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Computational Fluid Dynamics in Drying Process Modelling—a Technical Review

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Abstract

Powerful computational tools such as computational fluid dynamics (CFD) have now replaced the classic method of numerical analysis of drying processes based on experimental models. Its capabilities include the adaptability to model different flow processes such as drying, with high spatial and temporal resolution facilitates and an in-depth understanding of the heat, as well as mass and momentum transfer. CFD complements the experimental and analytical approaches by simulating a range of complex flow problems. Although CFD has immense industrial applications in fluid dynamics, its use in different drying simulations is still in early stages of development. This paper presents a thorough review of the computational power of CFD packages and their application in the drying process simulation. The review also covers different mathematical approaches used in drying models, the commonly available commercial CFD codes, and the turbulence models used in simulations of drying problems. The factors contributing to the complexity and computational load of such CFD-based models are discussed. The later sections of the paper discuss various bottlenecks in the application of CFD in drying, such as the complexity of the models for convoluted geometries, and the limited description regarding the turbulent interaction between different phases.

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Abbreviations

A :

Surface area of solid (m2)

C :

Concentration (kg m−3)

C p :

Specific heat (J kg−1 K−1)

D ∗ :

Effective moisture diffusivity (m2 s−1)

DEM:

Discrete element method

E :

The activation energy (J mol−1)

F :

Momentum exchanged between phases in motion (N m−3)

FEM:

Finite element method

FVM:

Finite volume method

\( \dot{I} \) :

Volumetric evaporation (kg m−3 s−1)

K :

Thermal conductivity (W m−1 K−1)

L :

Characteristic length (m)

Le :

Lewis number (α/D)

M :

Moisture content (kg kg−1)

MR :

Moisture ratio

P:

Pressure (Pa)

PTM:

Particle tracing method

Q :

Energy of dispersed phase (J m−3 s−1)

R:

Universal gas constant (8.316 J mol−1)

S T :

Thermal sink or source (W m−3)

T :

Temperature (K)

TBC:

Thermal boundary condition

V :

Volume fraction

W :

Molecular weight of water

a:

Normal vector at the surface

h ∗ :

Mass transfer coefficient (m s−1)

h :

Heat transfer coefficient (W m−2 K−1)

g :

Body force per unit mass (m s−2)

k :

Drying constant

k ∗ :

Evaporation constant

m :

Mass of solid (kg)

\( \dot{m} \) :

Mass flux (kg m−3 s−1)

p :

Static pressure (Pa)

q :

heat flux (J m−3 s−1)

t :

time (s)

u :

Velocity of fluid (m/s)

r :

Radius (m)

α :

Thermal diffusivity (m2 s−1)

β :

Empirical coefficient for shrinkage

λ :

Latent heat of vaporization in (J kg−1)

μ :

Dynamic viscosity of fluid (kg m−1 s−1)

ρ :

Density (kg/m3)

∇ :

Laplace gradient function \( \left(\frac{\partial }{\partial x},\frac{\partial }{\partial x},\frac{\partial }{\partial x}\right) \)

τ :

Total tensor of strains (Pa)

⊗:

Tensor product

ξ :

Surface interface to a fixed value

e :

Equilibrium

f :

Fluid/drying medium

i :

Direction in the x, y, and z axes

in :

Interface layer

n :

Coefficient of drying

o :

Initial point (t = 0 s)

s :

Solid

v :

Water vapor

d :

Dry matter

a, b :

Components of mixture

w :

Water

df :

Drying front

sat :

Saturation

wb :

Wet bulb

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Acknowledgements

The authors acknowledge the Natural Sciences and Engineering Research Council of Canada, Graduate Enhancement of Tri-Council Stipends, and University of Manitoba Graduate Fellowship for their financial support.

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  1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, Canada

    Rani Puthukulangara Ramachandran, Mohsen Akbarzadeh, Jitendra Paliwal & Stefan Cenkowski

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  1. Rani Puthukulangara Ramachandran
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Correspondence to Jitendra Paliwal.

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Ramachandran, R.P., Akbarzadeh, M., Paliwal, J. et al. Computational Fluid Dynamics in Drying Process Modelling—a Technical Review. Food Bioprocess Technol 11, 271–292 (2018). https://doi.org/10.1007/s11947-017-2040-y

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  • DOI: https://doi.org/10.1007/s11947-017-2040-y

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