Application of Artificial Neural Networks to Calculation of Oil Film Reaction Forces and Dynamics of Rotors on Journal Bearings

Increase of energy efficiency and level of information system development of rotor machines in general requires improvement of theoretical approaches to research. In the present paper the problem of high-precision and high-performance computing programs development has been considered to simulate rotor vibrations. Based on two-layer feed-forward neural networks, numerical models have been developed to calculate oil film reaction forces to solve the rotor dynamics problems. Comparison has been done of linear and nonlinear approaches to solution of rotor dynamics problems, and a qualitative evaluation has been presented of accuracy and performance of a neural network approach compared to conventional approaches to rotor dynamics

Application of deep convolutional and long short-term memory neural networks to red blood cells motion detection and velocity approximation

The paper deals with processing data obtained using nailfold high-speed videocapillaroscopy. To detect the red blood cells velocity two approaches are used. The deterministic approach is based on pixel intensities analysis for object detection and calculation of the displacement and velocity of red blood cells in a capillary. The obtained data formulate targets for the second approach. The stochastic approach is based on a sequence of artificial neural networks. The semantic segmentation network UNet is used for capillary detection. Then, the classification network GoogLeNet or ResNet is used as a feature extractor to convert masked video frames to a sequence of feature vectors. And finally, the long short-term memory network is used to approximate the red blood cells velocity. The results demonstrated that the accuracy of the mean velocity approximation in the time range of several seconds is up to 0.96. But the accuracy at each specific time moment is less accurate. So, the proposed algorithm allows the determination of the RBCs mean velocity but it doesn't allow determination of the RBCs pulsations accurate enough

Influence of Critical Flow Rates on Characteristics of Enforced and Shear Flows in Circular Convergent-Divergent Channels

Analysis of the reasons of critical flow rate occurrence in hydraulic tracts of cryogenic machines has been carried out. Theoretical expressions have been derived to calculate critical velocities in a boiling multiphase medium. Applied to hybrid fluid-film bearings with throttles for lubricant supply, a mathematical model has been developed to calculate pressure distribution and hydrodynamic reaction forces of a lubricant considering the influence of steam content and critical flows in throttle devices. Numerical results of phase state and load capacity calculations of a hybrid fluid-film bearing under lubricant’s critical flow rates condition have been presented

A method to measure non-Newtonian fluids viscosity using inertial viscometer with a computer vision system

The theory of rheology of non-Newtonian fluids is based on the generalized Newtonian hypothesis of viscosity. The viscometers for non-Newtonian fluids should implement fluid flows with the known stress and strain state parameters distributions. Ideally, the distributions should be homogeneous in the flow domain. The idea of the proposed method is based on a combination of a capillary and a rotational viscometers implemented in the torus-shaped capillary viscometer. Analysis of the mathematical model of the inertial non-Newtonian fluid flow in the torus allowed to determine the conditions of homogeneity of the mechanical and thermal parameters in the flow domain and to develop method of viscosity measurement. The measured values are the shear rate on the inner surface of the capillary and the flow rate. The measurements are implemented with the computer vision system that processes data obtained from the high speed CMOS camera that records inertial flow in the transparent capillary illuminated with laser. The computer vision system is based on the application of deep convolutional neural network for laser speckle contrast imaging processing. During the experiments, the proposed viscometer was compared with the Brookfield rotational viscometer. The relative error of the proposed viscometer and method is less than 2. The inertial viscometer is compact, it allows to study the wide range of shear rates per one test in automatic mode, and it has low fluid capacity of approximately 1.87 ml. That makes it possible to use the viscometer as a point on care testing device in medicine to study the rheology of physiological fluids, in particular blood

Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

Maxillary sinus pathologies remain among the most common ENT diseases requiring timely diagnosis for successful treatment. Standard ENT inspection approaches indicate low sensitivity in detecting maxillary sinus pathologies. In this paper, we report on capabilities of digital diaphanoscopy combined with machine learning tools in the detection of such pathologies. We provide a comparative analysis of two machine learning approaches applied to digital diapahnoscopy data, namely, convolutional neural networks and linear discriminant analysis. The sensitivity and specificity values obtained for both employed approaches exceed the reported accuracy indicators for traditional screening diagnosis methods (such as nasal endoscopy or ultrasound), suggesting the prospects of their usage for screening maxillary sinuses alterations. The analysis of the obtained values showed that the linear discriminant analysis, being a simpler approach as compared to neural networks, allows one to detect the maxillary sinus pathologies with the sensitivity and specificity of 0.88 and 0.98, respectively