The article presents the results of research on design of a high-speed turbine, calculation of flow and losses in its flow passage. In the course of the study, optimization algorithms were used to change the geometric parameters of a three-dimensional parameterized flow section in stationary and non-stationary settings in order to improve the required integral characteristics of the turbine. Numerical calculations were performed in Ansys Workbench software package on heterogeneous Polytechnic – RSK Tornado cluster of SPbPU. The developed flow passage of the Turbo drill stage exceeds the parameters of the original model. This model, compared with the original version with the same pressure drop, allowed an increase in turbine efficiency with a significant increase in torque (+47%). In order to validate the results of numerical calculations using computational fluid dynamics methods, preparations are being made to perform experimental studies of the designed models on a water experimental unit.

The weakness of the Direct Vector Control (DVC) is lack of an estimation of the flux amplitude and the rotor position with high accuracy. These quantities are sensitive to parameter variations; it is important to use a robust estimation system for estimating the rotor flux with respect to parametric uncertainties. In this paper the sliding mode speed sensorless vector control based on the Model Reference Adaptive System (MRAS) of double stator induction motor is presented. First, the models of the double stator induction motor and the DVC are proposed. Second, the MRAS technique of the DSIM is adopted. In order to ensure a robust sensorless control, the sliding mode technique for the estimation system was used. The results showed the presented estimator has a positive effect on the system behavior especially in changing the reference and/or the parameters variation.

Structural health monitoring has been widely used during the past decades to evaluate the safety of structural assets, detect damages at an early stage, and prevent unexpected and costly damages. The research works in this field are often concerned with bridges and buildings and little research has been conducted on structural health monitoring of power plant infrastructures such as machine foundations. The Turbo generator, also referred to as the heart of the power plant, is supported by massive concrete foundations in the turbine hall of thermal power plants. Most TG foundations in thermal power plants are at or close to their design age. The reports on structural damages in thermal power plants show that cracks are frequently observed on the beams and columns of frame-type TG foundations. It is probably the most appropriate time for developing vigorous methods for health monitoring of power plant structures to extend their reliability and life span. This paper uses the vibration-based approach for damage detection of TG foundations. Analytical mode decomposition (AMD) and experimental mode decomposition (EMD) methods are both used for modal parameter identification. A genetic algorithm is further used for finite element model updating and damage detection. The performance of the method is investigated using a 3-D finite element model of a frame-type TG foundation.