Computational fluid dynamics (CFD) modelling of

Journal of Food Engineering 90 (2009) 90–103

Contents lists available at ScienceDirect

Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng

Computational fluid dynamics (CFD) modelling of local mean age of air distribution in forced-ventilation food plants Vivian Chanteloup, Pierre-Sylvain Mirade * UR370 Qualité des Produits Animaux, INRA, F-63122 Saint-Genes-Champanelle, France

a r t i c l e

i n f o

Article history: Received 15 February 2008 Received in revised form 4 June 2008 Accepted 7 June 2008 Available online 18 June 2008 Keywords: Age of air CFD code Fluent Air velocity Ventilation efficiency Ventilated food plants

a b s t r a c t This paper reports the implementation of the ‘‘mean age of air” (MAA) concept into the computational fluid dynamics (CFD) code Fluent through user-defined functions to assess ventilation efficiency inside forced-ventilation food plants. Two transient methods and a steady-state method based on the resolution of an additional scalar transport equation were implemented and compared with experimental and numerical data obtained by Bartak et al. [Bartak, M., Cermak, M., Clarke, J.A., Denev, J., Drkal, F., Lain, M., Macdonald, I.A., Majer, M., Stankov, P., 2001. Experimental and numerical study of local mean age of air. In: Proceedings of the 7th International Building Performance Simulation Association (IBPSA) Conference, Rio de Janeiro, Brazil; Bartak, M., Beausoleil-Morrison, I., Clarke, J.A., Denev, J., Drkal, F., Lain, M., Macdonald, I.A., Melikov, A., Popiolek, Z., Stankov, P., 2002. Integrating CFD and building simulation. Building and Environment, 37, 865–871] for a 45 m3 test room. As the steady-state method led to the best compromise between accuracy of results and computation time, this method was then used to characterize ventilation efficiency inside a pilot cheese ripening room and two modern sausage dryers of large height. In light of the results presented, local MAA is a better and a more sensitive parameter than mean air velocity for highlighting areas with inadequate ventilation, and thus for assessing ventilation efficiency in industrial food plants. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Inside forced-ventilation food plants such as modern meat dryers and cheese ripening rooms, it is of paramount importance to control both airflow (air velocity, air change rate and renewal in new air) and indoor climatic conditions (air temperature and relative humidity, gas concentrations), since this will determine both the efficiency and the homogeneity of meat drying or cheese ripening processes and the water losses of the food products. However, many authors have reported that the main reason for low quality is poor control of airflow patterns (Falconer, 1993; Dabin and Jussiaux, 1994; Pajonk, 2001). In fact, the role played by ventilation at whole-plant level is complex and remains poorly quantified. Air circulation on the one hand allows the evacuation of the heat and moisture generated by the food products inside the stacks and, on the other hand, determines both water losses and gas concentrations in the immediate atmosphere of the products, which itself influences the meat drying or cheese ripening processes. Accordingly, it would be very useful to have an alternative criterion to the commonplace mean air velocity for characterizing ventilation effectiveness within forced-ventilation food plants.

* Corresponding author. Fax: +33 473624089. E-mail address: [email protected] (P.-S. Mirade). 0260-8774/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2008.06.014

Researchers in building and room sciences characterizing indoor airflow patterns use for a long time a variety of ‘‘ventilation efficiency” parameters, such as macro air exchange rate, air change efficiency, local ventilation index, and purging airflow rate among others. Many of these parameters are associated with local mean age of air (MAA), which is generally defined as the average time for air to travel from a supply inlet area to any location in a ventilated room (Sandberg and Sjöberg, 1983; Federspiel, 1999). The age of air concept, which assumes that the age of air at the inlet area is equal to zero (100% fresh), therefore only gives a reflection of airflow pattern in the ventilated room (Li et al., 2003). On the other hand, in many ventilation systems the fresh air is mixed with return air, and thereby the age of air at the inlet area cannot be zero. To account for this, the age of air concept was extended to the socalled ‘‘total age of air” concept by considering the impact of the air delivery process on age of air (Li et al., 2003). Méndez et al. (2008) recently used this total age of air concept in order to numerically optimize the ventilation in a two-bedded hospital room. They compared four different configurations (with slight geometrical changes in room design) in terms of air-exchange efficiency. Local MAA can be obtained experimentally by tracer gas methods (pulse, step-up, and step-down (decay) method) or determined numerically using computational fluid dynamics (CFD) techniques. The tracer gas methods are based on releasing a tracer gas according to a predefined mode and measuring the change in tracer gas

