Optimum design of hydrodynamic thrust bearings with rayleigh's pocket profiles

Optimum design problem for hydrodynamic self-aligning acting thrust bearings was considered. Based on results for rectangular region the problem for sector region was solved. As an objective function, the maximum of pressure integral over the lubricant layer surface was used and five geometrical parameters described Rayleigh's pocket shape were used as optimization variables during optimization procedure. The bearing pressure distribution was determined on the basis of the Navier-Stokes equations using the ANSYS / CFX software. Numerically the optimization problem was solved using three different methods: IOSO, SIMPLEX and pilOPT+AFilter SQP realized in two commercial optimization software IOSO and modeFRONTIER. The aim of this investigation was designing the technologically advanced profiles of thrust bearing sector microgeometry ensuring the maximum load capacity

Predicting Rock Bursts in Rock Mass Blocks Using Acoustic Emission

Geophysical methods for local rock burst prediction are currently being developed along two lines: improving recording equipment and improving data processing methods. Progress in developing processing methods is constrained by the lack of informative prognostic models that describe the condition of rock mass, the process of rock mass fracturing, and the phenomena that can substantiate the choice of both criteria and test parameters of the condition of rock mass and give an estimate of the time remaining until rock pressure manifestation. In particular, despite achievements in hardware design, researchers using the seismo-acoustic method to predict rock bursts measure the acoustical activity or energy capacity of elastic wave scattering after a man-made explosion and are faced with the dependence of forecast results on destabilizing factors. To solve this problem, we applied an information and kinetic approach to forecasting. In this article, we discuss the principles of selecting test parameters that are resistant to destabilizing factors. We propose a micromechanical model of fracture accumulation in a rock mass block that reflects the dependence of acoustic emission (AE) parameters on time, which makes it possible to detect the influence of various factors on forecast data and filter the signals. We also propose criteria and a methodology for rock burst risk assessment. The results were tested in analyzing the seismo-acoustic phenomena caused by man-made explosions at the Taimyrsky and Oktyabrsky mines in Norilsk. The article gives examples of using the proposed criteria. The effectiveness of their application is compared with traditional methods for assessing rock burst risks and evaluating the stress–strain parameters of rock mass in terms of their being informative, stable, and representative by means of statistical processing of experimental data

Predicting Rock Bursts in Rock Mass Blocks Using Acoustic Emission

Geophysical methods for local rock burst prediction are currently being developed along two lines: improving recording equipment and improving data processing methods. Progress in developing processing methods is constrained by the lack of informative prognostic models that describe the condition of rock mass, the process of rock mass fracturing, and the phenomena that can substantiate the choice of both criteria and test parameters of the condition of rock mass and give an estimate of the time remaining until rock pressure manifestation. In particular, despite achievements in hardware design, researchers using the seismo-acoustic method to predict rock bursts measure the acoustical activity or energy capacity of elastic wave scattering after a man-made explosion and are faced with the dependence of forecast results on destabilizing factors. To solve this problem, we applied an information and kinetic approach to forecasting. In this article, we discuss the principles of selecting test parameters that are resistant to destabilizing factors. We propose a micromechanical model of fracture accumulation in a rock mass block that reflects the dependence of acoustic emission (AE) parameters on time, which makes it possible to detect the influence of various factors on forecast data and filter the signals. We also propose criteria and a methodology for rock burst risk assessment. The results were tested in analyzing the seismo-acoustic phenomena caused by man-made explosions at the Taimyrsky and Oktyabrsky mines in Norilsk. The article gives examples of using the proposed criteria. The effectiveness of their application is compared with traditional methods for assessing rock burst risks and evaluating the stress–strain parameters of rock mass in terms of their being informative, stable, and representative by means of statistical processing of experimental data

Intelligent Data Analysis for Infection Spread Prediction

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive

Intelligent Data Analysis for Infection Spread Prediction

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive

Ways to Reduce Risks When Building the Digital Economy in Russia. Educational Aspect

А group of authors of Peter the Great St. Petersburg Polytechnic University, working within the framework of the activity of Coordination Council for the Federal Educational and Methodical Associations on education in the field of “Engineering, Technology and Technical Sciences” have undertaken a comprehensive study dedicated to the processes of the digital economy formation. More than two hundred primary sources of reference have been analyzed. Several main groups of risks associated with the transition to global digitalization have been defined and classified. Further research results described in this article allow to characterize the specific features of the occurrence of risks in the Russian Federation and to determine the ways of reducing these risks.In addition to the six groups of risks which are currently possible to arise and which are characteristic of the entire world space, the authors have revealed a number of several additional risks typical only of Russia. One of the key areas in Russia that poses a whole range of various risks is the education system. The authors have analyzed and classified the policies and moves suggested by the researchers and politicians to reduce the likelihood of these specific risks occurrence.The qualitative leap of the educational process in Russia is possible only by means of the formation of some new competence profiles of educational institutions graduates with reference to the digital economy development conditions. The authors have defined seven types of competencies that are relevant for the transition of Russia to the digital economy and developed twelve pilot educational modules necessary for their formation

Optimum design of hydrodynamic thrust bearings with rayleigh's pocket profiles

Optimum design problem for hydrodynamic self-aligning acting thrust bearings was considered. Based on results for rectangular region the problem for sector region was solved. As an objective function, the maximum of pressure integral over the lubricant layer surface was used and five geometrical parameters described Rayleigh's pocket shape were used as optimization variables during optimization procedure. The bearing pressure distribution was determined on the basis of the Navier-Stokes equations using the ANSYS / CFX software. Numerically the optimization problem was solved using three different methods: IOSO, SIMPLEX and pilOPT+AFilter SQP realized in two commercial optimization software IOSO and modeFRONTIER. The aim of this investigation was designing the technologically advanced profiles of thrust bearing sector microgeometry ensuring the maximum load capacity