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Accepted Manuscript
Title: Ef ficacy of greenhouse natural ventilation:environmental monitoring and CFD simulations of a study
case
Author: Stefano Benni Patrizia Tassinari Filippo Bonora
Alberto Barbaresi Daniele Torreggiani
PII: S0378-7788(16)30375-9
DOI: http://dx.doi.org/doi:10.1016/j.enbuild.2016.05.014
Reference: ENB 6649
To appear in: ENB
Received date: 2-10-2015
Revised date: 13-4-2016
Accepted date: 6-5-2016
Please cite this article as: Stefano Benni, Patrizia Tassinari, Filippo Bonora,
Alberto Barbaresi, Daniele Torreggiani, Ef ficacy of greenhouse natural ventilation:
environmental monitoring and CFD simulations of a study case, Energy and Buildings
http://dx.doi.org/10.1016/j.enbuild.2016.05.014
This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.
The manuscript will undergo copyediting, typesetting, and review of the resulting proof
before it is published in its final form. Please note that during the production process
errors may be discovered which could affect the content, and all legal disclaimers that
apply to the journal pertain.
http://dx.doi.org/doi:10.1016/j.enbuild.2016.05.014http://dx.doi.org/10.1016/j.enbuild.2016.05.014http://dx.doi.org/10.1016/j.enbuild.2016.05.014http://dx.doi.org/doi:10.1016/j.enbuild.2016.05.014
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Efficacy of greenhouse natural ventilation:
environmental monitoring and CFD simulations of a study case
Stefano Benni*, Patrizia Tassinari a , Filippo Bonora b ,
Alberto Barbaresi c , Daniele Torreggiani d
Department of Agricultural Sciences, University of Bologna. Viale Fanin 48, 40127Bologna (Italy)
*Corresponding author, email: [email protected]
phone +39 051 2096166 fax +39 051 2096171
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]
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Graphical Abstract
Highlights
CFD analyses of natural ventilation in an horticultural greenhouse were
performed
Cooling achievable through various configurations of vents opening of
was assessed
Closed windward roof vent and open wall vent entailed 64% of maximum
heat removal The other possible scenarios showed a performance index of about 50%
Results suggest to enhance vent control system considering wind
direction as input
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Abstract
Indoor microclimate control is fundamental in greenhouse design, and vent
dimensions and positions play a crucial role in natural ventilation management.This research considers an Italian greenhouse for horticultural production and
aims at identifying optimal vent configurations and opening management
procedures for indoor environment control, focusing on summer cooling.
Numerical modelling of airflows and temperature distributions was carried out
through finite element CFD software, with streamline upwind discretization
schemes for advection terms. Calibration of the numerical modelling was
performed by comparing data collected in controlled environmental condition
with simulations results. The automatic vent opening system of the greenhouse
is programmed to fully open all the windows of each span when indoor air
temperature overcomes a threshold value. Numerical simulations were
performed to assess the efficacy of this solution in comparison with alternative
strategies. Various configurations of roof vents were tested, with side wall vents
always open. The best performances were obtained with windward roof vent
closed, which entailed 64% of the maximum heat removal achievable through
natural ventilation. The other possible scenarios considered showed a
performance index of about 50%. The results suggest therefore to enhance the
vent control system by considering also wind direction as input.
Keywords: CFD simulation; Airflow; Greenhouse; Cooling
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1. Introduction
The distribution of environmental parameters in greenhouses should be
maintained as much uniform as possible for general purposes of crop production
[1]. In particular, most of the plants grown in greenhouses fit at an optimal
temperature between 17°C and 27°C, with extremes of 10°C and 35°C [2]. Up to
outdoor temperatures of 27°C, internal temperature control can be achieved by
means of ventilation, while for higher external temperatures (30-35°C for
extended periods) it is necessary to intervene by means of artificial cooling in
order to maintain microclimate functional to the physiology of cultivated plants.
Moreover, inlet of outdoor air is essential to replenish the CO2 consumed by
plants for the photosynthesis process. Indoor microclimate control is thus a
fundamental issue in greenhouse design and analysis [3 –6], and natural
ventilation plays a crucial role in indoor climate control [7], as it directly affects
heat and mass transport between the outside environment and the greenhouse,
thus strongly influencing its inside climate [8]. Vent dimensions and positions are
key elements in natural ventilation design. The correlations between ventilation
rates and environmental parameters, including wind speed and direction, havebeen assessed with various approaches [9]. A thermal model based on energy
balance was proposed by [10] to predict the natural ventilation rate of
greenhouses. Decay-rate tracer of N2O techniques were used by Kittas et al. [11]
to experimentally investigate the influence of vent type and screening on airflow
and temperature distribution within a mono-span greenhouse with round-arch
roof and vertical walls. It was found that in case of side openings only, indoor air
velocity showed a fast flow near the ground and low velocity near the roof, while
the combination of roof and side openings caused an increase in air velocity and
a decrease in indoor temperature, together with a greater microclimate
heterogeneity. Demrati et al. [12] computed global energy balance of a large-
scale greenhouse in hot climate, based on the values of inside and outside air
temperature and humidity, outside global solar radiation, and wind speed and
direction measured in significant time spans, thus determining ventilation fluxes.