91

V. Chanteloup, P.-S. Mirade / Journal of Food Engineering 90 (2009) 90–103

concentration with time to obtain the age of air. For instance, for the traditional step-down or decay method, the ventilated room is first filled up with a tracer gas which is then removed; the decay curve for the tracer gas concentration due to infiltration of outdoor fresh air into the room is then recorded, and the local MAA corresponds to the area under the transient concentration curve (Chow et al., 2002). Fisk et al. (1997) used a sulphur hexafluoride (SF6) tracer gas step-up procedure to measure age of air in order to compare air change effectiveness in 26 laboratory experiments. The results highlighted significant short-circuiting in ventilation air between the supply air diffuser and the return air grille in experiments with heated supply air, whereas short-circuiting was rarely a problem in experiments with cooled supply air. In a mechanically ventilated laboratory room, Jung and Zeller (1994) compared the experimental step-up, step-down and pulse methods using two tracer gases (SF6 and N2O) simultaneously to study practicability and reproducibility. They showed that the pulse method was not suitable for determining air exchange efficiency from age of air on account of many disadvantages compared to the step-up and the step-down methods. Karlsson (2006) experimentally evaluated the ventilation effectiveness inside a low-energy building, using these methods. He highlighted differences in local MAA distribution according to the floor of the building and even, according to the height in the floor. Moreover, Chow et al. (2002) concluded that all three experimental methods were not really practical for large spaces, due to the heavy costs involved and the great difficulty in mixing the tracer gas thoroughly throughout the space. Hence, they proposed a new experimental method for evaluating ventilation in large enclosed spaces based on the conventional step-down method applied not to the entire large space but just to a control volume of 1 m3. Several positions of this control volume are then considered and the ages measured within each small volume following the study of the transient decay curve of the concentration in tracer gas can be used to characterize the ventilation in a large enclosed space, such as a big hall (Chow et al., 2002). Muhic and Butala (2006) also proposed a new method for measuring local air-change efficiency and local age of air in ventilated rooms, based on the relative decrease of tracer gas only in the first minute of system operation. They validated their new method by comparing experimental data to CFD-predicted values. Local MAA can also be determined by numerical modelling rather than experimental measurement. One of the advantages of numerical simulation over experimental determination is that parameters influencing room air movement, such as wind and buoyancy effects, can easily be evaluated both individually and collectively (Gan, 2000). There are three strategies for numerically predicting MAA: the transient method similar to the experimental step-down or step-up methods, the steady-state method based on the resolution of an additional transport equation, and the particle marker method (Gan and Awbi, 1994). Li et al. (1992) compared these three numerical-based approaches and found that only the transient and the steady-state methods were really reliable for predicting local MAA, with, however, the transient method requiring much more time than the steady-state method to achieve a full solution. With the development of cheaper, more powerful computers and commercial CFD codes over the last ten years, local MAA has increasingly been determined numerically in more or less large enclosed spaces such as clean or hospital rooms (Hu and Chuah, 2003; Rouaud and Havet, 2005; Méndez et al., 2008), classical ventilated rooms (Stankov, 1999; Gan, 2000; Abanto et al., 2004; Lin et al., 2005) or, more originally, in deep coal mines (Parra et al., 2006), in a 3125 m3 underground car park (Chow, 1998) and also, in a very large open space, namely the tidal York River estuary (Shen and Haas, 2004). However, through the determination of local MAA distribution, many numerical studies reported in literature aimed at studying and optimizing the ventilation effectiveness in buildings, by comparing different types of ventilation

systems and air diffusers (Roos, 1998; Hu et al., 1999; Chang et al., 2003; Noh et al., 2008). In the majority of the above-cited studies, local MAA was calculated using the steady-state method by solving an additional partial differential equation describing the transport of a scalar called ‘‘age of air”. In addition to the experimental and numerical methods described above, a new approach called data-based mechanistic (DBM) modelling has been developed to assess MAA in ventilated spaces (Desta et al., 2005; Van Buggenhout et al., 2006). According to these authors, a DBM model is an intermediate type of model that exploits the availability of time-series data in statistical terms and also attempts to produce models having a physical meaning but being simpler than the purely mechanistic CFD models, making it possible to incorporate control algorithms. The objective of the study reported here was to implement the ‘‘age of air” concept into the commercial CFD code Fluent (Anonymous, 2001) through a user-defined function (UDF) – a quantity not calculated by default in the Fluent code, but calculated by some other CFD software specifically developed for solving ventilation problems such as, for example, Ansys Airpak – in order to assess ventilation efficiency inside forced-ventilation food plants. Two transient methods and the steady-state method were incorporated into the code, and their programming was validated by comparing our numerical results with the experimental and numerical results obtained by Bartak et al. (2001, 2002) for a 45 m3 test room with isothermal mixing ventilation. The UDF developed was then used to characterize ventilation efficiency inside a pilot cheese ripening room and inside sausage dryers of large height.

2. Description and validation of the numerical methods tested 2.1. Description of the step-up injection method According to Desta et al. (2005), the concept of age of air can be adapted to either a contaminant or any tracer gas that simulates a contaminant. For the step-up injection method, the tracer gas or the contaminant is assumed to be ‘‘born” when entering the ventilated room and the local MAA at an arbitrary point is the mean time spent for all contaminant particles to reach that point from the inlet area. This means that the ‘‘youngest” air is still at the air supply inlets, whereas the ‘‘oldest” air may be at stagnant zones or held in the air outlets (Lin et al., 2005). It is obvious that local MAA values will significantly increase if part of the air circulates for a long time inside the room, and thus that these values strongly depend on the effectiveness of the ventilation system (Simons and Waters, 2002). The determination of local MAA using the step-up injection method therefore requires that the concentration history with time of the tracer gas is available and thus was previously recorded. Using this history, the local MAA can be quantified through the step-up injection method as follows (Sandberg and Sjöberg, 1983):

sI ¼

Z 0

1

1

C i ðtÞ dt; C i ð1Þ

ð1Þ

where si is the local MAA (s), ci is the contaminant concentration at sampling position i (kg contaminant/kg mixture) and t is time (s). Approaching Eq. (1) numerically using the classical trapezoidal method based on the available data calculated by the unsteady CFD model leads to the age of air distribution inside the ventilated enclosure. 2.2. Description of the step-down injection or tracer decay method The step-down injection method resembles the step-up injection method except that, at the start time, the room’s full volume


is filled with the tracer gas at a homogeneous concentration before injecting fresh air with a null concentration through the air inlets until the room volume is completely ‘‘decontaminated”. Accordingly, in an enclosed space, local MAA represents the time necessary to clean any point in the room (Rouaud and Havet, 2005). Again, this method is also founded on prior knowledge of the transient concentration history. Through the tracer decay method, local MAA is then calculated by approaching the following equation by means of, for example, the classical trapezoidal method:

si ¼

1 ci ðt 0 Þ

Z

1

ci ðtÞ dt;

ð2Þ

3

Step -up inje ction method Série1

HHeight in the test room (m)

92

Step -down inj ection method Série1

2

Steady -state method Série2 Experimental da ta

Série3 (Bartak et al., 2001 & 2002) Numerical data

Série4 (Bartak

et al., 2001 & 2002)

1

t0

where si is the local MAA (s), ci(t0) is the local concentration in tracer gas at time t0, and ci(t) is the local concentration in tracer gas at time t.

