Computational Fluid Dynamics (CFD) Simulation of Solar Dryers

Achint Sanghi, Dr. R.P. Kingsly AmbroseDepartment of Agricultural and Biological Engineering, Purdue University

Introduction

2

Postharvest losses in developing countries

• Losses in the cereal grain postharvest chain

Quantity: 10-23% weight loss1 in sub Saharan Africa

Quality: More than 1/3rd of samples tested with aflatoxin above regulatory limit during a 3-year study in Kenya2

• Open-air sun drying could be one contributing factor

1African Postharvest Losses Information System (2012)2Daniel et al (2007) Comprehensive assessment of maize aflatoxin levels in Eastern Kenya 2005-2007

Traditional drying in Ghana (Picture courtesy Dr. Klein Ileleji)

Introduction

3

Postharvest losses in developing countries

• Mechanical dryers still not feasible

23% of the population in developing countries lack access to electricity1

Gas and biofuel-fired dryers operating costs are around 500 to 1000x than sun drying2

1World Energy Outlook, International Energy Agency (2012)2Boroze et al (2014) Inventory and comparative characteristics of dryers used in the sub-Saharan zone: Criteria influencing dryer choice

Introduction

4

1Kumar et al (2016) Progress in solar dryers for drying various commodities2Boroze et al (2014) Inventory and comparative characteristics of dryers used in the sub-

Saharan zone: Criteria influencing dryer choiceEkechukwu and Norton (1997) Review of solar-energy drying systems II

• Solar dryers- variety of designs developed in last 40 years1

• Limited adoption when compared to traditional sun drying2

• Most dryers developed for particular crop/location/throughput

5

Objectives

• Develop and validate a mathematical grain drying model for a low-capacity solar dryer.

• Develop and validate mathematical grain drying models for high-capacity natural and forced convection solar dryers.

Materials and Methods

6

Computational Fluid Dynamics

• Allows for 3D simulation of fluid flow problems

• Solution of equations of mass, momentum, and energy:

) 0 (Mass Transfer)

+ ∙ =- p g (Momentum Transfer)

∙ ∙ T - density of air– air velocity

p – pressureSm- momentum source terms– energy source terms (radiation

and drying energy)

E – internal energyh – enthalpyk – thermal conductivityJj – moles of species j

7

Model Key Features

• Thin layer dying equation (constants are crop-dependent)

• Moisture migration

• ‘Greenhouse effect’

• Direct and diffuse radiation (cloud cover)

Materials and Methods

8

Solar bubble dryer

Cabinet dryer Greenhouse dryer

Solar bubble dryer

Natural Convection

Naturalconvection

Forced Convection

45 kg 4000kg 1000 kg

Mixed-mode Direct Direct

Small farm Cooperative Small farm

Greenhouse dryerCabinet dryer

Materials and Methods

9

Experimental validation

Solar cabinet dryerResults

10

Tray 4Tray 3Tray 2Tray 1

Cabinet Inlet

Outlet

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

9:20 10:00 10:40 11:20 12:00 12:40 13:20 14:00 14:40 15:20 16:00

Relativ

e hu

midity

 (%)

Time

Ambient

Tray 4

Tray 3

Tray 2

Tray 1

Relative humidity

Results

11

Tray 4Tray 3Tray 2Tray 1

Cabinet Inlet

Outlet

0

200

400

600

800

1000

1200

1400

1600

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

9:20 10:00 10:40 11:20 12:00 12:40 13:20 14:00 14:40 15:20 16:00

Solar radiation (W/m

2)

Tempe

rature (°C)

Time 

Ambient

Tray 4

Tray 3

Tray 2

Tray 1

Solar radiation

Temperature

Solar cabinet dryer

Solar cabinet dryerResults

12

Relative humidity at 9:20 AM Temperature at 9:20 AM

Average deviation 8.4% from experiment Average deviation of 6.9% from experiment

Tray 4Tray 3Tray 2Tray 1

Cabinet Inlet

Outlet

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Tray 1 Tray 2 Tray 3 Tray 4 CabinetInlet

Outlet

RH(decim

al)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

Tray 1 Tray 2 Tray 3 Tray 4 CabinetInlet

Outlet

Tempe

rature (°C)

Solar cabinet dryerResults

13

Relative humidity at 9:20 AM Temperature at 9:20 AM

Solar cabinet dryerResults

14

Velocity profile in cabinet at 9:20 AM

Greenhouse dryerResults

15

20.0

25.0

30.0

35.0

40.0

45.0

50.0

55.0

60.0

9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00

Tempe

rature (°C)

TimeLayer 4 Layer 3 Layer 2 Layer 1 Ambient

Temperature profile at 9:00 AM

Comparison of simulation and experiment

Temperature profile throughout the day

Conclusions• The cabinet dryer model can predict the temperature and

relative humidity to within 7% and 8.5% respectively• The models are able to account for the effect of heating and

moisture loss due to solar radiation, as well as the natural convection heat transfer

• The models can be used to understand the performance of the dryers with different loading and weather conditions

Ongoing work• Simulation of the solar bubble dryer• Full-day-length simulations

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Acknowledgements

• USAID Borlaug Fellows program • Dr. Sam McNeill, University of Kentucky• Dr. Paul Armstrong, USDA-ARS-CGAHR• Isaac Addo, KNUST, Kumasi, Ghana

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Thank You!