=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper26
|storemode=property
|title=Precision Modelling of Distributed Greenhouse Climate
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper26.pdf
|volume=Vol-2030
|authors=Thomas Bartzanas,Dimitirs Fidaros,Catherine Baxevanou
|dblpUrl=https://dblp.org/rec/conf/haicta/BartzanasFB17
}}
==Precision Modelling of Distributed Greenhouse Climate==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper26.pdf
Precision Modelling of Distributed Greenhouse Climate
Thomas Bartzanas, Dimitris Fidaros, Catherine Baxevanou, Nikolaos Katsoulas
Institute of Bio-economy and Agri-technology (IBO), Centre for Research & Technology -
Hellas (CERTH), 6km Charilaou-Thermis St. Thessaloniki, Greece, e-mail:
bartzanas@ireteth.certh.gr
Abstract. A completely dynamic computational fluid dynamics model for
greenhouse climate was developed and analysed. The simulations were carried
out for an arc type tunnel greenhouse with a tomato crop representative of the
greenhouses used in the Mediterranean region. The CFD code Fluent was used
as a basis where the required external source code for the dynamic boundary
conditions (written in C) was embodied. In the present paper the distribution of
solar radiation during a whole day was incorporated in the crop model which is
represented by the equivalent porous medium approach to model dynamic
effects and a macro-model of heat and mass transfer to model the exchanges of
heat and water vapour between leaves and air. Time step for the unsteady
simulations was 1 sec. The results show the distribution of solar radiation and
the exchanges of heat and mass between crop and air in for a whole day period
and they compared with relative results from simulations carried out with
steady state conditions.
Keywords: Greenhouse, indoor climate, temperature, air velocity, CFD
1 Introduction
In Controlled Environment Agricultural (CEA) systems, indoor environmental
conditioning is a tool to improve the growth, development and quality of the crops
and animals allowing higher yields and better quality compared with outdoor.
From many researchers in the last decade have been recognized and proved that the
indoor environment in CEA systems is imperfectly mixed. Such imperfect mixing
leads to gradients in variables such as temperature, humidity, gas, dust and air
velocity, all of which affect the micro-environment around the animal or plant. Even
in well-designed agricultural buildings, large gradients of environmental parameters
exist (Bartzanas et al. 2013). The large differences, lead to higher energy and water
consumption, cause non-uniform production and quality, but also lead to problems
with pests and diseases. These interactions of environmental variables in production
system are complex involving a number of physical and chemical properties (most of
the times in different scales) of overall system and various system configurations and
thus, they are not easily, if not impossible to measure it accurately, but even
challenging to model them. There are several approaches and strategies for modelling
and controlling the spatial heterogeneity of indoor climate of CEA systems.
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Numerical modelling techniques such as Computational Fluid Dynamics (CFD) can
offer an effective way of accurately quantifying the influence of structures/machinery
design, environment parameters and weather conditions within a virtual environment.
Thus, the amount of physical experimentation can be reduced considerably, although,
as of yet, not eliminated. CFD is a simulation method that can efficiently estimate
both spatial and temporal field fluid pressure as well as other chemical and
environmental scalars, and the method has proven its effectiveness in system design
and optimization within the chemical, aerospace, and hydrodynamic industries
(Zhang et al., 2006). Today, CFD is the art and science of analyzing and simulating
systems in which a fluid flow is of central interest and in which heat and mass
transfer and chemical reaction may take place. CFD became an integral part of the
engineering design and analysis environment of many companies because of its
ability to predict the performance of new designs or processes prior to manufacturing
or implementation (Schaldach et al., 2000). The ubiquitous nature of fluids and their
influence on system performance has caused a widespread take-up of CFD also in
CEA (Norton et, al. 2007; Bartzanas et al., 2013). Distributed parameter modelling is
now a standard operation in the application to agricultural ventilated buildings.