0 0

2.3. Description of the steady-state method

HHeight in the test room (m)

Step -up injec tion method Série2

leff 0:7

Steady -state method Série2 Experime ntal da ta

Série3 (Bartak et al., 2001 & 20 02) Numerical data

Série4 (Bartak

et al., 2001 & 2002)

1

0 0

ð4Þ

0.5

1 1.5 Local MAA value under dimensionless for m (-)

2

2.5

3

Step -up injecti on method Série2

Height in the test room (m)

where t is time (s), q is the fluid density (kg.m-3), Ui is the scalar to be solved (namely the age of air si), v is the fluid velocity (m s-1), CUi is the diffusion coefficient of the scalar Ui, and SUi is the source term of the scalar Ui. For accurate predictions, the diffusion term should be taken into consideration together with the convection term in the age of air transport equation, since Gan and Awbi (1994) affirmed that neglecting the diffusion term leads to an over-prediction of the age of air. According to Abanto et al. (2004), the diffusion term C, for the age of air (si), can be numerically calculated from the effective viscosity of the air, leff, using the following simple relation:

Csi ¼ 2:88 105 q þ

Step -down inject ion method Série1

2

ð3Þ

For steady-state conditions, the previous equation can be simplified and written as

vUi Ci rUi Þ ¼ SUi ; r ðq~

2.5

3

CFD codes like Fluent can commonly solve the transport equation for an arbitrary, user-defined scalar in the same way that they solve the transport equation for a scalar such as species mass fraction (Anonymous, 2001). User-defined scalars can therefore be used to implement the calculation of local MAA distribution in the code so that the age of air distribution in an enclosed room can be viewed and the ventilation effectiveness assessed. To calculate the transport of an arbitrary scalar Ui, one additional convection-diffusion equation needs to be solved, taking the following general form:

oqUi r ðCi rUi Þ ¼ SUi : ot

0.5 1 1.5 2 Local MAA va lue under dimensionless form (-)

Step -down inject ion method Série3

2 Steady -state method Série2 Experimental da ta

Série3 (Bartak et al., 2001 & 2002) Numerical data Série1 (Bartak et al., 2001 & 2002)

1

ð5Þ

A value of 2.88 105 in Eq. (5) means a constant laminar viscosity; this may be appropriate only for air temperatures remaining constant around 20 °C. If the air temperature decreases from 20 °C to 0 °C, the difference in laminar viscosity value will reach over 10%. However, this variation could be significant only in areas with laminar flow regime; for turbulent flows, it will have poor effect because turbulent viscosity is higher compared to laminar viscosity. In Eq. (4), the source term Ssi is generally simply taken as equal to 1 (Gan, 2000; Bartak et al., 2002; Hu and Chuah, 2003). The boundary conditions for the solution of Eq. (4) are a zero value at air inlets and a zero gradient at air outlets and at solid wall surfaces (Gan and Awbi, 1994; Gan, 2000; Hu and Chuah, 2003; Lin et al., 2005). In the steady-state method, the age of air is then considered as a passive quantity that does not affect airflow patterns. The local

0 0

0.5 1 1.5 2 Local MAA value under dimensionless form (-)

2.5

Fig. 1. Vertical profiles of local MAA values calculated in a test room from three different numerical methods (first, the step-up injection method; second, the stepdown injection or tracer decay method; and third, the steady-state method consisting in solving an additional scalar transport equation) and compared with the experimental and numerical data obtained by Bartak et al. (2001, 2002). These profiles are located at half-width into the room and at three different distances from the air supply opening, i.e. (a) 1.13 m, (b) 2.2 m and (c) 3.2 m.

MAA is thus determined from the steady-state solution of airflow equations. Unlike the step-up and step-down injection methods, no transient resolution of the additional scalar transport equation is required, thereby probably lowering the total computation time.

93


2.4. Implementation and validation of the three numerical methods All three numerical methods detailed above were implemented in the CFD code Fluent through user-defined functions (UDF). A UDF is a function scripted by a user in C programming language that can be dynamically linked with the Fluent solver in order to enhance the standard features of the CFD code. The exactness of the programming of these UDF was validated by comparing the numerical results determined using our UDF with the experimental and numerical data obtained by Bartak et al. (2001, 2002) within a 45 m3 (4.2-m long, 3.6-m wide and 3-m high) laboratory room with isothermal mixing ventilation. All geometrical details of this test room and its operating conditions are given in Bartak et al. (2001), and were accurately reported in our CFD models. Briefly, the room has only one 0.3 m 0.2 m supplyair opening placed symmetrically on a lateral wall and only one air outlet located on the ceiling close to the lateral wall opposite the air supply. The air inlet was specifically designed in order to generate the most uniform possible air inlet velocity profiles. During the experiments, the test room was placed in a bigger air-conditioned enclosure so that the air temperature was the same (differences lower than 0.3 °C) inside the test room and at the air supply opening. Local MAA was evaluated experimentally at 23 locations in the test room with a reasonable repeatability of 6.5% using SF6 as tracer gas coupled with the step-down method, and numerically from CFD modelling (Bartak et al., 2001, 2002). The numerical methodology used by Bartak et al. (2001, 2002) was accurately adopted in our numerical modelling so that the CFD models built were identical in terms of mesh structure, turbulence model and numerical schemes. Fig. 1a–c shows comparisons of local MAA calculated from the UDF developed and from a three-dimensional (3D) mesh identical to the one built by Bartak et al. (2001, 2002) (21,890 cells and 24,300 vertices) with 13 experimental values and profiles numerically obtained by Bartak et al. (2001, 2002). Each figure presents local MAA profiles calculated according to the three numerical methods tested and previously described, and corresponds to three different distances from the air supply opening, i.e., 1.13 m (Fig. 1a), 2.2 m (Fig. 1b) and 3.2 m (Fig. 1c), respectively. Moreover, local MAA values are given in the dimensionless form calculated from