Airspeed measurements through vents and inside the greenhouse were also
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performed to compute airflow patterns. In this regard, Kittas et al. [13] collected
data through hot-wire anemometry to deepen previous analyses on natural
ventilation in relation to vent opening choices. In particular they focused on
greenhouses ventilated by both roof and side vents and provided experimental
results for the main greenhouse climate parameters (including air velocity and air
temperature) in the whole greenhouse volume, resulting in a database for
validating computational fluid dynamics (CFD) research approaches for
greenhouse ventilation. A discussion on the efficiency of different discretization
methods used as CFD solvers for the simulation of natural ventilation in
greenhouses was developed by Molina-Aiz et al. [14]. Ayuga [15] underlined that
ventilation is one of the major problems in greenhouse technology, and a good
balance between airflow and air speed can be designed by means of proper CFD
analyses. Teitel et al. [16] underlined the importance of analysing air movements
in greenhouses, caused by ventilation, and their effects on indoor microclimate
uniformity. In fact growers has been increasingly tending to exploit every area of
the greenhouse for high quality yields, due to the ever stronger competition
within the global market. Experiments - developed by assessing the decay rate of
a tracer gas (N2O), and by means of 3D sonic anemometer and dry and wet-bulb
thermocouples - showed that ventilation rate increases linearly with wind speed,
and gradients in temperature and air velocity occurred in the vertical and
horizontal directions. CFD simulations were extensively used to investigate the
effects of structure shape, and ventilator size and arrangement (with or without
insect-proof screens) on microclimate [17]. Teitel and Wenger [18] compared
experiments, CFD, and a model relating flow rate through openings to their
pressure drops, to determine the air exchange in a small naturally ventilated
monospan greenhouse. The three methods agreed well up to a wind speed of
about 4 m/s, while at higher wind speeds the ventilation rates were lower, with
consequently diminished air-exchange efficiency, due to imperfect mixing of the
supply air with the greenhouse air. The complexity and realism of both
experimental analyses and simulations has been continuously increased, as
recent studies account for all the relevant variables concerning the greenhousesystem [19]. The effects of vent configurations in terms of natural ventilation
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have been considered as a topical subject in several researches. Bartzanas et al.
[8] numerically investigated the effects of ventilation configuration of a tunnel
greenhouse on airflow and temperature patterns was through CFD with
validation against experimental data. Results indicated that the highest
ventilation rates are not always the best criterion to assess the performance of
different ventilation systems in greenhouses. Analyses must consider also the air
velocities in the zone interested by crops, the efficiencies of ventilation on flow
rate, and the air temperature differences between inside and outside.
Furthermore [20] showed that the basic mechanism of heat transfer in a
greenhouse is the convection due to the entering air stream, also when the
global solar radiation of a sunny day is considered. Sun radiation represents the
greatest heat load for a greenhouse in summer periods in Europe. For example in
the area of Bologna, in Centre-Northern Italy there is an average of 2074 annual
hours of sunshine and 250 h of monthly sunshine from May to September [21].
Nevertheless, in certain conditions of hour angle, airflows and temperature
distributions in a greenhouse can be analysed independently on the
consideration of the external incident radiation [22]. This study deals with the
thermo-fluid-dynamic analysis of a greenhouse in these conditions.
Specifically, this research considers a study case of a common greenhouse for
horticultural production and aims at identifying optimal vent configurations and
vent opening management procedures for indoor environment control, focusing
on summer cooling. The specific objective is the assessment of the efficacy of
natural ventilation strategies in greenhouse cooling, in environmental conditions
which allow target temperatures to be reached by means of natural ventilation,
with no HVAC system. In particular, the study aims at assessing the effect of
natural ventilation in the conditions when the contribution of solar radiation can
be neglected.
