with different percentages of passenger cars and trucks are solved, which allows demonstrating a drop in the capacity of elements of the road network with an increase in the share of trucks in the flow. The cases of uniform distribution by lanes and the restriction to the right lane for trucks are simulated. The positive effect of introducing a ban on the movement of trucks in left lanes on a multi-lane highway is illustrated.

Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and strongly correlated trajectories that reduce the statistical efficiency of return estimation. These well-known limitations often lead to slow convergence and unstable learning dynamics, particularly in settings where only a small fraction of collected trajectories is actually informative for policy improvement. A key challenge is therefore to identify a compact yet diverse subset of episodes that contributes most to the accuracy of value estimates while preserving sufficient exploration of the environment. To address this challenge, we reformulate episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using quantum-inspired sampling techniques. Our method, MC+ QUBO, inserts a combinatorial filtering step into the standard MC policy-evaluation pipeline: given a batch of trajectories, it selects a subset that maximizes cumulative reward and encourages broad state-space coverage. This selection procedure is expressed as a QUBO model, where linear terms favor high-return episodes, quadratic terms penalize redundancy between trajectories, and additional coupling terms can be used to enforce coverage-related constraints or promote structural diversity. Within this framework, we investigate two black-box QUBO solvers: Simulated Quantum Annealing (SQA), which emulates tunneling-based exploration of the search landscape, and Simulated Bifurcation (SB), a dynamical-systems-based iterative optimization method. Both solvers demonstrate the ability to efficiently navigate the combinatorial structure of the trajectory-selection problem and to handle batch sizes that are otherwise computationally expensive for exhaustive or deterministic search. Experiments in a finite-horizon GridWorld environment show that MC+QUBO consistently outperforms vanilla MC in convergence speed, stability of return estimates, and final policy quality. These results highlight the promise of quantum-inspired optimization as a practical decision-making subroutine within reinforcement-learning algorithms, offering a scalable way to improve sample efficiency without modifying the underlying learning paradigm.

The aim of research is to develop software system for solving gas dynamic problem in multiply connected integration domains of regular shape by high-performance computing system. Comparison of the various technologies of parallel computing has been done. The program complex is implemented using multithreaded parallel systems to organize both multi-core and massively parallel calculation. The comparison of numerical results with known model problems solutions has been done. Research of performance of different computing platforms has been done.

Статья представляет собой пример компьютерного моделирования в области инженерной механики. Численным методом находятся поля скорости в гидроциклоне, которые недоступны прямому измерению. Рассматривается численное моделирование трехмерной гидродинамики на основе k-ε RNG модели турбулентности в гидроциклоне со встроенным инжектором, содержащим 5 тангенциально направленных сопла. Показано, что направление движения инжектируемой жидкости зависит от расхода жидкости через инжектор. Расчеты показывают в соответствии с экспериментами, что зависимость сплит-параметра от расхода инжектируемой жидкости имеет немонотонный характер, связанный с отношением мощности основного потока и инжектируемой жидкости.

Complex geometric organization of subcellular structures such as photosynthetic or mitochondrial membranes determines mechanism of electron and proton transfer processes. We propose new approach in modeling processes, where geometric shape of membranes is accurately taken into account. Different stages of charge transfer process are simulated using different approaches, which are integrated into a combined model. We implemented this model as software which utilizes parallel computations on high-performance clusters and GPUs for better performance.

Mathematical simulation on unsteady natural convection in a closed porous cylindrical cavity having finite thickness heat-conducting solid walls in conditions of convective heat exchange with an environment has been carried out. A boundary-value problem of mathematical physics formulated in dimensionless variables such as stream function and temperature on the basis of Darcy–Boussinesq model has been solved by finite difference method. Effect of a porous medium permeability 10^–5≤Da<∞, ratio between a solid wall thickness and the inner radius of a cylinder 0.1≤h/L≤0.3, a thermal conductivity ratio 1≤λ_1,2≤20 and a dimensionless time on both local distributions of isolines and isotherms and integral complexes reflecting an intensity of convective flow and heat transfer has been analyzed in detail.

The article contains results of modeling a meteor entering dense atmosphere layers using Galerkin’s method and smoother particle hydrodynamics. Numerical simulations were run using experimental data gathered for the Chelyabinsk meteor while varying the meteor material characteristics and its orientation when entering the atmosphere.

In this paper, we present new parallel algorithms for finite element analysis implemented in the FEStudio software framework. We describe the programming model of finite element method, which supports parallelism on different stages of numerical simulations. Using this model, we develop parallel algorithms of numerical integration for dynamic problems and local stiffness matrices. For constructing and solving the systems of equations, we use the CUDA programming platform.