the product of the local MAA value with the volumetric air flow rate entering the room divided by the room volume (hence, for information, the dimensionless value 1.0 in Fig. 1 corresponds to a local MAA of 538 s). Analysis of Fig. 1a–c leads to three main conclusions: (i) the profiles of the dimensionless local MAA are identical whatever the numerical method used for computing age of air, (ii) our predictions of local MAA values are in close agreement with the numerical values reported by Bartak et al. (2001, 2002), and (iii) the difference between the experimental and our numerical values of local MAA does not exceed 20%, i.e., a discrepancy commonly reported in this type of computation (Davidson and Olsson, 1987; Bartak et al., 2001), thus validating the accurate programming of the three UDF. As illustrated in Fig. 1a–c, the predicted local MAA values are still higher than the measured ones, which is consistent with the results obtained in other studies (Davidson and Olsson, 1987; Bartak et al., 2001, 2002). Bartak et al. (2002) attributed this discrepancy to deviation in experimental repeatability (evaluated at 6.5%), experimental accuracy (5%), uncertainty in accurately determining the volume of the test room (5%) and the use of mean velocities instead of instantaneous values in the CFD model. According to the authors, other sources of discrepancy between experimental and numerical values of local MAA could be assuredly the use of a too coarse mesh that does not give truly independent CFD results, the incorporation of a first-order scheme in the CFD models for assessing the convective terms in the governing equations, since this is known to increase numerical diffusion due to discretization errors (Hirsch, 1988), and the incorporation of the standard k–e turbulence model (Launder and Spalding, 1972), which performs poorly in such a complex, highly 3D airflow that includes separation, reattachment and recirculation. Computation of local MAA was performed on an Athlon 1900 XP + PC with 1.5 Go of RAM. Computation time ranged from 18 min (and 1097 iterations) when determining the age of air with the steady-state method to 3.8 h (and 9217 iterations) when using the step-up injection method. With the step-down method, the computation time was intermediate, at 1.6 h, and the total convergence needed 3836 iterations. As indicated before, the fact that unsteady computation was required to calculate the age of air with

Rows of stacks of cheese models

Blowing duct fitted with holes System for automatically moving the sensors

Airflow rate : 1600 m3.h -1

Data logg er

Stacks of racks of cheese models Blowing duct Extraction duct

(a)

(b) Extraction duct

Fig. 2. Description of the geometry of the pilot cheese ripening room investigated in this study: (a) an inside view, and (b) a top view.

94


the step-up and the step-down injection methods strongly increased the total computation time. In reality, the main drawback of both these methods is the difficulty in accurately determining the time when equilibrium is reached so that the calculation can be stopped, i.e. maximal value of concentration in tracer gas in the step-up injection method and minimal value of this same concentration in the step-down injection method. For all these reasons, the steady-state method represents a good compromise between the accuracy of local MAA prediction and computation time, and thus therefore used in the next phase of study. Furthermore, the steady-state method can also be used in a transient way when the operation of the food plant varies with time, as in modern meat dryers. 3. Application to the assessment of the ventilation efficiency inside two types of ventilated food plants The steady-state method previously identified as the best solution for computing age of air was applied to assess ventilation efficiency inside two types of ventilated food plants, namely a cheeseripening room and two sausage-dryers of large height.

3.1. Description of the two food plants 3.1.1. Cheese-ripening room Fig. 2 shows the geometry of the pilot ripening room investigated in this study. The room was 5.8-m long, 4.95-m wide and 2.95-m high, which gives an overall volume of about 84 m3. Six rows (visually three, since they were placed two by two) of 7 stacks of 16 racks of 21 cheese models were thus installed inside this room to obtain a filling pattern that was representative of current industrial practice. An air-conditioning system composed of two fans and two batteries was installed in a space located above the ceiling of the pilot ripening room. This arrangement controlled the temperature and flow rate of the air blown into the room. Inside the pilot ripening room, the ventilation system was designed to mimic a common industrial configuration, i.e., an air-conditioning system placed at the end of the room, where the lower part extracted air and the upper part blew in the conditioned air through a duct. This system was composed of a 340-mm blowing duct made of textile material, and a 315-mm suction duct. The blowing duct running along the ceiling at half-width in the room was fitted on each side with three

Extraction ducts fitted with extraction apertures

Height : from 2m to 6 m

Blower ducts fitted with conical jets

Area of filling Area filled with meat products with meat products About 0.5 m

Width : from 2 m to 7 m (Length : from 2 m to 20 m) Fig. 3. Layout of a typical modern sausage dryer according to a vertical section with a schematic description of the airflow patterns (black arrows).


rows of several hundred 6-mm-diameter holes. After being blown into the room, the air was extracted at 35 cm from the ground by means of the suction duct placed against a vertical lateral wall at half-width in the room. The suction duct was connected to the space located above the ceiling of the pilot ripening room where the fans were installed. The full airflow rate blown into the room was 1600 m3 h1, i.e., an air change rate of 19 volumes per hour, which reflects normal industrial practice. 3.1.2. Sausage-dryer Fig. 3 shows the layout of a typical modern sausage dryer with a schematic description of the airflow pattern. The geometry of this type of meat dryer is simple; the airflow is supplied through two stainless steel ducts of rectangular cross-section fitted with plastic conical jets placed on each side of the plant, and air is extracted at ceiling level by means of stainless steel ducts fitted with adjustable

95

plastic extraction apertures. However, when attempting to create homogenous drying conditions throughout the entire volume of a plant, it is important to take into account the fact that the operation of a modern sausage dryer is time-dependent, and this for two reasons. Firstly, as regards air distribution, the airflow supplied by the two lateral blower ducts ranges from a high to a low rate in the first duct within a few dozen seconds while it reciprocally ranges from a low to a high rate in the second duct, thus giving rise to a periodic ventilation cycle. However, the overall blower airflow rate remains steady at any given time, at 100%. The ventilation cycle generally has a sinusoidal form, since the distribution of airflow rate in the two blower ducts is set by adjusting a control valve placed on the duct supplying air to the two blower ducts. The two inlet airflows are then directed downstream, descending along the lateral room walls before merging over the floor in a location

Fig. 4. Distribution of the calculated local MAA around and through the stacks in the pilot ripening room according to (a) a vertical section located at half-length into the room and (b) a top view located at 169 cm from the ground, i.e. 10 cm above the top of the stacks of cheese models.

