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Design a cabinet dryer with two geometric configurations using CFD

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Abstract

Cabinet dryers are the most popular devices for fruit drying. One of the drawback of this dryer can be non-uniformity in the desired end product moisture content. To surmount this problem, a new version of cabinet dryer with separate entrances for the trays was designed in this research. Some involving physical properties of fruit such as air flow resistance, kernel and bulk densities and porosity were measured. Several different three dimensional geometries of cabinet dryer with three fruit holding trays were studied theoretically using Computational Fluid Dynamics (CFD) technique. The most appropriate geometrical sketch with acceptable uniform air flow and temperature distribution in the cabinet dryer was selected and fabricated. Several experiments were conducted on the new pilot size dryer Lemon fruit with initial moisture content of 84 % (wb) was selected to be dried in the new dryer. The experimental results showed that the new cabinet dryer illustrated an even distribution of air velocity and temperature throughout the dryer. Comparing the experimental and predicted (extracted for the CFD analysis) data under different operating conditions ( air velocities of 1, 2 and 3 ms−1, initial product moisture content of 84 %(wb) and drying air temperature of 50 ◦C) revealed a very good correlation coefficient of 0.99 for air velocity in the drying chamber. In the next step, the new cabinet dryer was compared with the existing design Amanlou and Zomorodian (J Food Eng 101: 8–15, 2011) with air flow distribution uniformity among the trays, rate of drying in different trays, and electrical energy consumptions, It was revealed that the new design was superior to the existing design in all of these aspects.

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Abbreviations

C, D:

prescribed matrices

C 0; C 1 :

empirical coefficients

C ij :

prescribed matrices

D ij :

mass diffusion coefficient

ρ :

density of fluid

k:

turbulent kinetic energy

ε :

rate of dissipation

μ :

dynamic viscosity

μ t :

turbulent viscosity

G k :

generation of turbulent kinetic energy due to the mean velocity gradients

G b :

generation of turbulent kinetic energy due to buoyancy

Y M :

contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate

C 1ε , C 2ε , C 3ε :

constants used in turbulent model

σ k :

turbulent Prandtl numbers for k

σ ε :

turbulent Prandtl numbers for ε

E:

total energy

υi :

velocity vector

υ mag :

velocity magnitude

\( {{\left( {{\tau_{ij }}} \right)}_{eff }} \) :

deviatoric stress tensor

P:

pressure

Pr t :

Prandtl number

T:

temperature

c p :

specific heat capacity at constant pressure

u:

velocity magnitude in x direction

t:

time

S k , S ε , S h :

user-defined source terms

S i :

source term for i th momentum equation

References

  • Amanlou Y, Zomorodian A (2010) Applying CFD for designing a new fruit cabinet dryer. J Food Eng 101:8–15

    Article  Google Scholar 

  • Adams RL, Thompson JF (1985) Improving drying uniformity in concurrent flow tunnel dehydrators. Trans ASAE 28(3):890–892

    Article  Google Scholar 

  • Anderson JD (1995) Computational fluid dynamics. International Edition. McGraw-Hill, New York

    Google Scholar 

  • Fluent 6.3 user’s Guide (2005) Fluent documentation. Fluent Inc, Lebanon

    Google Scholar 

  • Foster AM, Madge M, Evans JA (2005) The use of CFD to improve the performance of a chilled multi-deck retail display cabinet. Int J Refrig 28:698–705

    Article  Google Scholar 

  • Franco ADL, Valeva A, Pena A, Perez M (2011) Aerodynamic and lysis CFD simulation of several cellulose evaporative cooling pads used in Mediterranean greenhouses. 76: 218–230

  • Hong SI, Lee H, Hwang I, Seo J, Bitog J, Song (2011) CFD modeling of livestock odour dispersion over complex terrain. 108: 253–264

  • Incropera FP, DeWitt DP (1990) Fundamentals of heat and mass transfer. Wiley, New York

    Google Scholar 

  • Karathanos VT, Belessiotis VG (1997) Sun and artificial air drying kinetics of some agricultural products. J Food Eng 31(1):35–46

    Article  Google Scholar 

  • Margaris DP, Ghiaus AG (2006) Dried product quality improvement by air flow manipulation in tray dryers. J Food Eng 75:542–550

    Article  Google Scholar 

  • Mathioulakis E, Karathanos VT, Belessiotis VG (1998) Simulation of air movement in a dryer by computational fluid dynamics: application for the drying of fruits. J Food Eng 36:183–200

    Article  Google Scholar 

  • Mirade PS (2003) Prediction of the air velocity field in modern meat dryers using unsteady computational fluid dynamics (CFD) models. J Food Eng 60:41–48

    Article  Google Scholar 

  • Mulet A, Berna A, Borras M, Pinaga F (1987) Effect of air flow rate on carrot drying. Dry Technol 5(2):245–258

    Article  Google Scholar 

  • Norton T, Sun DW (2006) Computational fluid dynamics (CFD)—an effective and efficient design and analysis tool for the food industry: a review. Trends Food Sci Technol 17:600–620

    Article  CAS  Google Scholar 

  • Norton T, Grant J, Fallon R, Sun D (2010) Temproving the representation of thermal boundary conditions of livestock during CFD modeling of the indoor environment. Journal of Computers and Electronics in Agriculture 73:17–36

    Article  Google Scholar 

  • Scott G, Richardson P (1997) The application of computational fluid dynamics in the food industry. Trends Food Sci Technol 8:119–124

    Article  CAS  Google Scholar 

  • Verboven P, Scheerlinck N, De Baerdemaeker J, Nicolaï BM (2000a) Computational fluid dynamics modeling and validation of the temperature distribution in a forced convection oven. J Food Eng 43:61–73

    Article  Google Scholar 

  • Verboven P, Scheerlinck N, De Baerdemaeker J, Nicolaï BM (2000b) Computational fluid dynamics modeling and validation of the isothermal airflow in a forced convection oven. J Food Eng 43:41–53

    Article  Google Scholar 

  • Xia B, Sun DW (2002) Applications of computational fluid dynamics (CFD) in the food industry: a review. In: Sun DW (ed), CFD Applications in the Agri-Food Industry (special issue) Computers and Electronics in Agriculture, 34(1–3): 5–24

  • Yongson O, Badruddin IA, Zainal ZA, Narayana PAA (2007) Airflow analysis in an air conditioning room. Build Environ 42:1531–1537

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of agricultural engineering, Faculty of agriculture, Shiraz University, Shiraz, Iran

    H. Darabi

  2. Department of agricultural engineering, Faculty of agriculture, Shiraz University, Shiraz, Iran

    A. Zomorodian

  3. Department of mechanical engineering, Shiraz University, Shiraz, Iran

    M. H. Akbari

  4. Mechanical Engineering of Agricultural Machinery Department, Faculty of Agriculture, Razi University, Kermanshah, Iran

    A. N. Lorestani

Authors
  1. H. Darabi
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  2. A. Zomorodian
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  3. M. H. Akbari
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  4. A. N. Lorestani
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Corresponding author

Correspondence to H. Darabi.

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Darabi, H., Zomorodian, A., Akbari, M.H. et al. Design a cabinet dryer with two geometric configurations using CFD. J Food Sci Technol 52, 359–366 (2015). https://doi.org/10.1007/s13197-013-0983-1

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  • DOI: https://doi.org/10.1007/s13197-013-0983-1

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