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Statistical characteristics of falling-film flows: A synergistic approach at the crossroads of direct numerical simulations and experiments

Alexandros Charogiannis1, Fabian Denner2, Berend G. M. van Wachem2, Serafim Kalliadasis3, and Christos N. Markides1,*

  • 1Clean Energy Processes (CEP) Laboratory, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
  • 2Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
  • 3Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom

  • *Corresponding author: c.markides@imperial.ac.uk
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Phys. Rev. Fluids 2, 124002 – Published 8 December, 2017

DOI: https://doi.org/10.1103/PhysRevFluids.2.124002

Abstract

We scrutinize the statistical characteristics of liquid films flowing over an inclined planar surface based on film height and velocity measurements that are recovered simultaneously by application of planar laser-induced fluorescence (PLIF) and particle tracking velocimetry (PTV), respectively. Our experiments are complemented by direct numerical simulations (DNSs) of liquid films simulated for different conditions so as to expand the parameter space of our investigation. Our statistical analysis builds upon a Reynolds-like decomposition of the time-varying flow rate that was presented in our previous research effort on falling films in [Charogiannis et al., Phys. Rev. Fluids 2, 014002 (2017)], and which reveals that the dimensionless ratio of the unsteady term to the mean flow rate increases linearly with the product of the coefficients of variation of the film height and bulk velocity, as well as with the ratio of the Nusselt height to the mean film height, both at the same upstream PLIF/PTV measurement location. Based on relations that are derived to describe these results, a methodology for predicting the mass-transfer capability (through the mean and standard deviation of the bulk flow speed) of these flows is developed in terms of the mean and standard deviation of the film thickness and the mean flow rate, which are considerably easier to obtain experimentally than velocity profiles. The errors associated with these predictions are estimated at ≈1.5% and 8% respectively in the experiments and at <1% and <2% respectively in the DNSs. Beyond the generation of these relations for the prediction of important film flow characteristics based on simple flow information, the data provided can be used to design improved heat- and mass-transfer equipment reactors or other process operation units which exploit film flows, but also to develop and validate multiphase flow models in other physical and technological settings.

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Phys. Rev. Fluids 2, 124002– Published 8 December, 2017
  • Received 22 February 2017
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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