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silo articulo cientifico
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ro
a
RS, Br
Computer Science Department, University of
a r t i c l e i n f o
Article history:
Received 21 August 2007
Received in revised form
Aeration is widely used in grain stores to cool the grain mass,
ous impurity in the mass, on the configuration and size of the
interstitial space in the mass, on the size and amount of bro-
ken grains, and on the depth of the grain.
The research carried out by Shedd (1953), Brooker (1961,
1969), Brooker et al. (1982), Bunn and Hukill (1963), Pierce and
et al. (2000), Navarro and Noyes (2001), Khatchatourian and
can no longer be assumed homogeneous. Non-homogeneity
can significantly alter the physical parameters involved in
the aeration process, such as air velocity and static pressure
drop. However, there is no research relating compaction of
the grain and the airflow pattern under these conditions.
* Corresponding author. Department of Physics, Statistics and Mathematics, Regional University of the Northwest, Rio Grande do Sul,R. Sao Francisco, 501, 98700-000 IJUI, RS, Brazil.
, [email protected] (M.O. Binelo).
Avai lab le a t www.sc iencedi rec t .com
vi
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8E-mail addresses: [email protected] (O.A. Khatchatourian)to avoid humidity migration, to temporarily conserve the hu-
midity of grains, to remove scents from the grainmass, and to
apply fumigation.
The resistance to the airflow in an aeration systemdepends
on the airflow parameters, on the characteristics of the prod-
uct surface (i.e. rugosity), on the form and size of any extrane-
Savicki (2004), and Khatchatourian and de Oliveira (2006) has
examined the influence of some of these parameters on air-
flow pattern in seeds storage. A recent review of the reported
mathematical models of airflow through grain mass was pre-
sented by Gayathri and Jayas (2007).
With increasing depth of grain storage, the mass of graingood performance. It was considered that themethod could be applied to optimise the perfor-
mance of existing grain stores and lower the engineering costs of new grain stores.
2008 IAgrE. Published by Elsevier Ltd. All rights reserved.
1. Introduction Thompson (1975), Haque et al. (1981), Ribeiro et al. (1983), Jayaset al. (1987), Maier et al. (1992), Weber (1995), Khatchatourian29 May 2008
Accepted 4 June 2008
Published online 21 August 20081537-5110/$ see front matter 2008 IAgrEdoi:10.1016/j.biosystemseng.2008.06.001Cruz Alta, R. Andrade Neves, 308, 98025-810 Cruz Alta, RS, Brazil
Amathematical model and software were developed for the three-dimensional simulation of
airflow throughhigh capacity grain storage bins by considering the non-uniformity of the seed
mass. To validate the proposedmodel, empirical relationships between air velocity and static
pressure drop were obtained for compacted layers of several storage depths for soya bean,
maize, rice and wheat mass. The software was written in ANSI C which is transferable toa variety of platforms. For the construction of 3D geometry and the generation of meshes
free-of-charge software was used. The solver software generated a system of linear algebraic
equations using the finite -elementmethod. Three iterative processeswere carried out: (1) cal-
culation of a local permeability coefficient, using the pressure distribution in the immediately
previous iteration step, (2) search for the systemdesignpoint, located in theperformancecurve
of the aerator fan, and (3) adaptation to refine the mesh. A local criterion to estimate the effi-
ciency of complex aeration system in storage bins was proposed. The simulations showedR. Sao Francisco, 501, 98700-000 IJUI,bazilaDepartment of Physics, Statistics and Mathematics, Regional University of the Northwest, Rio Grande do Sul,Research Paper: SEdStructures and Envi
Simulation of three-dimensionstorage bins
O.A. Khatchatouriana,b,*, M.O. Binelob
journa l homepage : www.e lse. Published by Elsevier Ltdnment
l airflow in grain
er .com/ loca te / i ssn /15375110. All rights reserved.
a product-dependent constant
b product-dependent constant
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8226To simulate the aeration of grain, with any type of air dis-
tribution systems, it is necessary to develop software to pre-
dict the distribution of the parameters, because obtaining
empirical data is very difficult and costly. Most research
on airflow simulation in grain stores is related to one-
dimensional, two-dimensional or axisymmetric cases; al-
C compaction function, dimensionless
c product-dependent constant
G product-dependent constant
H bed depth, m
i order number of corresponding inlet
k permeability coefficient, m3 kg1 sL bed depth, m
LX full length of a trajectory, m
M total number of experimental points
m grain mass, kg
n product-dependent constant; inlet number
n unit vector normal
P pressure, Pa
Pe air entrance or exit pressure in Pa;
Q global airflow rate, m3 s1 kg1
q local specific airflow rate, m3 s1 kg1
R product-dependent constant
S empirical coefficientNomenclature
A surface area, m2though the flow is usually three-dimensional. Even when
the grain mass distribution is two-dimensional or axisym-
metric, the airflow inlets do not satisfy these conditions.
Also, the aeration of large grain stores is frequently carried
out separately in different segments.