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2. Materials and methods
2.1. Greenhouse description
The experimental greenhouse is a 307 m2 three-span greenhouse of the
Department of Agricultural Sciences of the University of Bologna, located in the
Imola branch of the University (44.337340° N latitude, 11.718647° E longitude
and 72 m altitude, about 30 km far from Bologna). The azimuth of spans
longitudinal axis is 55° direction, with access doors located on the north-eastern
front of the building. Each span is 8 m wide, 12.7 m long, and 4 m high at eave,
with 5.5 m maximum height (pitch slope is 40%). The greenhouse has concrete
floor and steel structure, with 4 mm thick coverings of tempered glass. Each span
has two central top roof openings of 25° over 1.50 m length. Two wall openings
of the same size are located at the top of the north-western and south-eastern
side walls (Figure 1). Both roof and side vents are continuous along the whole
length.
The spans correspond to indoor areas separated by glass walls and connected
through internal doors. This study focuses on the south-eastern span (on the left
in Figure 1). This sector is provided with heated benches with aluminium
structure, lamps for supplementary photoperiodic lighting, a reverse osmosis
system for the treatment of irrigation water, and a high-precision mixer for the
formulation of fertilizer solutions. The areas is dedicated to experimental and
educational activities related to propagation, protection, nutrition, and
programming of phenological phases of potted and soilless ornamental plants.
Moreover this sector is used to experience directly microclimate management in
a protected environment and test the interactions between climate parameters
and plants growth. The equipment allows indoor environment of each sector to
be managed independently, by means of a computer which controls the heating
and cooling systems, vent opening, and shading curtains. As mentioned above,
this study focuses on natural ventilation in summer cooling, in conditions when
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sun radiation can be neglected, therefore no operation of HVAC systems and
shading curtains is considered during the trials and simulations. Indoor
temperature and relative humidity are monitored in each sector with 10 min
acquisition time; outdoor meteorological data of illuminance, temperature, wind
speed and direction are measured with the same frequency. The material
properties considered in the numerical models are reported in Table 1.
2.2. Computational approach
Numerical modelling of airflows and temperature distributions was carried out
through Autodesk CFD 2015 software, adopting a finite element approach
[23][24]. This software uses several variants of the streamline upwind
discretization schemes to model the advection terms and a true pressure-
velocity algorithm based on the SIMPLE-R technique [25]. The finite element
method is used to reduce the governing partial differential equations to a set of
algebraic equations. In this method, the dependent variables are represented by
polynomial shape functions over the element (a small area or volume). These
representations are replaced into the governing partial differential equations and
then the weighted integral of these equations over the element is taken where
the weight function is chosen to be the same as the shape function. The result is
a set of algebraic equations for the dependent variable at nodes on every
element.
2.2.1. Governing equations
The governing equations for fluid flow and heat transfer are the continuity
equation, the momentum equations and the energy equation. The first is
expressed as follows:
0
(1)
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where is the density, t is time and u, v , w are the velocity components along
the global coordinate axis respectively x , y , z.
In turbulence modelling, the software solves the time-averaged governing
equations, which are obtained by assuming that the dependent variables can be
represented as a superposition of a mean value and a fluctuating value about the
mean. Using the notation that capital letters represent the mean values and
lower case letters represent fluctuating values for the velocity components and
pressure, the Reynolds averaged momentum equation referred to a generic x
reference axis can be written as follows:
[2 ]
[ (
)]
[ (
)]
(2)
where g x is the gravitational acceleration in x direction, P represents pressure,
is the fluid viscosity and t is the eddy viscosity. SDR is the distributed resistance
term, which can be written in general as:
( )
2 (3)
Where i refers to the global coordinate direction, V i represents the
corresponding fluid velocity component, K is a factor that can be determined
from measurements of pressure drop versus flow rate, f is the friction factor, DH
is the hydraulic diameter and C is the viscosity coefficient, i.e. the inverse of the
permeability.
S is the source term for rotating flow, which can be written in general as:
2 (4)
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where is the rotational speed and r is the distance from the axis of rotation.
For incompressible and subsonic compressible flow, the Reynolds averaged
energy equation, written in terms of static temperature T , is:
[ ]
[
]
[
]
(5)
Where C p il the constant pressure specific heat, k is the thermal conductivity, k t
the eddy conductivity and qV is the volumetric heat source.
