Coupling CFD and Diffusion Models for Analyzing the Convective Drying Behavior of a Single Rice Kernel

ElGamal, Ramadan; Ronsse, Frederik; Radwan, Sherif M.; Pieters, Jan

doi:10.1080/07373937.2013.829088

Cited by 46 publications

(21 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of this method for understanding the dynamics of thermal transport during rice grain drying could be very valuable for quality control of rice grains during storage. Reviews of the recent studies on the application of finite element modeling that describes heat and mass transport has been limited only to either two-dimensional (2D) or threedimensional (3D) single domain models [11,13,14,[16][17][18][19][20][21] . Many of these relevant studies attempted to simplify the complex process of the heat and mass transfer in the processing operation.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the use of computer simulation using modeling of various engineering processes has become very popular [11][12][13][14][15][16] . This approach is very powerful, cheap and fast for predicting variations in operational parameters such as temperature and moisture, air velocity and their effect on the mechanical properties of the product.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-Dimensional Numerical Modeling of Convective Heat Transfer During Shallow-Depth Forced-Air Drying of Brown Rice Grains

Pérez

Tanaka

et al. 2015

Drying Technology

View full text Add to dashboard Cite

We describe here a three dimensional (3D) numerical study of the convective thermal transfer during forced-air drying of brown rice grains with the inclusion of moisture evaporation. Three levels of temperature were tested across a range from 40 to 60 o C. The objective of the study was to determine the temperature distributions in the drying chamber and as well as the surface and intra-kernel temperature gradients during convective drying of rice grains. The numerical model was based on the Navier-Stokes equation for the fluid flow and the Fourier's equation for heat transfer. Results of the numerical solution showed that temperature distribution in the air and brown rice grains during the early minutes of drying were significantly influenced by the direction of flow of the heated air. Air gaps between grains and at the periphery of the drying chamber affected the rate and direction of airflow. The temperature history of the rice grains showed variations at different levels within the layer of grains. Moreover, the stream of heated air showed regions with airflows eight times than the inlet air velocity. Downloaded by [FU Berlin] at 04:09 15 May 2015 2 KEYWORDS: 3D finite element model, convective heat transfer, rice grain drying, evaporation, thermal diffusion, numerical simulation Notation: BDF Backward Differentiation Formula CFD Computational Fluid Dynamics Cp a specific heat capacity at constant pressure of air, J/kg-K Cp specific heat capacity at constant pressure of brown rice, J/kg-K GMRES Generalized Minimal Residual method k a thermal conductivity of air, W/m-K k o thermal conductivity of brown rice, W/m-K k 1 drying rate constant of brown rice, s -1 l 3x3 identity matrix M moisture content, % d.b. MR moisture ratio, dimensionless M o initial moisture content, % d.b. M i instantaneous moisture content, % d.b. M e equilibrium moisture content, % d.b. P pressure, Pa Q st latent heat of vaporization, J/g Q heat lost from evaporation, W/m 3 Re Reynolds number, dimensionless t time, s Downloaded by [FU Berlin] at 04:09 15 May 2015 3 T temperature, K T o initial/ambient temperature of air, K T ∞ maximum drying temperature of air, K u velocity vector, m/s V t total volume of grains, m 3 W d weight of dry solids, kg x,y,z Cartesian coordinates, mm ∞ infinity, supremum of the temperature ρ a density of air , kg/m 3 ρ density of brown rice, kg/m 3 μ a dynamic viscosity of air, Pa-s

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Numerical Modeling of Convective Heat Transfer During Shallow-Depth Forced-Air Drying of Brown Rice Grains

Pérez

Tanaka

et al. 2015

Drying Technology

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4] CFD has been widely used to improve process capabilities in many industrial applications, including industrial drying processes. [1][2][3][4][5][6][7][8][9][10] Natural rubber is a major component of vehicle tires and many other products. It is preserved in the form of ribbed smoked sheet (RSS) prior to production.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Numerical Modeling of Heat and Moisture Transfer in Natural Rubber Sheet Drying Process

Tekasakul

Tirawanichakul

2015

Drying Technology

View full text Add to dashboard Cite

The effects of heat and moisture transfer on rubber sheet drying were investigated by 3-D modeling using computational fluid dynamics (CFD). Complexities arose due to the inclusion of moisture transport. Local convective heat transfer coefficients for the sheets were predicted and then used to determine the mass transfer coefficients using the Chilton-Colburn analogy. Deviations between experimental and simulated temperatures were observed to range from 4.0 to 7.7 C. Temperature and moisture content of the rubber sheets were accurately predicted, and agreement between the experimental and simulated results was acceptable. This is useful for the design of an efficient rubber sheet drying chamber.

show abstract

“…ElGamal et. al [9] studied drying of single rice kernel, and Perez et. al [10] , studied drying of multiple layers of rice grains with actual geometry of individual rice grain.…”

Section: Introductionmentioning

confidence: 99%

Drying of Almonds II: Multiple Particles

Chilka

Ranade

2017

Indian Chemical Engineer

View full text Add to dashboard Cite

Computational modeling is an efficient and effective tool for modeling of drying process for food products. Developing validated computational models for drying processes is essential to build energy efficient drying units, producing uniform quality of dried products. This work presents drying behavior of almonds with a specific focus on understanding interaction among multiple almonds. Eight (2x2x2) particles, and twenty-seven (3x3x3) particles arranged in the shape of a cuboid were used to conduct drying experiments in a Mettler Toledeo Moisture Analyzer unit. Experiments were conducted to measure the moisture loss data with respect to drying time using Almond kernels. Experimental data was used to understanding drying kinetics as well as variation in moisture content with respect to their positions in a cuboid. Computational fluid dynamics (CFD) based simulations were carried out for the flow, heat transfer and drying of particles in the unit. Actual geometry of individual particles were considered in simulations, to predict the variation in velocity, heat and mass transfer coefficients for all the particles. Simulations predicted moisture loss data that matches well with the experimentally measured values. Average moisture for each layer was also compared for various intermediate drying times. Simulation results captured the overall drying process for multiple particles system adequately.The results are compared with the results obtained with drying of a single almond. The approach, models and presented results will be useful for designing large scale drying units for almonds.

show abstract

Coupling CFD and Diffusion Models for Analyzing the Convective Drying Behavior of a Single Rice Kernel

Cited by 46 publications

References 20 publications

Three-Dimensional Numerical Modeling of Convective Heat Transfer During Shallow-Depth Forced-Air Drying of Brown Rice Grains

Three-Dimensional Numerical Modeling of Convective Heat Transfer During Shallow-Depth Forced-Air Drying of Brown Rice Grains

Three-Dimensional Numerical Modeling of Heat and Moisture Transfer in Natural Rubber Sheet Drying Process

Drying of Almonds II: Multiple Particles

Contact Info

Product

Resources

About