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i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8

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Portable forced-air tunnel evaluation for cooling productsinside cold storage rooms

D.F. Barbin*, L.C. Neves Filho, V. Silveira Junior

Department of Food Engineering, University of Campinas, Rua Monteiro Lobato 80, CEP: 13083-862 Campinas, SP, Brazil

a r t i c l e i n f o

Article history:

Received 28 July 2009

Received in revised form

19 August 2011

Accepted 22 August 2011

Available online 27 August 2011

Keywords:

Frozen food

Air distribution

Refrigeration

Freezing

Heat transfer

* Corresponding author. Tel.: þ55 19 3521 40E-mail address: dfbarbin@yahoo.com.br (

0140-7007/$ e see front matter ª 2011 Elsevdoi:10.1016/j.ijrefrig.2011.08.008

a b s t r a c t

Freezing process efficiency is affected by the required conditions to keep the air flow and

temperature at the product surface. The objective of this work was to obtain results on

comparative studies with air exhaustion and blowing using an experimental portable

forced-air freezing tunnel. The device was designed to improve cooling rates inside storage

room without the need for a cooling/freezing tunnel. A heterogeneity factor was proposed

for air circulation evaluation and compared with convective heat transfer coefficient (hef)

values. Lower modules of heterogeneity factor values represent smaller temperature

differences among samples. Comparing two different air flow processes, heterogeneity

factor values were similar for regions where the cooling air could flow without obstruc-

tions. However, larger differences were observed for regions with hampered air circulation.

Results indicated that the air distribution, as well as the heat transfer, occurs more

uniformly around the products in the exhausting process than in the blowing system.

ª 2011 Elsevier Ltd and IIR. All rights reserved.

Evaluation d’un tunnel de refroidissement et de congelationpour le refroidissement de produits a l’interieur de chambresfroides utilisees pour l’entreposage

Mots cles : Produit alimentaire congele ; Distribution de l’air ; Refrigeration ; Congelation ; Transfert de chaleur

1. Introduction

Forced-air systems use cooling air to reduce products

temperature based on the convection principle. Cooling air is

circulated through the product, packed in boxes, in order to

decrease freezing time (Brosnan and Sun, 2001; Thompson,

2004). This process may be used in batch or continuous

processes. Fruit cooling and freezing and fruit pulp freezing

95; fax: þ55 19 3289 1513.D.F. Barbin).ier Ltd and IIR. All rights

packed in polyethylene packages are among the main batch

freezing process (Resende et al., 2002; Castro et al., 2003;

Dussan Sarria et al., 2006). Talbot and Fletcher (1996) showed

the efficiency of a forced-air cooling system compared to

a cooling room for grapefruits. The results showed a reduction

of 6.7 �C in one hour and 14.6 �C after 2.5 h, compared to 2 �Cand 3.5 �C for one hour and 2.5 h, respectively, for the cooling

room.

reserved.

Nomenclature

A heat transfer surface area (m2)

cp specific heat (J kg�1 K�1)

cpm metal body specific heat (J kg�1 K�1)

cpAl aluminum specific heat (J kg�1 K�1)

hef effective convective heat transfer coefficient

(Wm�2 �C�1)

kAl aluminum thermal conductivity (Wm�1 �C�1)

m mass (kg)

mi product infinitesimal mass (kg)

mt sample total mass (kg)

n number of results obtained (dimensionless)

Q total heat (J)

S2 linear regression line inclination (�C s�1)

t time (h)

Tb product temperature (�C)Ti initial temperature (�C)TN chilling medium temperature (�C)Tc product average representative temperature in

a layer (�C)Tmax maximum temperature (�C)Tmin minimum temperature (�C)Tref reference temperature (�C)V volume (m3)

Vi ‘Vi’ number (dimensionless)

Greek letters

rm metal density (kgm�3)

rAl aluminum density (kgm�3)

4 heterogeneity factor (dimensionless)

i n t e rn a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8 203

Model systems are commonly used to simulate food stuff

in processing techniques because of its homogeneity and

ease of batch reproducibility. Such characteristics could not

be ensured with real foodstuffs, which have a great vari-

ability in structure, texture and composition (Woinet et al.,

1998; Chevalier et al., 2000). Resende et al. (2002) and Berto

et al. (2003) used a sucrose solution system to observe

freezing process in forced-air room and thermal processing,

respectively. According to the authors, the solution is prof-

itable for pulp simulation with low changes in repeated

processes.