2 Numerical Model
The flow inside the greenhouse is assumed to be 3D, steady-state, incompressible
and turbulent (Ferziger and Peric, 1996). The flow and transport phenomena for a
dependant variable Φ are described by the Reynolds Averaged Navier-Stokes
(RANS) equations. In momentum, equations are incorporated as source terms the
natural convection effects due the temperature difference between the ground and the
roof of the building and the plants’ resistance. The natural convection effects are
incorporated by the use of Boussinesq model, which offers faster convergence and
accuracy, assuming constant density in all equations except from the calculation of
buoyancy term in the momentum equations. The porous media are modelled by the
addition of a momentum source term to the fluid flow equation. The specific source
contribution is composed by a viscous loss term known as Darcy law and an inertial
loss term.
An ideal binary mixture approach is used for the prediction of the specie distribution
in order to predict the concentration of fresh air and the water vapor inside the
greenhouse, for the prediction of internal humidity. In particular, the model solves for
n-1 contaminants of the mixture and for the needs of the present simulation, the code
is running for the water vapour concentration while the remaining quantity is
corresponding to fresh air concentration. The binary diffusion coefficient based on
Fick’s law is a function of the local temperate and density according to Schirmer
relation while the general diffusion coefficient for the species convection-diffusion
equation is corrected by the addition of the mass diffusion caused by the turbulence,
taking under consideration the turbulent Schmidt number.
The energy conservation is modelled by the energy transport equation taking under
consideration the plants existence, modifying the thermal conductivity coefficient,
and by the radiation diffusion equation. The radiation is simulated by the Discrete
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Ordinates (DO) model using the approach proposed by Chui and Raithby (1993) and
its contribution is adjusted properly as source term in the energy transport equation.
The energy distribution inside the greenhouse and on the cover of it is calculated by a
RANS type equation. Additionally, the energy equation is also solved in solid region
in its reduced form, and its total calculation is derived in terms of enthalpy for better
accuracy. The DO model is used for the simulation of solar incident radiation on the
greenhouse cover and the internal radiation transfer. The effect of turbulence on the
flow is implemented via the high Re k-ε model (Launder and Spalding 1972). The
crop inside the greenhouse is simulated as a four rows of porous zone, modelling the
viscous and inertial resistances of the crop according to Forchheimer equation
(Miguel, 2008) while the plant’s transpiration is not taken into account in the present
study. The equations of mass conservation, momentum, species, turbulence, energy
and radiation are resolved numerically by finite volume method, using a grid
consisted of 2.9 millions of hexahedral cells and 22 blocks produced after grid
independence tests. The SIMPLEC algorithm is used for pressure-velocity coupling,
yielding an elliptic differential Poisson equation in order to formulate the mass
conservation equation.
Since the major problem of the Mediterranean greenhouses is the alleviation of
heat stress during the warm period of the year, the final model was tested for
assessing the climate distribution in greenhouse a) under natural ventilation and b)
under evaporative cooling
3 Results
Climate distribution in a greenhouse is of major concern since it greatly influences
crop growth and development. In particular, vertical temperature distribution is of
great importance, since temperature has a direct effect on the sink strength at the
individual plant parts. Homogeneity of solar radiation, air temperature difference and
air velocity was evaluated for the two different cases studied (natural ventilation and
evaporative cooling). The ratio of standard deviation of each parameter to its mean
value was used as an indicator for homogeneity. Lower values of this ratio indicate
more homogenous climate conditions. The use of evaporative cooling with the fans
(and mainly the absence of the uncontrolled outside wind speed) leads to a more
homogeneous climate conditions since little exchanges (both heat and mass) take
place. Concerning the distribution of air temperature, the ratio of σ(ΔΤ)/ ΔΤ was 0.
43 with natural ventilation and 0.32 with the evaporative cooling. Similar patterns
were observed for air humidity, solar radiation and air velocity (Table 1). Fig. 1
presents also an indicative figure of air temperature distribution (a) with natural
ventilation and (b) with the use of evaporative cooling.
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Table 1. Air temperature, air humidity, solar radiation and air velocity distribution inside the
greenhouse.
Climate variable Natural ventilation Evaporative cooling
Air temperature 0.43 0.32
Air humidity 0.38 0.29
Solar radiation 0.42 0.40
Air velocity 0.52 0.25
Natural ventilation Evaporative cooling
Fig. 1. Climate distribution inside the greenhouse.
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