96


that depends on the value of the ventilation cycle. Once merged, the airflow then moves upward towards the extraction ducts, at the same time bathing the sausages filling the plant and exchanging heat and moisture with them. All sausages are consequently bathed by the same airflow during full periods of the ventilation cycle, which a priori means they are dried in a homogeneous way. Furthermore, during ventilation cycles, air velocity ranges from 5 to 20 m s1 at the output of the conical blower duct jets, averaging 0.4 m s1 around the products. Secondly, the specific ventilation pattern previously described and which is intended to supply air to the dryer alternates rapidly with a rest sequence in which there is no ventilation, depending on the level of relative humidity in the volume filled with the products; this relative humidity increases due to water evaporating from the surface of the sausages. Ventilation is reactivated once a

high level of relative humidity is reached, in order to reduce relative humidity until a satisfactorily low level is achieved. Periods of ventilation generally correspond to around 1/3 of total ripening time. A modern dryer therefore works by a series of ventilation cycles and rest sequences managed by an operator who generally uses empirical rules to adjust the drying conditions. In practice, these rules are known to be affected by the dryer design (dimensions, location of the blower and extraction ducts, etc.) and filling level, as well as by the amplitude of the ventilation cycle (Mirade, 2003). In the race to increase the competitiveness of dried meat products, production has been excessively mechanized in recent years with the introduction of robots, thus leading to a strong increase in height of modern meat dryers that now commonly reach 6-m high. Many manufacturers have reported poor sausage drying in

Fig. 5. Vertical section located at half-length of the pilot ripening room showing the measured air velocity field around (a) and through the stacks (b) of cheese models (in the upper left-hand corner, a top view shows where the section crosses the room).


this type of dryer, particularly in the upper half just above the lateral blower ducts, where products appear too moist and too mould-ridden during the drying process, probably as a result of insufficiently ventilated areas. To address this problem, some plant designers have installed a specific system that inverts airflows during the ventilation periods. Some systems blow air at a steady airflow rate without a ventilation cycle via the ducts placed at ceiling level and extract it via ducts fixed to the lateral walls of the plant. The difficulty then lies in setting the duration of the periods when the airflow is inverted. 3.2. Local MAA distribution in the cheese ripening room From the geometrical configuration presented in Fig. 2, the Fluent code (Anonymous, 2001) was used to construct numerical models based on a 3D mesh of 1.2 million hexahedral and tetrahedral cells. About five thousand hexahedral cells were used for meshing each of the three rows of the stacked racks of cheese models. Each hole of the blowing duct was meshed with at least 8 triangular cells, while the outlet area was meshed with 328 triangular cells. To link the fine mesh of the holes to the coarser mesh built into the other parts of the blowing duct, hexahedral ‘boundary layer’-type cells were placed in the vicinity of the holes. During the calculations, airflow was considered as steady, incompressible and turbulent. Besides the Navier–Stokes equations, the energy conservation and species transport equations were also solved in order to calculate air temperature and relative humidity fields inside the pilot ripening room. Main flow turbulence was taken into account using the standard k–e model (Launder and Spalding, 1972) far from the walls, which were assumed to be smooth and where the standard wall function was applied. The SIMPLE algorithm (Patankar and Spalding, 1972) was chosen for coupling pressure and velocity and introducing pressure into the continuity equation. The first-order upwind differencing scheme was also chosen as the discretization scheme for the convection terms of each governing equation. Although first-order schemes therefore give less accurate results due to numerical discretization error (Hirsch, 1988), better convergence of calculation is obtained when using first-order versus second-order schemes.

97

In the inlet area corresponding to the outlet from the blowing holes, an air velocity of 11 m s1 determined from in situ measurements, a turbulence rate arbitrarily valued at 10%, an air temperature of 286 K, a relative humidity of 86% and a zero MAA value were specified. A classical outflow-type boundary condition and a zero MAA gradient were applied at the bottom of the extraction duct. It was also necessary to apply a zero MAA gradient at the walls of the plant. The material filling the ripening room (i.e. the rows of cheese models) was considered as a porous medium which was modelled by the addition of a momentum source term to the standard fluid flow equations. A precise description of the porous medium formulation used can be found in Mirade and Daudin (2006). Constant heat and water vapour source terms were introduced directly into the porous medium to account for, in a very straightforward way, the interaction between air and cheeses. Their values (10 W m3 and 3 kg s1 m3, respectively) were determined from industrial data. By solving an additional specific scalar transport equation allowing MAA to be computed by means of a UDF compiled into the Fluent code (Anonymous, 2001), MAA distribution was calculated in three dimensions in the pilot ripening room. Fig. 4a and b shows a strong heterogeneity in MAA distributions calculated at half-length (Fig. 4a) and at 169 cm from the ground (Fig. 4b) of the pilot ripening room, since MAA values ranged from less than 150 s near the ceiling and the lateral walls to approximately 220 s at half-width and half-height of the room. These figures also indicate that the high MAA values were above all located in the row of stacked racks of cheese models located in the middle of the plant. Fig. 5a and b, showing the measured air velocity field both around the stacks (Fig. 5a) and into the stacks (Fig. 5b) according to a vertical section located at half-length in the pilot ripening room, indicates that the air blown through the holes flowed along the ceiling and the lateral wall at a velocity of over 0.4 m s-1, before splitting into two bodies when reaching the top of the side stacks. From here, the first body of air continued to flow down along the wall before entering the stacks, while the second body of airflow appeared to travel towards the blowing duct, giving rise to a swirl above the side stacks and to the formation of a poorly ventilated area above the stack located at half-width in the room and

Fig. 6. Vertical section located at half-length of the pilot ripening room showing the calculated relative air humidity field around and through the stacks of cheese models.

98


underneath the blowing duct (Fig. 5a). Inside the side stacks of cheese models, there appeared a marked gradient in relation to height as the air velocities peaked at 0.3 m s-1 in the lower part of the stack while the upper part was obviously poorly ventilated. Furthermore, ventilation within the stack located in the middle of the room was clearly poor, whatever the height considered (Fig. 5b). Thorough comparison between the measured air velocity field and the predicted local MAA values unambiguously indicated that the lowest clearly-highlighted MAA values corresponded to the most ventilated areas (with air velocities exceeding 0.4 m s1), while the highest MAA values were located in areas where air velocities were clearly lower than 0.1 m s1. Fig. 6, depicting the relative air humidity field calculated at halflength of the pilot ripening chamber, reveals that air moisture, which was 86% immediately at the output of the blower holes, rap-

idly reached 90–92% near the ceiling and the lateral walls (i.e. the most ventilated areas into the room) following a mixing of different air streams. Moreover, the poor ventilation in the upper part of the middle stack located underneath the blower duct (Fig. 5b) undoubtedly caused the increase in relative air humidity that locally peaked at 97%. This increase indicated a strong accumulation of airborne water vapour that was further increased by the fact that air temperature in this area also increased by more than 0.8 °C, as calculated by the CFD model (Mirade et al., 2005). This means that the water mass in air increased in reality by 1 g kg1 of dry air (i.e. by about 12%) as a result of very low air velocities at that location. Fig. 6 also shows a slight dissymmetry in the relative air humidity field according to a plane crossing the blowing duct, which was almost certainly due to a slight dissymmetry in the air velocity field, although this dissymmetry was far from

