The forced oscillations of a four-axle vehicle with a double spring suspension are considered. The motion of a system with six degrees of freedom can be represented with sufficient accuracy by a system with two degrees of freedom. Therefore, the body of the vehicle has two degrees of freedom: sideways movement and wagging (jumping and galloping carts will be neglected). It is assumed that the rheological properties of the spring (suspension) are different and obey the hereditary theory of Boltzmann-Volterra viscoelasticity. As the core of heredity, the Koltunov-Rzhanitsyn core is used, which has weakly singular features of the Abel type. Effective computational algorithms for solving problems based on the use of quadrature formulas have been developed. For numerical calculation, a computer program has been compiled, the results of which are presented in the form of graphs. The influence of the rheological properties of the suspension on the forms of vertical and angular movement of the body is studied. It was found that due to the suspension viscosity, the amplitude of vertical and angular oscillations decreases and the frequency increases.
At present, a significant part of optimization problems, particularly questions of combinatorial optimization, are considered NP-complete problems. When solving optimization problems, the neural network approach increases the probability of obtaining an optimal solution. The traveling salesman problem is considered a test optimization problem. This problem was solved using the Hopfield neural network. In solving optimization problems, numerous computation processes and computation time are required. To improve performance and increase the program's speed, there are cases of inappropriate purchase of additional programs and tools, and involvement of additional services. In these cases, parallel computing technologies are used to give an effective result. Based on the developed algorithms, several computational experiments were carried out. The analysis of the obtained results showed that the algorithms of artificial neural networks proposed by us, in comparison with the algorithms created based on Hopfield neural networks, are characterized by low resource consumption and efficiency in terms of high speed. But, it should be noted that if the volume of tasks is very large, neural network algorithms may become less efficient due to longer computation. In such cases, it is usually advisable to use evolutionary algorithms. In particular, the study considers using the bee swarm algorithm for parallel computing technologies. Solving optimization problems using the bee swarm algorithm in parallel computing technologies can be significantly efficient and fast.
Methods of calculating the shell structures from composite materials and studying the concentration of strains near the holes are devoted to a rather large number of works. However, most of the research was carried out within the framework of the classical Kirchhoff-Love hypothesis, which does not take into account the interlayer and lateral shifts characteristic of composite materials. Application of finite elements method to solve the problems on orthotropic shells of composites, weakened by several holes is reported within the improved theory of Timoshenko type. A numerical algorithm has been developed using the finite element method and a software package has been implemented on a computer that makes it possible to solve problems of stress concentration near two apertures in shells of composite materials. Shearing parameter has been studied for its influence on stress concentration near two circular holes internal pressure for orthotropic spherical shells. Specific results are presented for the cases of large and small holes, taking into account the rigidity of the reinforcing elements. Thus, this software package can be applied to the calculation of the elements of shell structures from orthotropic composite materials weakened by several holes.
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