DOI: 10.25136/2644-5522.2020.2.34102

Abstract: The simulation model of the column string-emulsion reactor previously suggested that the temperature does not change on the height of reactor and over time is consistent. The assessment of temperature changes in the reactor requires the knowledge on the amount of heat necessary to heat up the particles, absorbed or emitted in the course of chemical reactions, as well as the speed of heat transmission in space. The possibility of calculating these parameters for each floating particle in online regime is limited by the operating speed of the computer system. For accelerating the calculations, the author creates the database of these parameters for all substances involved in the reactions. In these circumstances, enthalpies and entropies were expressed in through the specific thermal capacity calculated based on the fifth degree polynomial. The coefficient values of the polynomial and phase transitions were taken from the reference books. The article provides an algorithm in form of the logic diagram for calculating the specific enthalpy of the particle. Based on the developed algorithm, the author creates the software that allows calculating thermodynamic functions. The interaction between the classes are demonstrated in the UML class diagram. The research presents the calculations of specific enthalpy and entropy for substances in the interval of temperatures of 298-1850 K. Variations of the values of enthalpy and entropy at the temperature of 1700 K compared to the reference values do not exceed 1.2 %.

DOI: 10.25136/2644-5522.2018.5.20345

Abstract: The article describes the NVIDIA CUDA parallel programming technology used  in the task of melting a spherical particle. The tendency of modern computers to increase power by increasing the number of cores, and not by increasing the frequency of the processor (which leads to significant energy consumption and heat generation). The Amdal law is presented, allowing to estimate the acceleration of the program time when parallelized on N processors. The conditions for increasing the performance of the algorithm in parallelizing tasks are listed. The task of melting iron ore particles is presented. The features of the parallel programming language CUDA C are considered and the algorithms for the selected task are presented. A comparative analysis of the task execution time on the CPU (C #) and GPU (CUDA C) has been made. The technology of parallel programming CUDA allows you to increase the performance of parallelized algorithms of complexity N up to 60 times. This requires the presence of a graphics processor supporting this technology, the development environment and the CUDA compiler, knowledge of the CUDA C language, as well as a good knowledge of the task and the possibility of its parallelization.