Convection heat transfer is related to the amount of energy

transferred from the product surface when it is in contact with

the refrigerating fluid (Welty et al., 2000). Dussan Sarria et al.

(2006) studied the influence of the air velocity in a cooling

tunnel. According to the authors, air velocities greater than

2.0 m s�1 did not affect the convective coefficients (hef), as

results obtained were not greater than 23.8 Wm�2 �C�1. Dincer

(1995) determined the experimental heat transfer coefficient

with data obtained during forced-air cooling, with results

varying from 21.1 to 32.1 Wm�2 �C�1 for air velocities of

1.1e2.5 m s�1. Mohsenin (1980) obtained hef values in the range

of 20e35Wm�2 �C�1 for air forced systems with air velocity

from 1.5 to 5.0 m s�1. Experiments carried out in a forced-air

roomwith air velocities in the range of 1e2 m s�1 resulted in hefvalues varying from 28Wm�2 �C�1 up to 52Wm�2 �C�1 for

cylindrical products (cucumber) during cooling (Dincer and

Genceli, 1994).

Le Blanc et al. (1990a,b), Resende et al. (2002) and Barbin

et al. (2010) reported experiments for determination of hefusing the product cooling temperature curves approach. In

this method, a metallic aluminum body with high thermal

conductivity is used to minimize the temperature gradient

formed during the heat transfer process between the product

and the cooling medium. The heat transfer rate in a deter-

mined control volume is given by equation (1):

dQdt

¼ hefAðTb � TNÞ (1)

where Q is the energy amount (J) drawn back per time t (s); hefis the effective heat transfer coefficient (Wm�2 �C�1); A is the

heat transfer area (m2); Tb is the product temperature (�C) and

TN is the air temperature (�C). Energy variation in a metallic

body with constant properties is given by equation (2):

dQdt

¼ rmVcpmdTdt

(2)

Combining equations (1) and (2), then integrating and

adopting the initial contour condition T(t¼0)¼ Ti, leads to the

equation for time dependent temperature variation:

Tb � TN

Ti � TN

¼ e�hefAtrmcpmV (3)

Equation (4) can describe the fast cooling process, which is

a simplification of Equation (3):

ðTb � TNÞðTi � TNÞ ¼ eS2$t (4)

where Ti is the initial temperature of the metallic body and TN

is the cooling air average temperature, measured by the ther-

mocouples inside the cooling room. Parameter S2 represents

the cooling coefficient, a simplification from equation (3).

Vigneault et al. (2004) proposed a new calculationmethod

for air distribution in recipients during forced-air cooling

process. Moreover, the authors developed a dimensionless

number to compare air velocity distribution heterogeneity

flowing through a porous medium, called the “Vi” number.

This was defined as the rate of the standard deviation and

the average of the air velocity flowing through a mass of

product inside a recipient. Experiments were carried out

with spherical samples inside a forced-air circulation

tunnel.

Some authors have studied the air temperature conditions

inside forced-air tunnels (Thompson, 2004; Dussan Sarria

et al., 2006). The portability of the device was not reported

before.

The objective of this work was to present a new mathe-

matical approach for the evaluation of a portable forced-air

tunnel built to enhance the freezing process of packed prod-

ucts stored in commercial boxes inside a storage room. The

described device could be adopted to avoid extra expenses

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8204

with new equipment such as freezing tunnels. Heat transfer

coefficients were used for an indirect analysis of the air

distribution inside the equipment and comparison of

temperature variation among samples considering the loca-

tion between layers of boxes. A new method for the quanti-

tative evaluation of the temperature variation for different

positions in the system was proposed.

2. Material and methods

2.1. System experimental design

Model systemwith 15% (weight/weight of solution) of sucrose

and 0.5% (weight/weight of solution) of carboxy methyl

cellulose (Carbocel AM, Arinos, SP, Brazil) was packed in

polyethylene bags (0.1 kg) with similar dimensions (0.095 m�0.07 m� 0.015 m) to pulp fruit products in the market. The

bags were stored in 35 plastic boxes (Fig. 1), with external

dimensions of 0.6 m� 0.4 m� 0.12 m and kept inside the

freezing room. The boxes had an opening area of 21% of the

total area, more than the minimum values recommended for

a good air flow (Castro et al., 2003), and were stacked over

a 1.00� 1.20 m commercial pallet in seven layers, with five

boxes each. Each box contained 8.6 kg of product in eighty-

four plastic bags.