The Mixing Length turbulence model was adopted, as it is primarily designed for
internal natural convection analyses for gas flows. Specifically, the eddy viscosity
is calculated according to the expression:
√
(6)
Where l m is a mixing length calculated in the turbulent boundary layer as the
product of the wall parameter and the normal distance from the wall, while the
parameter G is given by:
2 ()
2 ()
2 ( )
( ) ( ) ( )
(7)
2.2.2. Advection scheme and convergence criteria
For numerical stability, the advection terms are treated with upwind methods
along with the weighted integral method. The advection scheme adopted is the
Modified Petrov-Galerkin, a more stable variation of the Petrov-Galerkin one
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[26]. It is suitable for all application types recommended with the latter, but
typically produces more globally conservative results. In particular it has shown
improvements as for accuracy of recirculating and secondary flows, natural
convection stability, compressible flow accuracy and stability, and energy
balance stability.
Transient and steady state analyses were performed for model calibration. For
the latter ones, convergence was assumed based on the union of the following
criteria:
the maximum instantaneous slope of the convergence data of all of the
dependent variables from one iteration to the next is below the set level
of 10-4;
the slope of the convergence data averaged over several iterations of
minimum, maximum and mean values of all of the dependent variables
are below 0.01;
the derivative of the maximum time averaged convergence slope
(concavity) falls below the pre-determined level of 0.01;
the fluctuation of the dependent variable about the mean value shows a
standard deviation below the set level of 10-5.
2.2.3. Numerical modelling
A two-dimensional simulation approach was adopted, as it is considered a
computationally beneficial assumption for the investigation of the transport
phenomena especially at the middle section of a structure with side vents
opened along the whole length [22]. Under these conditions, the 2D model
proved to be an accurate simplification of the three-dimensional phenomenon.
In particular, two-dimensional simulations are used in scientific literature to
investigate the effects of the ventilation configuration of tunnel greenhouses
with inlet flow direction perpendicular to the longitudinal axis of the greenhouse
[8,22]. According to literature, a large 2D geometrical model domain, of 160 m
long and 50 m high, including the greenhouse at its centre, was used (Figure 2
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shows the greenhouse and a portion of the surrounding domain). This domain
was proposed in order to allow the interaction between external and indoor
airflows to be naturally developed [27].
The greenhouse was modelled as an empty building, in order to obtain results
independent on the quantity or type of plants inside the structure, and on their
arrangement. The building envelope was modelled with finite elements with
physical properties corresponding to the real glass panels. The internal walls are
not airtight, therefore the presence of several small openings allowing air
passage among the various spans was modelled as an equivalent permeability of
the internal walls corresponding to 1% of the length, uniformly distributed. The
domain was subdivided into a number of surfaces necessary to identify the
portions characterized by different initial conditions, and to develop meshes with
detail levels suitable for the complexity of the phenomena to be modelled in the
different zones of the domain. The domain was discretized into a hybrid grid that
combine triangular and quadrilateral element types thus providing maximum
flexibility in matching mesh cells with the boundary surfaces. It is well known
that this generally leads to both accurate solutions and better convergence forthe numerical solution methods [28]. In particular, unstructured meshes were
adopted for the various domain portions to generate the numerical grids outside
and inside the greenhouse, with higher density in critical zones of the flow
subject to strong gradients. Grid quality was enhanced through the placement of
three layers of quadrilateral elements in resolving boundaries near solid walls
and desks. The total number of elements in most of the results that are
presented here was 24776, with average dimension of mesh sizes for the indoor
domain of 0.2 m. This value was considered appropriate also in scientific
literature with reference to the same investigation subject [8,22]. Mesh
independency was verified by performing several steady state analyses with
increasingly finer meshes simulating the indoor air. Eight meshes, from about
13000 elements up to more than 52000 were adopted and the results in terms of
air speed and pressure are reported in Figure 3. Transient analysis simulations
with a larger number of elements – 110947 – were also performed, to further
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verify the independency of the numerical results from the grid features adopted,
thus verifying the mesh sensitivity, as indicated in literature [29].