The principal objectives of the presentworkwere as follows:
(a) to create a mathematical model, algorithm, and software,
to calculate the static pressure, streamlines, and airflow
velocity distribution in three-dimensions under non-
homogeneous conditions;
(b) to determine the variation in compaction factor for several
depths of grain;
(c) to study the relationship between the air velocity and the
pressure gradient as a function of the compaction factor;
(d) to develop and incorporate into the software a criterion
for system performance based on estimating three-
dimensional air distribution in grain storage bins; and
(e) to carry out numerical simulations of real and hypothetical
grain storeswith aeration to detect areas of operational risk.
2. Mathematical model
The problem of incompressible viscous isothermal flow is de-
scribed by the system of equations of continuity [Eq. (1)] and of
NavierStokes [Eq. (2)]:div V 0; (1)
rDVDt
grad P mV2V; (2)
t time, s
U intermediate argument
V velocity vector, m s1
V velocity, m s1
Xi product-dependent constant (i 1, 2, 3)x coordinate located in floor plan, m
y coordinate along airflow axis, m
z coordinate located in floor plan, m
3 porosity factor, dimensionless
r density, kgm3
DP pressure drop, Pa
m dynamic viscosity, Pa s
Subscripts
a air
b bulk
e entrance, exit
g grain
i order number of corresponding inlet
k kernel
L local
X in point X(x, y, z)where V is the velocity vector in m s1; r is the density inkgm3; t is the time in s; P is the pressure in Pa; m is the dy-namic viscosity in Pa s.
The solutions of this system (usually reduced to the non-
dimensional form) depend on the effective Reynolds number
(calculated on apparent velocity taking into account the po-
rosity of the grain mass) and relate to the pressure and veloc-
ity distributions in each point of the integration domain for
each moment in the form of a vector-function V f (grad P),where the components u, v andw of velocity V and P are prim-
itive variables of the initial system.
However, experimental data show that the relationships
between the velocity and pressure gradient are different for
each type of grain, even for the same Reynolds number. This
is probably caused by the factors that cause airflow resistance
to vary, e.g. the geometrical form of the particles since grains
are not spherical they are distinct for different products; zones
within the grain mass exist where there is limited porosity
and there are differences in the rugosity of particle surface.
There are also other factors, e.g. grain layer compaction, var-
iation of humidity content, and the presence of impurities
that create differences between the measured values and
those calculated by solutions of the system described by Eqs.
(1) and (2). This implies that attempts to simulate the airflow
through the grain mass using the equations of continuity
and NavierStokes whilst contributing to our theoretical un-
derstanding of the problem are far from practical. The local
air velocity in an aerated grain storage can vary over a wide
can produce significant errors. For large grain storage bins,
especially with aeration in sections, there are regions with
the increased air velocity and regions where air velocity can
be practically zero. In these cases for calculations of the distri-
bution of pressure and velocity the variation in flow condi-
tions has a significant influence.
It is difficult to describe precisely airflow by means of these
relationships (which depend only on two constants) for all flow
regimes (laminar, transition and turbulent flows). If coefficients
a and b are chosen to accurately describe the transition regime,
the influence of velocity in limiting situations (laminar or
turbulent regime) will be too strong. If limiting regimes are
well described, then, the relationship for the transition regime
is insufficiently exact. Moreover, these relationships when
applied to two-dimensional and three-dimensional cases are
difficult to analyse.
Finding the derivative dln V=dlnjdP=Lj from both Eqs. (7)and (8) respectively:
dln V 1 bVln1 bV ; (9)
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8 227range depending on the cross-sectional area and on the design
of the aerator. Grain stores can have regions of laminar, turbu-
lent and transition flows. This complicates the creation of
mathematical models based on the use of the NavierStokes
equation.
For small velocities corresponding to laminar flow, a propor-
tional relationship exists between the air pressure drop and the
air velocity (i.e. the HagenPoiseuille or BlakeKozeny equation):
dP=dyfV0V kdP=dy; (3)where k is coefficient of proportionality; V jVj is the absolutevalue of velocity, ms1.
Applying logarithms and taking the derivative produces
dln VdlnjgradPj 1: (4)
For the turbulent regime that corresponds to the larger
values of air velocity, the pressure drop is proportional to
the velocity squared (i.e. the BurkePlummer equation) where
dP=dyfV20V kjdP=dyj1=2: (5)Thus, for turbulent flow
dln VdlnjdP=dyj 0:5: (6)
For the transition flows the relationship between the air
pressure drop and air velocity lies between linear and square
law dependency.
There are a large number of nonlinear motion equations in
the literature to describe airflow in porous media (Scheideg-
ger, 1960; Bear, 1988). In most of these equations the gradient
of pressure is expressed as function of velocity by second-order
parabola without a free term, i.e. as the sum of dependences
for the laminar and turbulent regimes.
The fullest recommendations for estimating static pres-
sure requirements are given by Navarro and Noyes (2001)
and the basic results of works for pressure-drop modelling
in stored grain masses are complied in ASAE (2000). In both
these studies the equation of Hukill and Ives (1955) has been
adapted to calculate the pressure gradient:
DPL aV
2
ln1 bV; (7)
where a and b are constants used to describe a particular
grain. However, in addition to Eq. (7), Navarro and Noyes
(2001) also recommend the use of the following equation:
DPL RV SV2; (8)
where R and S are product-dependent constants.