The projected freezing system was built as described in

Barbin et al. (2009), with a plastic cover connected to an

aluminum flexible duct and a fan that blows or exhausts the

air. The plastic covers the boxes that contain the product,

stacked on a commercial pallet. The portable tunnel fan used

has axial airscrews with a tri-phase induction engine (Weg,

Brazil, model 71586 and 0.5 hp). The whole device was placed

inside a freezing storage room (Recrusul, Brazil), with internal

dimensions of 3 m� 3 m� 2.3 m (20.7 m3) and walls made of

0.01 m aluminum panels filled with expanded polyurethane

as insulation.

The cooling process consists in circulating the air from

insideof the storage roomthrough theopen spaces in theboxes

and around the product. In an exhaustion process, the air flows

from the lower part of the system to inside the boxes and

through the fanback to theroom. In theblowingprocess, theair

flow is changed, blowing the cooling air from the room directly

to the product. The forced-air circulation is vertically oriented

Fig. 1 e Plastic box for freezing products.

in both the exhaustion and the blowing process. During

exhaustion, it goes from the bottom to the top of the pallet;

while in the blowing process, it goes from top to bottom (Fig. 2).

A freezing process without the portable tunnel was carried

out as a reference test to be compared to the experiments

using the tunnel device. This reference freezing process con-

sisted in leaving the boxes inside the cold room until all of the

samples reached the final freezing temperature.

Blowing (B) and exhausting (E) air tests were run in tripli-

cate. Mixed experiments with both air circulation directions

were tested with each orientation during half of the process

length. Two types of these experiments were made, one star-

ted with the blowing process, and then changed to exhaustion

after approximately 24 h (BeE), and another started with

exhaustion and changed to blowing (EeB). The velocity of the

cooling air was measured for comparison with the convective

coefficients andheterogeneity factors obtained. Results for the

air velocity are presented in Barbin et al. (2009).

Sample temperatures were monitored using type T ther-

mocouples (coppereconstantan), acquired by a monitoring

system composed of an automatic channel selector system

model Scanner 706 (Keithley Instruments Inc., OH, USA).

2.2. Convective heat transfer calculation

Convective heat transfer was obtained using temperature

measurements of 5 identified (T1 to T5) aluminum test bodies

(with dimensions of 0.1 m� 0.07 m� 0.025 m), distributed in

layers 1, 3, 4, 5 and 7, respectively, including both the extreme

layers (1 and 7) and the central layers (3, 4 and 5) (Barbin et al.,

2010). All the aluminum test bodies were positioned over the

samples in the center of the boxes along with the thermo-

couples identifiedwith number 5 as last algorism (15, 35, 45, 55

and 75, Fig. 3) and in contact with the cooling air (Fig. 4b). The

second layer did not have a test body, but it had a temperature

measurement (thermocouple 25). The sixth layer was not

monitored with a test body neither thermocouples.

Two other thermocouples were distributed inside the room

for air circulation temperature monitoring: one in the evapo-

rator blowing air and one to evaluate the evaporator returning

air, to measure the temperature variation in these points

during the process.

The test body is an aluminum plate with similar size to the

samples. Thermocouples were inserted inside holes in the

body tests that were filled with thermal paste to avoid bubbles

which could interfere in the temperature measurement.

Polystyrene was used as insulation around the test body to

keep only one surface exposed in contact with the cooling air

(Fig. 4a). This procedure was adopted to analyze one-

dimension heat flow and avoid edge effects. Aluminum

thermo physical properties (as a metallic test body) used for

the determination of the convective heat transfer coefficients

are shown in Table 1.

After obtaining S2 values (according to equation (4)), the

effective heat transfer coefficients were calculated for

the samples in the sensible heat loss phase, as shown in

Equation (5):

hef ¼ �rAlVCpAl

AS2 (5)

Fig. 3 e Thermocouples and test body positioning and identification used in the system with seven layers.

Fig. 2 e Plastic boxes stacked on a commercial transport pallet covered with plastic, and fan orientation during the

exhaustion and blowing processes.

i n t e rn a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8 205

Fig. 4 e Aluminum test body insulation (a) and positioning inside the box (b).