2.3. Measurement trials
Calibration of the numerical modelling was performed by means of an ad-hoc
data collection campaign in controlled environmental condition, and comparison
with corresponding results obtained through CFD simulation. High frequency and
spatial resolution of surveys allowed to obtain detailed data for accurate
calibration. Monitoring of temperature and air speed was performed on 10
February 2015. All data were measured on the vertical cross section intersecting
the midpoint of the greenhouse length. In particular, temperature was recorded
at the centre of the span at heights of 0.25m, 0.85 m, 1.45 m, 2.05 m, and 2.65 m
and on the cultivation benches (0.85m height) at distances from the SE wall of
0.90 m, 2.50 m, 3.60 m, 4.90 m, 7.30 m. Indoor temperature data were first
collected with vents closed; then the side vent was opened and air speed and
temperature were measured in the inlet edge. Temperature was measured with
Delta Ohm thermo-hygrometer HD 2101 with resolution of 0.1°C, accuracy of
±0.1°C, operating temperature of-5°C ÷ +50°C, working relative humidity range of
0 ÷ 90% RH without condensation, total measured values storage of 38000
samples; together with stand-alone-data-logger PCE-HT71 with resolution of
0.1°C, accuracy of ±0.5 C, measurement range of −40 C ÷ +70 C, total memory
storage of 16000 records. Air speed was measured through Delta Ohm DO9847
multi-function datalogger, with working temperature range from -5°C ÷ +50°C,
RH range 0 ÷ 90% not condensing, storage temperature range from -20°C ÷
+60°C. Vane probe anemometer adopted was model AP472 S1, with 100 mm
diameter, speed measurement range from 0.4 m/s ÷ 30 m/s, resolution of 0.01
m/s, accuracy of ± (0.1 m/s + 1.5% full scale). Hot wire probe anemometer
adopted was model AP471 S4, with measurement range from 0.1 m/s ÷ 5 m/s,
resolution of 0.01 m/s, accuracy of ±0.05 m/s (from 0 to 0.99 m/s) and ±0.15 m/s
(from 1.00 m/s to 5.00 m/s). Data were collected with constant time step of 5s.
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Boundary and initial conditions of the model (Table 2) were defined on the basis
of the measured data. In particular, inlet air speed in the domain was defined
based on meteorological data recorded, while outdoor air temperature was set
on the basis of data measured through the external sensors. A symmetry
condition was applied at the top of the domain, while at the bottom boundary
ground temperature of the greenhouse was set at a value obtained from
temperature measured close to the floor during the trials. Differences between
the floor temperature and the air temperature in the position of the indoor
sensor were recorded and their average value was adopted to assess the ground
temperature for simulations under conditions when only data of indoor air
temperature were available. These estimates were adopted as boundary
conditions for numerical simulations.
Air properties have been defined constant on the basis of CFD preliminary
analyses carried out with transient approach referred to the conditions of Table
2, considering either fixed or variable material properties.
Measured and simulated data were compared for validation by computing the
RMSE, defined according to the usual expression:
RMSE= ∑ −̂^=1 , (8)
where N is the number of observations, yi is the measured parameter, and ŷ is
the predicted value.
Moreover, the ratio of percentage deviation (RPD) index was considered for the
time histories of the parameters analysed. RPD is defined as the ratio of standard
deviation of measured data to RMSE of predicted values. The model was
assessed according to Rossel et al. [30]: RPD < 1 means “very poor” model; 1 ≤
RPD < 1.4 means “poor” model; 1.4 ≤ RPD < 1.8 means the model is “fair”,
therefore it can be adopted for general considerations; 1.8 ≤ RPD < 2 indicates a
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“good” model; 2 ≤ RPD < 2.5 indicates a “very good” model; and RPD ≥ 2.5 means
the model is “excellent”.
The soundness of our model regarding the prediction on air speed was further
assessed according to scientific literature [31], by comparing the RMSE of our
calibration with that obtained from a statistical model adopted as a benchmark
for short-term predictions of air speed. The statistical model adopted is a multi-
linear regression one (MLR) developed by Mathur and Mathur [32], where the
following quantities are the independent variables:
- weighted moving average (MA) defined as follows
MA2, t y1∗p1+y∗pp1+p (9) where yt-1 and yt-2 are air speed respectively at time (t -1) and (t -2); pi are
weights. Lag was choses equal to 2 based on autocorrelation lower limit of
0.7. Autocorrelation values obtained were adopted as weights p1 and p2;
- Exponential moving average EMA(2,t ) with lag of 2 data, defined as
EMA(2,1)=y 0; EMA(2,t )= * y t- 1+(1- )*EMA(2,t -1), with
=2/(lag+1)=2/3;
- Oscillator (OSC), defined as the difference of moving averages or exponential
moving averages of two different periods: OSC (t )= EMA(3,t )-EMA(2,t ), where
EMA(3,t ) is defined as EMA(3,0)= y 0 ; EMA(3,t )= *y t-1+(1- )*EMA(3,t -1),
with =2/(lag+1)= 1/2
- Rate of change within time steps ROC(t );
- Second and third moment of the series M2(t ) and M3(t ). The r th moment of y ,
Mr is given by
∑ −̅ (10) where ̅ is the mean of N variables.
