The results of application possibility research of the optimized body form with the minimal force of aerodynamic resistance as a heat sink in a convective gas flow are presented in the paper. The computational experiment was carried out in the Ansys Fluent software system. The conditions of comparison of heat-conducting bodies in the computational experiment are the preservation of constants: the volume and shape of the working area; distances from sources, drains and centers of bodies; gas flow rates; body mass; thermal power source and other secondary characteristics in addition to just the very shape of the surface. The main advantage of the resulting optimized body shape is that it coincides with the streamlines, thereby not separating flow from the surface around it. Thus, the entire surface area will be the effective surface area of the heat sink, unlike other compared forms of bodies, due to which the temperature of the heat-loaded element placed in the center of the heat sink will decrease.
The damage caused by thunderstorms increases year by year - this is due to an increase in thunderstorm activity, as well as urbanization, the development of new territories and the expansion of the use of microelectronics and microprocessor technology. The increase in thunderstorm activity is also associated with climate change and solar activity growth, and predicts further growth. This means that comprehensive research and long-term monitoring of thunderstorms are needed, and the importance of systematic analysis of lightning activity data, search for protection measures and warnings about the development of hazardous thunderstorms is increasing. In this article the authors presents the report about on the analysis of the data of remote observations of the network of sensors of auto - radar-rangefinder LS 8000 on the territory of the North Caucasus for the period from 2009 to 2016.
The aim of the work is to develop a model of adaptive system of neuro-fuzzy inference based on PI- and PI-FUZZYcontrollers, allowing to simplify, automate and unify the design process of modern automated control systems. To achieve a specific goal, a method for managing a technical object has been developed based on the construction of an adaptive system of neuro-fuzzy inference. As controllers in the system of neuro-fuzzy inference, the classical PI-controller and fuzzy PI-FUZZY-controller were chosen. Interaction between controllers is provided with the help of the hybrid control system developed. The result of interaction of the two models is automatic formation of the basis of fuzzy controller rules based on knowledge of the control object obtained with its control using the classical controller. In the developed adaptive system of neuro-fuzzy inference, error and control signals in the classical model are used as data for building a hybrid network. Error and control signals in the fuzzy model with automatically generated fuzzy inference rules are used as data to verify the hybrid network built in order to detect a fact of its retraining. Thus, during the control of a technical object by means of a hybrid system, the knowledge of an expert in subject domain for adjusting the parameters of the fuzzy controller is completely eliminated, which makes it possible to control difficultly formalizable objects in conditions of uncertainty. To obtain reliable research results, a hybrid control system was developed, consisting of classical and fuzzy models. Numeric values of the error and control signals are obtained at discrete instants of time as a result of interaction of the two models. Special files have been created to build and test a hybrid network in the form of numerical matrices.