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8206

2.3. Heterogeneity factor

The freezing process was monitored until the center of the

samples reached �18 �C. A method was developed for deter-

mination of the temperature distribution inside the pallet. The

objective was to evaluate temperature heterogeneity in

different pallet positions during the freezing process. This

parameter was obtained from the heat transfer between the

product (model system) and the cooling air (Equation (6)):

Q ¼ mcpDT (6)

where Q is product energy (J), m is product mass (kg), cp is

product specific heat (J kg�1 �C�1), and DT is the temperature

difference between the samples (model system) and

a temperature reference (Tref, �C).

If the whole mass of product in the pallet is reduced as

much as possible (to infinite small parts), the total energy of

the pallet is equivalent to the sum of all parts of the energy of

the products. Equation (7) represents this calculation:

mtcpTc ¼

ZmicpTðx;y;zÞ dVZ

dV(7)

where mt is the total mass of the product on the pallet or in

a layer, Tc is the product average temperature for the respec-

tive amount of product (layer or whole pallet), mi is the local

mass of an infinitesimal part of the product, T(x,y,z)¼ Ti� Tref is

the temperature of this mass and Tref is equal to 0 �C. The

product average representative temperature in a layer at

a certain moment is shown in Equation (8):

Table 1 e Aluminum thermo physical properties at 20 �C(Welty et al., 2000).

Density, rAl(kgm�3)

Specific heat, CpAl

(J kg�1 �C�1)Thermal conductivity,

kAl (Wm�1 �C�1)

2701.1 938.3 229

Tc ¼PðmiTiÞ

mt(8)

The temperature difference between the representative

average temperature and theminimumandmaximumproduct

temperatures during the freezing process was calculated using

Tc values obtained. The temperature variation in each moni-

tored point in the layer is obtained using Equation (9):

DTc ¼ Tc � Tmin or DTc ¼ Tc � Tmax (9)

A dimensionless factor was suggested to evaluate the

temperature variation compared to the average temperature,

aiming to quantify the cooling performance obtained by

different air circulation processes through the product using

Tc and DTc values calculated. This factor was an extension of

the ‘Vi’ number proposed by Vigneault et al. (2004), who

worked with the air flow around samples. In this work the

heterogeneity factor was defined as the rate between the

square root of the second potency sum of the DTc values and

the number of monitored samples (n), divided by the Tc values

obtained during the freezing process (Equation (10)):

4 ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiP ðDTcÞ2n

s

Tc

(10)

Minimum heterogeneity factor value is 0 (zero), which

represents a perfect temperature distribution inside the

system, with no temperature difference between samples.

Greater module of 4 values means bigger differences between

the considered monitored samples.

The heterogeneity factor was obtained for three layers

(upper, central and lower) in the system, for each conforma-

tion of air direction. In each of these calculations, n is equal to

5 monitored points in each layer, representing the heteroge-

neity of temperature distribution in different positions of the

respective layer. Later, it was obtained for the whole pallet,

where n is equal to 15 monitored samples. In this case, it

represented the heterogeneity of temperature distribution

between layers in the pallet.

i n t e rn a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8 207

3. Results

3.1. Convective heat transfer

The average dimensionless temperature [(T� TN).(Ti� TN)]

neperian logarithm versus cooling time graphs for every test

body monitored were plotted. Based on these graphs, the

angular coefficient (S2) and linear coefficient (A) for the

y ¼ A eS2x equation were obtained. Angular coefficient values

were used to obtain the hef values previously reported (Barbin

et al., 2010). The authors have shown that every hef value was

larger for the process with the tunnel for both the exhaustion

and blowing processes, compared to the reference. In addi-

tion, the exhaustion process resulted in greater values for the

local effective heat transfer coefficients at the product surface

and also a more homogeneous distribution at the lower layer

of boxes inside the pallet. The large value obtained for the

upper layer could indicate that the air flow causes a rapid

temperature reduction of the samples located at that position,

which could be related to the fact that cooling air had a great

velocity at that point for the blowing process (Barbin et al.,

2009). It was reported air velocities over 15 m s�1 in certain

areas for the insufflation process, while the exhaustion

process had air velocity values around 3 m s�1 (Barbin et al.,

2009; Barbin and Silveira Jr., 2011).