Therefore the autoregressive model is expressed as follows:
ŷ (t ) = a0 + a1*MA(2,t )+a2*OSC (t ) + a3*ROC (t ) +
+a4*EMA(2,t ) + a5*M2(t ) + a6*M3(t ) (11)
2.4. Numerical simulations
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The study aims at investigating the effect of vents opening in terms of natural
ventilation, independently on the other factors potentially affecting indoor
climate, in particular solar radiation. Therefore the analyses were performed in
condition where solar radiance can be neglected, namely for hour angles of 80°
or more. In this regard the study by [22] found that airflows and the temperature
distribution in a greenhouse are significantly altered by the consideration of
external incident radiation in the calculations when hour angles are between -60°
and +60° (being noon 0°).
The greenhouse system of automatic vent opening of the study case is
programmed to fully open all the windows of a span when indoor air
temperature overcomes a threshold value, usually set at 26°C. Numerical
simulations were performed to assess the efficacy of this solution in comparison
with the other possible vents opening configurations. The conditions recorded on
8 August 2014 since 19.30 to 20.30 were considered as the study case for
simulating the alternative scenarios and assessing their performance in summer
cooling. Indoor temperature and meteorological conditions recorded are
reported in Table 3. Wind direction was from SE. The resulting boundary andinitial conditions are reported in Table 4.
Simulations were performed to analyse the following scenarios of vent opening
configuration, summarized in Table 5. The scenario AO (All Open) was defined as
the real operating condition of the greenhouse span, i.e. with all the vents open
during the considered time interval; scenario WC (Windward Closed) was defined
as the condition of side vent open, roof windward vent closed and roof leeward
vent open; the scenario LC (Leeward Closed) was defined as the condition with
side vent open, windward roof vent open and leeward roof vent closed.
3. Results and Discussion
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3.1. Calibration
Both steady state and transient CFD simulations of the greenhouse were
performed under the environmental conditions surveyed during the calibration
trials.
Steady state analyses were developed to simulate the conditions surveyed in the
calibration trial after 420 s since wind opening: from that moment, both air
speed and air temperature showed a steady behaviour for about 140 s (Figure 4).
Thus air speed results of the steady state simulation were compared with the
corresponding average data measured over that period of 140 s (28 data).
Average air speed measured was 0.37 m/s, and simulated one in the same point
in steady state conditions resulted 0.36 m/s. Therefore the model proved to
represent real data with excellent soundness.
Transient analyses results regarding air speed and temperature were then
compared to measured data (Figure 5a).
As measured data of air speed showed high-frequency oscillations with low-
amplitude, data were processed according to Hilbert-Huang transformations: six
intrinsic mode functions were found for measured data and four for simulated
data. The intrinsic mode function with lowest amplitude of measured data was
omitted, thus obtaining the smoothed trend of Figure 5b: the corresponding
RMSE is 0.0559 m/s, which is very close to anemometer accuracy and therefore
indicates a good agreement between measured and simulated data.
A further quantification of the agreement between measured and simulated air
speed data was performed by developing an autoregressive model, as illustrated
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in the methodology section. The model was calibrated based on the first 100
time steps and was applied for an interval corresponding to half the calibration
period. RMSE between measured data and the autoregressive model was 0.0967
m/s; RMSE between measured data and CFD simulated data was 0.0974 m/s.
Therefore the CFD model proved as sound as the statistical predictive model
based on the measured data.
Air temperature measured in the point of anemometric survey was compared
with corresponding CFD simulated results (Figure 6). Resulting RPD is 1.6091,
thus qualifying fair soundness of the model.
Temperature trends monitored inside the greenhouse were compared with ones
simulated through transient analyses. Data referred to the centre of the span
width at 0.85 m and 2.05 m height showed good agreement (Figure 7) between
surveyed and computed values: in fact RPD on the average trend is 1.721 (fair
model).
3.2. Simulations
As mentioned above, once the model had been validated, it was applied to
analyse the effects of different vents opening configurations on temperature
reduction in hot climate conditions. The simulated temperatures for the scenario
AO were compared with temperature data recorded by the standard greenhouse
management system, with reference to the point where the thermometer of the
greenhouse facility is installed. The comparison provided a RMSE equal to 0.32°C,
thus allowing a further confirmation of the reliability of the model.
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Simulated temperature and air speed data were computed above the centre of
each workbench at heights of 1.00 m, 1.50 m, 2.00 m,2.50 m, thus obtaining a
grid with 4 rows and 3 columns. Such grid is representative of the microclimatic
condition of the indoor working and cultivation area. Mean temperature data
trends (over these twelve points) of the three opening configurations are
reported in Figure 8. These temperature trends resulted substantially similar
among rows and columns of the grid.
