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ISSN Online: 2688-7231

ISBN Online: 978-1-56700-524-0

Proceedings of the 26thNational and 4th International ISHMT-ASTFE Heat and Mass Transfer Conference December 17-20, 2021, IIT Madras, Chennai-600036, Tamil Nadu, India
December, 17-20, 2021, IIT Madras, Chennai, India

An inverse method to estimate temperature-dependent viscosity of a liquid

Get access (open in a dialog) DOI: 10.1615/IHMTC-2021.1490
pages 987-990

要約

An incompressible fluid flowing through a pipe is used to estimate the temperature-dependent viscosity of a liquid. A liquid with initial velocity and temperature is sent through a pipe whose outer surface is subjected to a uniform heat flux. The temperature and pressure of a liquid at the pipe interface vary along the length of the pipe due to the heat flux from the boundary, and their values depend upon the thermo-physical properties of a liquid, like viscosity, specific heat, thermal conductivity, etc.

In this paper, the pressure distribution at the interface along the length of the pipe is simulated by solving the two-dimensional steady laminar flow in COMSOL. The obtained pressure distribution is used to estimate and establish a functional form of temperature-dependent viscosity by using an inverse technique. The simulated pressure distribution data in the COMSOL will act as a forward model for inverse analysis, and the simulated data is considered as surrogated data by adding noise to it. The obtained surrogated data is used as measurement data to match up with the simulated data for estimating the parameters in the assumed function form of temperature-dependent viscosity. The SNOPT (Sparse Nonlinear Optimizer) with conjugate gradient-based Optimization solver in the COMSOL is used to do the inverse analysis. Two parameters exponential function form of temperature-dependent viscosity (µ = A × ε(B/T)), is used to demonstrate the ability of this method to efficiently and accurately recover the temperature-dependent viscosity of a liquid. This method is validated by comparing the viscosity data in the literature.