Fig. 7. Distribution of the mean air velocity (a) and the local MAA (b) calculated from 2D unsteady CFD modelling on a vertical section of a 6-m high and 4.3-m wide industrial sausage dryer running under standard conditions, over 5 min (i.e. 5 periods) of a ventilation cycle of sinusoidal form and amplitude 90%/10%, and without air inlet inversion.


obvious when examining the calculated air velocity patterns in the full width of the pilot ripening room (Fig. 5a). A comparison of Fig. 6 and Fig. 4a highlights strong similarities between the distribution of relative air humidity and local MAA. The three previous figures (Figs. 4ab, 5, 6) proved that MAA appeared to be a better and a more sensitive criterion than mean air velocity in detecting insufficiently ventilated areas leading to a combined accumulation of heat and moisture that is potentially harmful to the cheese ripening process. Unlike the mean air veloc-

99

ity criterion, the MAA criterion was so pertinent that the dissymmetry in airflow patterns was clearly highlighted, as in the calculated relative air humidity distribution. In reality, this imbalance between the two sides of the room was due to dissymmetrical filling of the room, because the rows of stacked racks of cheese models were not accurately distributed in relation to the blowing duct; the median plane of the filling of the pilot ripening chamber was out of line with the median plane of the blowing duct by about 15 cm.

Fig. 8. Distribution of the local MAA calculated from 2D unsteady CFD modelling on a vertical section of a 6-m high and 4.3-m wide industrial sausage dryer over 10 min of ventilation corresponding to: (a) 8 min of ventilation cycles of sinusoidal form and amplitude 90%/10%, and 2 min where the air inlet was inverted; (b) 7 min of ventilation cycles and 3 min of air inlet inversion; (c) 6 min of ventilation cycles and 4 min of air inlet inversion; and (d) 5 min of ventilation cycles and 5 min of air inlet inversion.

100


3.3. Local MAA distribution in the sausage dryer Local MAA and air velocity distributions were calculated from CFD models based on an unstructured two-dimensional mesh of about 60,000 tetrahedral cells, for different configurations of two modern 6-m high industrial sausage dryers by solving the specific scalar transport equation into the Fluent code (Anonymous, 2001). The aim of these calculations was to identify a technical solution that gives the best globally evened ventilation around the sausages. The numerical methodology adopted for calculating

the 2D airflow pattern was identical to that set-up in Mirade (2003), namely the joint use of the second-order upwind differencing scheme, of the PISO algorithm (Issa, 1985) and of the Reynolds-Stress model for modelling turbulence coupled with a non-equilibrium wall function (Warsi, 1993). To account for the time-dependent boundary conditions due to the ventilation cycle, specific UDF were written and applied to the inlet areas, leading to an unsteady airflow calculation. At each time step, the airflow and local MAA distributions were calculated and then averaged on one or several full periods of ventilation cycle, before analysis.

Fig. 8 (continued)

101


The geometry of the two sausage dryers investigated was identical to that depicted in Fig. 3 with conical blowing jets located at 2.9 m from the ground; the only difference between the two plants being the width: 4.3 m for the narrowest, and 5.3 m for the widest. In standard dryer configurations, for one period of a ventilation cycle of amplitude 90%/10% and without air inlet inversion, CFD calculations revealed a lack of ventilation in the upper half of the plants compared with the lower half, with air velocities below 0.2 m s1 (Fig. 7a) and MAA values reaching 70 s at the level of the rows located immediately above the lateral blower ducts and even peaking at 76 s near the ceiling (Fig. 7b). Air velocities in the lower half of the dryer were higher, reaching 0.8 m s1 at 50 cm from the ground, and the MAA values remained below 64 s as a result of the higher ventilation at that location. These findings unambiguously corroborate the observations made in industry in this type of modern sausage dryer, where over-moist and excessively moulded sausages are found just above the lateral blower ducts during the drying process. These results also confirmed the potential advantages of inverting the airflow inlet inside the dryer in order to invert this imbalance in airflow distribution between the upper and the lower halves of the dryer. Fig. 8 illustrates local MAA calculated in the 4.3-m wide sausage dryer, taking into account an air inlet inversion of 2 min (Fig. 8a), 3 min (Fig. 8b), 4 min (Fig. 8c) or 5 min (Fig. 8d) over 10 min of ventilation. Even though numerical results revealed few differences in the air velocity distribution between the four configura-

tions (Table 1 indicates air velocities ranging from 0.17 to 0.25 m s1), the analysis of Fig. 8 shows marked differences in local MAA distribution. Increasing the inversion of the air inlet led to marked gradients in MAA distribution in relation to height; when the inversion reached 5 min (Fig. 8d), local MAA values ranged from less than 55 s at ceiling level near the extraction ducts to over 105 s (exactly 130 ) in the lower part of the dryer (giving a mean of 79 s with a standard deviation of about 27 s, Table 1), whereas the heterogeneity of MAA distribution was much lower when using an inversion time of 2 min (Fig. 8a), which gave a mean of 67 s and a standard deviation below 5 s between the top and bottom of the dryer (Table 1). Accordingly, inverting the air inlet in the dryer significantly modified air distribution in the plant, even improving ventilation homogeneity around the sausages compared with the standard operation without air inlet inversion, provided that the inversion time did not exceed one fifth of the total ventilation time. Further calculations (data not shown) still combining both local air velocity and MAA distributions demonstrated that changing the height of the conical blowing jets between 2 and 4.1 m had a very poor effect on obtaining even ventilation in the area filled with the sausages, regardless of whether a 4.3 m or 5.3 m dryer was used. For example, in the 5.3-m wide sausage dryer, the mean air velocities ranged from 0.38 to 0.44 m s-1 with a standard deviation of 0.19/0.20 m s1, and mean MAA ranged from 34 to 36 s with a standard deviation below 5 s in all cases. Halving the duration of the ventilation cycle from 60 s to 30 s combined with the addition