Scenario WC proved to lead to the greatest temperature reduction, although the
scenario with leeward vents closed caused a faster initial temperature drop. In LC
scenario, after a first period of about 20 min of circulation of outside air in the
cultivation area, the prevailing airflow becomes almost steady and consists in
direct passage from the side wall vent to the leeward roof vent, with poor
contribution to temperature reduction in the cultivation area. The reduction of
outside air speed characterizing the final part of the simulation period causes
only slight temperature decrease. Scenarios AO and WC show a very similar
temperature reduction path, but their effects become opposite when outside
wind speed becomes near to zero, after 35 min from the beginning of the
simulation. The scenario LC entails further cooling, while temperatures in WC
scenario do not decrease anymore. Therefore, in presence of outside wind, the
opening of wall vent on the windward side together with roof leeward vent
proves more effective than the opening of both roof vents, to achieve air
exchange in the cultivation area. In fact, as it is shown in Figures 9 and 10, AO
conditions cause the formation of a direct airflow through the roof openings,
while the scenario WC enhances indoor air circulation besides relevant air
exchange between outside and inside; the scenario LC is clearly unfavourable to
outbound airflows.
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Temperature profiles along the span width at 1.50 m above the ground for the
three scenarios, at 15 min, 30 min, and 60 min are reported in Figure 11.
Average temperature reductions over the whole simulation period are 3.37°C
with WC, 2.77°C with LC, and 2.54°C with AO. The performances of the three
scenarios were assessed on the basis of a linear scale where 0 indicates no
reduction of temperature over the simulation period, and 100% represents a
sudden reduction of indoor temperature to outdoor one since the first time step
(average reduction of 5.23°C). Resulting performances are 64% for WC, 53% for
LC, and 48% for AO. Such percentages give an indication of the ratio of heat
removed from inside the greenhouse in the three scenarios (Figure 12). While
the effects of AO and LC scenarios are very close to each other, the performance
of WC solution entails a significantly greater cooling.
The trends of mean air speed are reported in Figure 13, and for each scenario
they are substantially similar among rows and columns of the grid.
Indoor air speed in the three scenarios resulted always far below the
acceptability limit for proper working conditions in greenhouses [2]. The scenario
WC entails higher rates of air speed for almost the whole duration of the
simulation, except for a couple of minutes when outside wind decreases to zero:
in this condition, indoor air speed becomes equal for the three scenarios.
Mean modules of the air speed components perpendicular to each inlet edge
was then computed along the edges themselves at every time step. Results are
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reported in Figure 14 , where positive values represent inlet flows, negative
outlets.
Overall average airflows through the windows, referred to one meter of span
length (inlet section width is 0.70 m for all vents), are reported in Table 6.
It should be noted that airflow balance for each scenario is different from zero,
because of air exchanges with the other spans, considered through proper
definition of filtration rates in internal walls. Table 6 clearly shows that the
scenario LC hampers outbound airflows and entails overall air inlet (54.58
m3/h/m) less than half of AO (121.18 m3/h/m) and also smaller than WC. This
phenomenon appears to be the main reason of the negative performance of
such scenario in terms of temperature reduction.
4.
Conclusions
The study analysed the effects of roof and side vents opening configuration on
temperature reduction in a study case of glass greenhouse in hot climatic
conditions. A finite element CFD model of the greenhouse was developed and
calibrated through experimental monitoring of indoor and outdoor climatic
parameters. Numerical simulations of indoor conditions under real outdoor
climatic parameters recorded in a selected time interval of over one hour wereperformed. Various possible configurations of opening and closure of roof vents
were set, with a side wall vent always open. The analyses also accounted for the
outside air speed variable, including the case of absence of wind. The results
were assessed in terms of effectiveness in indoor cooling. Indoor temperature
distributions and trends were analysed, together with indoor air speed and
airflows. The best performances were obtained with windward vent closed,
which entailed 64% of the maximum heat removal achievable through natural
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ventilation. The other scenarios considered showed a performance index of
about 50%.
The automatic system of vents management of the greenhouse, which is a
commercial and widespread solution, is set to open all roof vents when
temperature overcomes a predefined threshold. This proved significantly less
effective than the scenario where windward roof vents were closed. Therefore,
the results suggest to enhance the vents control system by considering also wind
direction as input, so that the windward roof-vents closed scenario could be
adopted for the upwind span of the greenhouse. The results represents the basis
of further developments, which are ongoing and are expected to include the
contribution of solar radiation and analyse how the presence of plants can
modify the results obtained in this study. The outputs are expected to complete
the framework of programming strategies to enhance the automatic windows
control devices of greenhouses.
