Theoretical premises of a vibro-inertial method of viscosity measurement

In order to develop a compact precise device for studying the rheological properties of Newtonian and non-Newtonian fluids in a wide range of pressures, temperatures and shear rates, in the present paper a new method and design of a vibro-inertial viscometer is presented. A simulation model was developed to calculate the flow of a viscous uncompressible fluid in a torus-shaped channel under the influence of vibration. The effect of boundary flow of low viscosity fluids is identified and the relationship between the vibrational frequency and main characteristics of the viscometer is shown

Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary

The aim of this work is to develop practical tools to recognize the average flow rate of physiological fluids in capillaries. This tool is represented by classification models in an artificial neural networks form. The flow rate data were obtained experimentally. Intralipid was used as the test liquid. Laser speckle contrast imaging was used to obtain images of liquid flow in a glass capillary. The experiment was carried out with an average flow rate of 0-2 mm/s with various concentrations of intralipid. The results of training of fully connected and convolutional neural networks for processing the received data are presented. The accuracy of determining the average flow rate of intralipid with different concentrations was comparable to the previously obtained results for a fixed concentration and amounted to approximately 97.5%

Laser speckle contrast imaging and machine learning in application to physiological fluids flow rate recognition

The laser speckle contrast imaging allows the determination of the flow motion in a sequence of images. The aim of this study is to combine the speckle contrast imaging and machine learning methods to recognition of physiological fluids flow rate. Data on the flow of intralipid with average flow rate of 0-2 mm/s in a glass capillary were obtained using a developed experimental setup. These data were used to train a feed-forward artificial neural network. The accuracy of random image recognition was quite low due to pulsations and the uneven flow set by the pump. To increase the recognition accuracy, various methods for calculating speckle contrast were used. The best result was obtained when calculating the mean spatial speckle contrast. The application of the mean spatial speckle contrast imaging together with the proposed artificial neural network allowed to increase the fluid flow rate recognition accuracy from about 65 % to 89 % and make it possible to exclude an expert from the data processing

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

Machine learning for rotating machines: simulation, diagnosis and control

The goal of this work is association of several machine learning methods in a study of rotating machines with fluid-film bearings. A fitting method is applied to fit a non-linear reaction force in a bearing and solve a rotor dynamics problem. The solution in the form of a simulation model of a rotor machine has become a part of a control system based on reinforcement learning and the policy gradient method. Experimental part of the paper deals with a pattern recognition and fault diagnosis problem. All the methods are effective and accurate enough

Modeling of Fluid Flow in the Cone Seal

AbstractThe fluid flow in cylinder-cone rotor-seal system is investigated. Mathematical model of three dimensional enforced and shear flow of viscosity incompressible fluid in the gap between cylinder rotor and cone stator is produced. The dimensional analysis of model is carried out. The velocity and pressure fields, leakage are presented as result, which are calculated by control volume approach

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