| dc.description.abstract | Most of the measuring methods for fluid density, viscosity and surface tension
coefficient nowadays can only measure one single physical property at one time and have
strict restriction of fluid temperature range. In addition, the cost of measurement is difficultly
reduced resulting in the complex and sophisticated equipment. Based on Draining Vessel
Method, a stationary fluid is contained in a vessel and discharged from the orifice of the
bottom of vessel. Make the mass flow rate over time or head measured from outlet as flowing
feature. The advantage of this method is high adaptability in cold or hot flow field.
Furthermore, it allows measuring three kinds of fluid physical property at one operation of
experiment with simple equipment and low maintaining cost.
In this study, we abandon the discharging coefficient and initial guess value in previous
research of Draining Vessel Method to avoid the problem owing to lack of physical property
range covered by experiment or the decision of initial guess value. We apply the deep learning
as the algorithm of regression. With the CFD simulation software (COMSOL Multiphysics),
we establish a model of draining vessel that is close to reality and generate a large number of
flow feature data of different fluids as the basis for the regression data. Through deep
learning, various flow features are sorted out and the regression model are established. It
saves the iterative calculation process during converting the experiment measurement data to
target physical property. The mass flow rates are rapidly figured out predicted target value
through the deep learning model directly.
We verify the final prediction model with the aqueous glycerol and aqueous propylene
glycol. The mean absolute error of prediction of density obtained from experiment data is
7.21kg/m3
, and the mean relative error is 0.66%. The mean absolute error of prediction of
viscosity obtained from experiment data is 0.215cP, and the mean relative error is 6.03%. In
the prediction result of surface tension coefficient obtained from experiment data, its mean
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absolute error 24.27mN/m and its mean relative error is 42.98%, while the mean absolute
error is 1.52mN/m, and the mean relative error is 3.16% with the prediction obtained from
using the simulated mass flow rate the as input data. This method is reliable in measuring
density and viscosity, but it hasn’t been able to measure surface tension coefficient in real
experiment. If we want to improve this measurement method in the future, it is necessary to
reduce error between the simulation and experiment as much as possible so as to make this
method more stable and accurate performance and realize the practical application of the
measurement of surface tension. | en_US |