Table 1 Influence of the duration of air inlet inversion over 10 min of ventilation on both mean air velocity and local MAA calculated on a vertical section of a 6-m high and 4.3-m wide industrial sausage dryer from 2D unsteady CFD modelling

Mean air velocity (m s1) Standard deviation (m s1) Mean MAA (s) Standard deviation (s)

Over 10 min of ventilation including 2 min of air inlet inversion




0.25 0.12 67.2 4.6

0.22 0.10 69.3 10.5

0.20 0.09 73.7 17.7

0.17 0.07 79.1 26.6

Fig. 9. Distribution of the local MAA calculated from 2D unsteady CFD modelling on a vertical section of a 6-m high and 5.3-m wide industrial sausage dryer over 10 min of ventilation (8 min of a ventilation cycle of sinusoidal form and amplitude 90%/10%, and 2 min of air inlet inversion) and where two 0.35-m long horizontal deflectors were fitted just above each blower duct.

102


of a third extraction duct at ceiling level had no effect on airflow patterns inside the two large-height dryers studied (air velocity mean of 0.42 m s1 compared to 0.41 m s1 in the standard configuration, and mean local MAA lowered by 3 s, at less than 32 s). Moreover, scaling down the amplitude of the ventilation cycle from 90%/10% to 60%/40% without inverting the air inlet during ventilation logically led to an increase in air velocities around the sausages located in the lower half of the plant, but to the detriment of the upper half, thus accentuating the height stratification phenomenon, particularly in terms of local MAA distribution. Finally, the numerical investigation carried out revealed that the best distribution of local MAA (mean value of 18 s and standard deviation below 1 s) was obtained when a 0.35-m long horizontal deflector was fitted just above each lateral blower duct in the 5.3m wide modern sausage dryer, for an air inlet inverted for 2 min for every 10 min of ventilation (Fig. 9). On the other hand, the air velocities were not homogeneously distributed around the sausages, since standard deviation reached 0.17 m s1 for a mean of 0.37 m s1. In addition, the strong impact on homogeneity of MAA distribution was much less marked when the same deflectors were added in the numerical model corresponding to the second sausage dryer, which was narrower. Taken together, these numerical conclusions confirm that airflow patterns are very difficult to assess a priori; they also underline how local MAA is able to give key information on ventilation distribution by detecting pockets of inadequate ventilation that could be harmful both to final product quality and process efficiency. 4. Conclusion This paper reported the implementation of the ‘‘age of air” concept into the commercial CFD code Fluent through UDF in order to assess ventilation efficiency inside forced-ventilation food plants. Two transient methods and the steady-state method were incorporated into the code and validated by comparing the results with experimental and numerical data obtained by Bartak et al. (2001, 2002) for a 45 m3 test room with isothermal mixing ventilation. The results indicated that calculating local MAA using the steady-state method, by solving an additional partial differential equation describing the transport of the scalar ‘‘age of air”, proved the best compromise between accuracy of results and computation time. This method was then used to characterize ventilation efficiency inside a cheese ripening room and inside a sausage dryer. In the case of the pilot ripening room, local MAA distribution allowed to detect a large insufficiently ventilated area located at half-height and half-width in the room that leads to a combined accumulation of heat and moisture that is potentially harmful to the cheese ripening process. Unlike the mean air velocity criterion, the MAA criterion was so pertinent that it highlighted a dissymmetry in airflow patterns due to a dissymmetry in how the room was filled, as with the calculated relative air humidity distribution. In the case of modern sausage dryers of large height, both local MAA distribution and mean air velocity distribution revealed a lack of ventilation in the upper half of the plants compared with the lower half, thus corroborating the observations made in industry indicating the appearance of over-moist and excessively moulded sausages during the drying process, just above the lateral blower ducts. Other local MAA distributions were then calculated with the aim of identifying a technical solution that would yield the best ventilation around the sausages. In light of the results presented in this paper, local MAA seems to be a better and a more sensitive parameter than mean air velocity for highlighting areas with inadequate ventilation and thus for