Acknowledgements
The authors wish to thank prof. Beatrice Pulvirenti PhD, Assistant Professor at
the Department of Industrial Engineering of the University of Bologna, for the
support provided in CFD modelling.
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Figure 1. Cross section of the greenhouse.
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Figure 2. Central portion of the FEM mesh with temperature contour of initial
condition for model calibration.
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Figure 3. Mesh convergence diagram in terms of air speed (v ) computed near
inlet opening and pressure difference ( p) between points at heights of 4 m and
1m in the middle of the greenhouse span.
0%
20%
40%
60%
80%
100%
120%
0 10000 20000 30000 40000 50000 60000
nr. of elements
Mesh convergence
v
p
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Figure 4. Trends of air speed and temperature in the observation period selected
for steady state CFD calibration analyses.
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a)
b)
Figure 5. a) Trends of air speed measured near the side wall vent and
corresponding data obtained though CFD transient simulation; b) comparison of
the same data processed through Hilbert-Huang transformation to remove the
high-frequency low-amplitude oscillations.
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Figure 6.Trend of air temperature in the air speed measurement point.
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Figure 7. Comparison between measured and simulated temperature at different
heights in the centre of the greenhouse. The continuous line indicates identity,
the dotted ones indicate differences of 0.5°C, the dash-dotted ones differences
of 1°C.
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Figure 8. Mean temperature trends in the cultivation area for the three opening
configuration considered.
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Figure 9 Temperature contour and air velocity vectors in transient analysis for
the three scenarios at 20 s.
AO WC LC
T(°C)
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Figure 10 Temperature contour and air velocity vectors in transient analysis for
the three scenarios at 2400 s.
AO WC LC
T(°C)
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Figure 11. Horizontal temperature profile at height of 1.50m for the three
scenario at time steps of 15 min, 30 min, 60 min.
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Figure 12. Performances of the three scenarios, in terms of heat removal form
the greenhouse.
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Figure 13 Mean air speed in the cultivation area.
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Figure 14. Air speed trends through vents in the various scenarios.
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Table 1. Material properties.
Material Density (kgm-3) Viscosity (Pa s) Conductivity
(Wm-1K-1)
Specific Heat
(J kg-1 K-1)
Air 1.20473 1.817 0.02563 1004
Glass 2700 - 0.78 840
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Table 2. Boundary conditions and initial conditions of CFD analyses for calibration
(10 February 2015).
Boundary conditions Initial conditions
Outdoor air
speed
Floor
temperature
Outdoor
temperature
Indoor temperature
0.1 m/s 22°C 9 °C
Height (m) Temperature
(°C)
< 0.4 18
0.4-0.8 20
0.8-1.4 23.5
1.4-1.8 22
1.8-2.6 23.7
2.6-3.6 28.7
> 3.6 30
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Table 3. Indoor and outdoor climatic parameters recorded during the reference
period considered for CFD simulations.
Date, time Indoor
T (°C)
Outdoor
T (°C)
Wind speed
(ms-1)
08/08/2014, 19.30 27.6 25.2 0.67
08/08/2014, 19.40 27.6 25.2 0.89
08/08/2014, 19.50 27.2 24.8 0.67
08/08/2014, 20.00 27.2 24.8 0.67
08/08/2014, 20.10 26.8 24.4 008/08/2014, 20.20 26.8 24.4 0.11
08/08/2014, 20.30 26.8 24.0 0
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Table 4. Boundary conditions and initial conditions of CFD analyses for
simulations (8 August 2014).
Boundary conditions Initial conditions
Outdoor air
speed
Floor
temperature
Outdoor
temperature
Indoor temperature
time
(s)
v
(m/s)
22°C 25.2°C
Height (m) Temperature
(°C)
0 0.67 < 0.4 22
600 0.89 0.4-0.8 25.5
1200 0.67 0.8-1.4 27.6
1800 0.67 1.4-1.8 29.5
2400 0 1.8-2.6 30.5
3000 0.11 2.6-3.6 31.5
3600 0 > 3.6 32.5
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Table 5. Combinations of vent opening and closure for the scenarios considered
in CFD simulations.
Scenario AO Scenario WC Scenario LC
Side vent Open Open Open
Roof Windward vent Open Closed Open
Roof Leeward vent Open Open Closed
Table 6. Airflows through greenhouse windows in the analysed scenarios.
Airflow (m3/h/m) Side wall vent Windward roof vent Leeward roof vent
Scenario AO 61.73 59.45 -77.50
Scenario WC 73.47 0 -50.62
Scenario LC 26.15 28.43 0