The same trend was observed by Resende et al. (2002), with

greater convection transfer coefficient for samples in direct

contactwith the circulating forcedair.According to theauthors,

the amount of product to be cooled is an obstacle for the cooling

air flow when it is driven directly toward the product. In the

blowing process, the air flows directly into the device and in

contact with the upper layer. This first layer of samples may

affect the air flow, leading to the high convective coefficient

obtained for the region that is in direct contact with the

incomingcoolingair, andthesmallervalues for the lower layers.

3.2. Heterogeneity factor

The heterogeneity factor 4 was calculated for each test and is

shown in Table 2.

Table 2 e Heterogeneity coefficient values (4) fortemperature distribution characterization for samples inlayers and layers in pallet.

Tests Heterogeneity factor (4)*

Lowerlayer

Centrallayer

Upperlayer

Pallet

Reference �0.12BC 0.21D �0.10B 0.18D

Exhaustion Average �0.06A 0.05A �0.05A 0.02A

AD 0.01 0.01 0.01 0.01

Blowing Average �0.14C 0.12BC �0.05A 0.09B

AD 0.01 0.02 0.01 0.01

Mixed (EeB) �0.09B 0.09B �0.02A 0.04A

Mixed (BeE) �0.08AB 0.09B �0.05A 0.10B

AD e average deviation. *Different letters means statistically

difference ( p< 0.05).

The results for the blowing and exhausting tests made in

triplicate were evaluated with one-way analysis of variance

(ANOVA) tests. Different letters mean significantly different

values ( p< 0.05) for rows or columns. Values did not differ for

the upper level, where the circulation of air had fewer obsta-

cles. However, the differences were greater for the central and

lower layers. Module values obtained for 4 topped 0.12 in the

central layer, and 0.14 in the inferior layer for the blowing

processes, while for the exhaustion processes these values

were not greater than 0.06. The reference test without the

forced-air equipment had 4 module values up to 0.21 for the

central layer, where the air flow is reduced.

This fact also occurredwhen analyzing the layers compared

to the whole pallet. The 4 value was greater for the blowing

process (0.09) than for exhausting process, which was 0.02.

This shows that the temperature distribution between layers

wasmore uniform for the exhausting process, compared to the

blowing process.

Reference tests showed heterogeneity not statistically

different ( p< 0.05) compared to the blowing process for the

lower layer, therefore central and upper layers showed values

of 0.21 and 0.10, respectively, which were greater compared to

both forced-air process (0.05). Analyzing the whole pallet, the

heterogeneity value for the reference test was 0.18, which is

greater than the results obtainedwith the tunnel. The value of

0.02 obtained for the exhaustion process is statistically

different than the result obtained to the blowing process (0.09),

and very close to 0 (zero), showing a homogeneous tempera-

ture distribution inside the device and around the samples.

The mixed tests showed similar results among them, and

intermediary between the exhausting and blowing processes.

However, the mixed test which started with the blowing

process showed a greater 4 value (0.10) than the mix test

started by the exhausting air, for the whole pallet (0.04).

4. Conclusion

The main difference between air flow processes is the

temperature variation at different positions throughout the

system, as the heterogeneity coefficient proposed showed

significant differences ( p< 0.05) for central and lower layers

of the system. These differences are greater for the blowing

process, what may lead to technological problems such as the

freezing time overestimation, causing unnecessary costs, or

underestimation, when the process could be considered

finished with samples above freezing temperature affecting

the final quality of the product.

Using the tunnel was a viable optionwhen compared to the

freezing process in a room without the portable tunnel forced

air as it reduces sample cooling and freezing time in the

storage room. Exhausting the air has a better performance

considering the cold room capacity as it drives the cooling air

through every sample in every layer equally, reducing pro-

cessing time and easing systemmonitoring, as it is allowed to

assume that all samples are at the same temperature in

unsteady state.

The use of the portable tunnel was useful to reduce

freezing time, as it allows amore homogeneous air circulation

surrounding the samples in the exhausting process resulting

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 5 ( 2 0 1 2 ) 2 0 2e2 0 8208

in more homogeneous temperature distribution in the

system. The currently proposed heterogeneity factor could

help in clarifying the cooling process during forced-air heat

transfer, once it is related to the temperature variation within

the system. More studies could be made regarding the system

energy consumption and comparing the use of the forced-air

tunnel to analyze the time reduction and the energy demand

during its operation.

Acknowledgments

The authors wish to acknowledge Coordenacao de Aperfei-

coamento de Pessoal de Nıvel Superior (CAPES) for the finan-

cial support.

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