assessing ventilation efficiency in industrial food plants. However, further research work will be still needed to link local MAA values to product quality in order to identify which local MAA value for a given air treatment-based food process (drying, ripening, chilling, cooking, etc.) will give the best and most efficient product treatment process. Acknowledgements The authors warmly thank L. Picgirard of the ADIV association and A. Kondjoyan for their cooperation. We also thank A. Lasteyras, J.M. Auberger, S. Portanguen and L. Lasteyras for building and equipping the pilot ripening room, and ATT for re-reading the English version of this manuscript. References Abanto, J., Barrero, D., Reggio, M., Ozell, B., 2004. Airflow modelling in a computer room. Building and Environment 39, 1393–1402. Anonymous. 2001. Fluent 6: User’s Guide. Fluent Inc., Lebanon. Bartak, M., Cermak, M., Clarke, J.A., Denev, J., Drkal, F., Lain, M., Macdonald, I.A., Majer, M., Stankov, P., 2001. Experimental and numerical study of local mean age of air. In: Proceedings of the 7th International Building Performance Simulation Association (IBPSA) Conference, Rio de Janeiro, Brazil. Bartak, M., Beausoleil-Morrison, I., Clarke, J.A., Denev, J., Drkal, F., Lain, M., Macdonald, I.A., Melikov, A., Popiolek, Z., Stankov, P., 2002. Integrating CFD and building simulation. Building and Environment 37, 865–871. Chang, H., Kato, S., Chikamoto, T., 2003. Room air distribution and indoor air quality of hybrid air conditioning system based on natural and mechanical ventilation in an office. International Journal of Ventilation 2 (1), 65–75. Chow, W.K., 1998. On safety systems for underground car parks. Tunnelling and Underground Space Technology 13 (3), 281–287. Chow, W.K., Fung, W.Y., Wong, L.T., 2002. Preliminary studies on a new method for assessing ventilation in large spaces. Building and Environment 37, 145–152. Dabin, E., Jussiaux, R., 1994. Le Saucisson Sec. Erti, Paris. Davidson, L., Olsson, E., 1987. Calculation of age and local purging flow rate in rooms. Building and Environment 22 (2), 111–127. Desta, T.Z., Van Buggenhout, S., Van Brecht, A., Meyers, J., Aerts, J.M., Baelmans, M., Berckmans, D., 2005. Modelling mass transfer phenomena and quantification of ventilation performance in a full scale installation. Building and Environment 40, 1583–1590. Falconer, R.M., 1993. Cooling air distribution in a cheese store. In: Proceedings of the Meeting of Commissions B1, B2, D1, D2/3 of the International Institute of Refrigeration, Palmerston North, New Zealand. Federspiel, C.C., 1999. Air-change effectiveness: theory and calculation methods. Indoor Air 9, 47–56. Fisk, W.J., Faulkner, D., Sullivan, D., Bauman, F., 1997. Air change effectiveness and pollutant removal efficiency during adverse mixing conditions. Indoor Air 7, 55–63. Gan, G., 2000. Effective depth of fresh air distribution in rooms with single-sided natural ventilation. Energy and Buildings 31, 65–73. Gan, G., Awbi, H.B., 1994. Numerical prediction of the age of air in ventilated rooms. In: Proceedings of 4th International Conference on Air Distribution in Rooms, Krakow, Poland. Hirsch, C., 1988. Numerical Computation of Internal and External Flows: Computational Methods for Inviscid and Viscous Flows. John Wiley & Sons, Chichester. Hu, S.C., Barber, J.M., Chuah, Y.K., 1999. A CFD study for cold air distribution systems. In: Proceedings of the 1999 ASHRAE Winter Meeting, Chicago, USA. Hu, S.C., Chuah, Y.K., 2003. Deterministic simulation and assessment of airrecirculation performance of unidirectional-flow cleanrooms that incorporate age of air concept. Building and Environment 38, 563–570. Issa, R.I., 1985. Solution of the implicitly discretized fluid flow equations by operator-splitting. Journal of Computational Physics 62, 40–65. Jung, A., Zeller, M., 1994. An analysis of different tracer gas techniques to determine the air exchange efficiency in a mechanically ventilated room. In: Proceedings of 4th International Conference on Air Distribution in Rooms, Krakow, Poland. Karlsson, J.F., 2006. Experimental evaluation of airflow in a low-energy building. International Journal of Ventilation 5 (2), 239–248. Launder, B.E., Spalding, D.B., 1972. Mathematical Models of Turbulence. Academic Press, London. Li, Y., Fuchs, L., Holmberg, S., 1992. Methods for predicting air change efficiency. In: Proceedings of the 3rd International Conference on Air Distribution in Rooms, Aalborg, Denmark. Li, X., Li, D., Yang, X., Yang, J., 2003. Total air age: an extension of the air age concept. Building and Environment 38, 1263–1269. Lin, Z., Chow, T.T., Fong, K.F., Tsang, C.F., Wang, Q., 2005. Comparison of performances of displacement and mixing ventilations. Part II: indoor air quality. International Journal of Refrigeration 28, 288–305.

V. Chanteloup, P.-S. Mirade / Journal of Food Engineering 90 (2009) 90–103 Méndez, C., San José, J.F., Villafruela, J.M., Castro, F., 2008. Optimization of a hospital room by means of CFD for more efficient ventilation. Energy and Buildings 40, 849–854. Mirade, P.S., 2003. Prediction of the air velocity field in modern meat dryers using unsteady computational fluid dynamics (CFD) models. Journal of Food Engineering 60, 41–48. Mirade, P.S., Daudin, J.D., Picque, D., Corrieu, G., 2005. Interaction entre l’ambiance des caves et l’affinage des fromages. Revue Générale du Froid 1055, 37–44. Mirade, P.S., Daudin, J.D., 2006. Computational fluid dynamics prediction and validation of gas circulation in a cheese-ripening room. International Dairy Journal 16, 920–930. Muhic, S., Butala, V., 2006. Effectiveness of personal ventilation system using relative decrease of tracer gas in the first minute parameter. Energy and Buildings 38, 534–542. Noh, K.C., Han, C.W., Oh, M.D., 2008. Effect of the airflow rate of a ceiling type airconditioner on ventilation effectiveness in a lecture room. International Journal of Refrigeration 31, 180–188. Pajonk, A., 2001. La solution flux variables alternants pour homogénéiser l’ambiance. Revue Laitière Française 614, 20–22. Parra, M.T., Villafruela, J.M., Castro, F., Méndez, C., 2006. Numerical and experimental analysis of different ventilation systems in deep mines. Building and Environment 41, 87–93. Patankar, S.V., Spalding, D.B. , 1972. A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. International Journal of Heat and Mass Transfer 15, 1787–1806.

103

Roos, A., 1998. The air exchange efficiency of the desk displacement ventilation concept: theory, measurements and simulations. In: Proceedings of the 6th International Conference on Air Distribution in Rooms, Stockholm, Sweden. Rouaud, O., Havet, M., 2005. Numerical investigation on the efficiency of transient contaminant removal from a food processing clean room using ventilation effectiveness concepts. Journal of Food Engineering 68, 163–174. Sandberg, M., Sjöberg, M., 1983. The use of moments for assessing air quality in ventilated rooms. Building and Environment 18 (4), 181–197. Shen, J., Haas, L., 2004. Calculating age and residence time in the tidal York River using three-dimensional model experiments. Estuarine, Coastal and Shelf Science 61, 449–461. Simons, M.W., Waters, J.R., 2002. Ventilation effectiveness parameters resulting from mechanical ventilation with recirculation. International Journal of Ventilation 1 (2), 119–126. Stankov, P., 1999. CFD prediction of air flow in a ventilated room as a tool for emerging design. Journal of Theoretical and Applied Mathematics 29 (2), 101– 112. Van Buggenhout, S., Desta, T.Z., Van Brecht, A., Vranken, E., Quanten, S., Van Malcot, W., Berckmans, D., 2006. Data-based mechanistic modelling approach to determine the age of air in ventilated space. Building and Environment 41, 557–567. Warsi, Z.U.A., 1993. Fluid Dynamics: Theoretical and Computational Approaches. CRC Press, Boca Raton.