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The article presents an information system for data analysis of electronic operational documentation, the main purpose of which is the storage, collection and analysis of information, as well as the formation of an expert decision and forecasting to improve the quality of user work and the speed of processing large amounts of information. The functional structure of maintaining operational documentation was considered, a description of data flows and business processes was carried out, and the interaction of decision support systems with the subject area was studied.

The paper is devoted to the development of a software complex for predictive modeling of biogeochemical cycles in the Azov Sea on high-performance computer systems in a limited time. The complex development includes the creation of software modules of the computational structure to calculate the concentrations of phosphorus, nitrogen, silicon, sulfur, dissolved oxygen forms and describe in detail phyto- and zooplankton dynamics in the Azov Sea; the integration of various environmental databases, satellite monitoring. The complex is adapted to solve a wide class of predictive problems of water ecology and water resources management. It includes the implementation of developed 3D mathematical model of hydrophysics and biological kinetics on computational grids, consistent with the complex shape of the water coastline. Parallel algorithms have been developed for numerical implementation of water ecology problems, oriented to high-performance computer systems. To increase the calculation efficiency of computational grid fragments assigned to graphics accelerators, an algorithm and its software implementation were developed in the CUDA C language. Based on the developed specialized software tools focused on supercomputers, scenarios for the development of the environmental situation and sustainable development management at biological rehabilitation of the Azov Sea were developed, including forecasts of changes in harmful algae concentration; dynamics of spatial interaction processes between phyto- and zooplankton populations; evolution of biological kinetics processes on the example of plankton interaction.

A method for solving grid equations in areas with geometry "stretched" along one of the spatial directions is proposed. Method proposed for model includes pollutants whose concentration in the Azov Sea, according to the analysis of scientific publications and environmental databases, exceeds the maximum permissible concentration (MPC). The mechanism of the preconditioner formation is described. The estimation of the minimum and maximum eigenvalues is performed. Using object-oriented analysis and design, a class library has been implemented to solve the problem of impurity propagation in a shallow reservoir. The architecture of an information system to support scientific research capable of functioning in conditions of using heterogeneous computing environments is described. The system is based on the requirement of the maximum possible use of free software in order to minimize capital and operational costs. The system uses classes that allow you to read and write information about the geometry of the object under study and the parameters of the calculated grid. Numerical experiments were carried out using the developed software package, including the proposed mathematical model, focused on the multiprocessor computing system (MCS). The dependences of the SLAE solution time on the number of diagonals and the order of the matrix are obtained. The number of iterations for solving grid equations has been experimentally determined depending on the ratio of the number of nodes in spatial coordinates.

The article deals with issues related to increasing the efficiency of working with data on the geometry of the computational domain when solving hydrophysics problems using the finite difference method. The model problem is a system of equations of the pollutant distribution, including the oil and its refined products, in the computational domain – Azov Sea. To describe the computational domain, a model of a two-dimensional computational grid is used, which is used in the implementation of numerical calculations. Class diagrams are presented for describing the geometry of the object under study, as well as its constituent segments. In order to improve the performance of calculations, an algorithm for combining geometry segments was developed, in which the original algorithm was divided into separate fragments by introducing a number of conditional structures. As a result of experimental data processing, regression equations were obtained that describe the dependence of the algorithm execution time on the number of joins. The developed algorithm and class library make it possible to work with the description of the geometry of the object under study as a set of parameterized primitives and educe the time spent on the formation of the description of the computational domain by up to 27%.

Liquid fertilizers fed into centrifugal device are spread along angle and radius of feed under action of blades. This article describes how to calculate throwing angular characteristics using Mathcad. The package consists of four programs. Program Mf is intended for calculation of probability density of supply point coordinates under assumption of bivariant normal distribution of system r, γ, which are specified in the form of vectors. The result of the calculation is displayed as matrix Mf. The program Mα calculates the throwing angle for all combinations r, γ.. To calculate the throwing angle, the method of solving differential equations of particle movement along the blade of the device with input data was used: Radius of the disk R, angular speed ω, coefficient of friction of fertilizers on the blade f. The program Ms extracts from the matrix Mf elements Corresponding to a throw angle less than a given number A. The program F (A) sums the elements of the matrix Ms. We obtained the values of the throw angle distribution function by multiplying the resulting sum by the intervals of vectors r and γ. The calculated throwing angle distribution function is approximated by the standard normal distribution function.