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

Elena P. Kornaeva, Ivan N. Stebakov, Alexey V. Kornaev, Viktor V. Dremin, Sergey G. Popov, Andrey Yu. Vinokurov

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number107967
Number of pages17
JournalInternational Journal of Mechanical Sciences
Early online date30 Nov 2022
Publication statusPublished - 15 Mar 2023

Bibliographical note

Copyright © 2022, Elsevier Ltd. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License [https://creativecommons.org/licenses/by-nc-nd/4.0/].


  • Non-Newtonian fluid
  • Viscosity
  • Inertial viscometer
  • Laser speckle-contrast imaging
  • Navier-Stokes equations
  • Deep learning


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