For example, this equation was used for simulation of air-
flow through packed bed of grain in works of Haque et al.
(1981), Haque et al. (1982), and Hunter (1983).
Eqs. (7) and (8), i.e. two-parametermodels, present good re-
sults to simulate static pressure drop in silos when the veloc-
ity is similar in all points of a silo and if this velocity pertains
values where constants a and b (or R and S ) have been accu-
rately determined for a particular grain. When calculating
the distribution of air in large grain storage bins with signifi-cant variations in cross-sectional area, when regions of lami-
nar, turbulent and transition flow exist, these relationshipsdlnjdP=Lj 21 bVln1 bV bV
dln VdlnjdP=Lj
1 SRV1 2SR V
: (10)
Both these expressions satisfy to limiting conditions for
laminar and turbulent flow conditions, i.e.
limV/0
dln V
dlnjdP=Lj 1 and lim
V/N
dln V
dlnjdP=Lj 0:5: (11)
This means that Eqs. (7) and (8) are capable of describing the
airflow through grainmass for all flow regimes (laminar, tran-
sition and turbulent flows). Also Fig. 1, where data from Shedd
(1953) and this study are presented, indicates that the data
predicted by Eq. (9) for recommended a and b values show
a significant divergence from experimental data and excessive
dominance by the transient regime. Estimation of Reynolds
number shows that deviation from Darcys law occurs after
Re 10, and the transient regime occurs where 10< Re< 60.
-2 0 2 4 6 8 100.5
0.6
0.7
0.8
0.9
1.0
Turbulentcondition
Transientregime
Laminar-flowcondition
d(ln
V)/d
(ln
|g
radP
|)
ln|gradP|, Pa m-1
Fig. 1 Observed and predicted variation of derivative
dln V=dlnjgradPj[fgradP for airflow through soya beanmass (blue points and curves) and wheat mass (black pointsand curves):,,6, Shedds (1953) data ;-,:, authors data;
predicted by Eq. (12); - - -, predicted by Eq. (9).
and (20), describe the steady-state pressure and velocity distri-
butions in a cross-section of an aerated grain storage.
equations and tool for results three-dimensional presentation
and analysis. Since commercial tools can be costly, free-of-
charge software was used when possible.
i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8Where Re> 60 turbulent flow occurs. These results are in
agreement with the data of Wright (1968) and Bear (1988).
Therefore the use of Eq. (7) is limited, since it is very diffi-
cult to simultaneously achieve good results for both Eqs. (7)
and (9), using only two constants. The same problem concerns
Eqs. (8) and (10). Also, Eqs. (9) and (10) depend on only one con-
stant b and S/R, respectively. To improve accuracy different
values of factors a and b are usually adopted for different in-
tervals and this can be very inconvenient.
Khatchatourian and Savicki (2004) proposed the formula to
describe the variation of the derivative d(ln V)/d(ln(jdP/dyj)) forall the three flow conditions corresponding to the laminar,
turbulent and transition flows:
dln Vdlnjgrad Pj
34 arctanU
2p; (12)
where U(P) a ln(jgrad Pj) b is an intermediate argument;a> 0 and b are constants.
Evidently, when jgrad Pj/ 0, U/N, limu/N3=4
arctanU=2p 1, which corresponds to the laminar flow; andwhen jgrad Pj/N, U/N, lim
u/N3=4 arctanU=2p 0:5,
which corresponds to the turbulent flow, i.e. Eq. (12) satisfies to
limiting conditions Eq. (11).
Fig. 1 shows reasonable agreement between the curve cal-
culated by the Eq. (12) and the experimental data.
Integrating Eq. (12) in relation to the logarithm of the pres-
sure gradient gives the expression for the velocity:
ln V ln1 U2 2U arctanUp 3U4a c; (13)where c is a constant of integration.
As will be shown, this equation, depending on three con-
stants (a, b and c), describes well the experimental data in all
regions. In addition, unlike Eq. (7), explicit dependence of ve-
locity on a pressure gradient in Eq. (13) essentially simplifies
its use together with the equation of continuity for problems
formulated in two-dimensional and three-dimensional.
Finally, the mathematical model of the airflow in the par-
ticular media for the three-dimensional case consists of a sys-
tem of two equations:
div V 0; (14)
V grad Pjgrad Pj exp
ln1 U2 2U arctanUp
3U4a c: (15)The scalar equation (14) is the continuity equation for incom-
pressible fluid. The vector equation (15), which has replaced
the NavierStokes equation, shows that the velocity vector
and pressure gradient are collinear in all points of the airflow
domain and that the ratio of the absolute values of these vec-
tors is a function of the pressure gradient. Expressing the co-
efficient of proportionality k by
kexpln1U22UarctanUp3U4acjgradPj;(16)
and using Eq. (15), the velocity components u, v and w for the
three-dimensional case can be expressed in the form
b i o s y s t em s e n g i n e e r228ukvPvx
; vkvPvy
; wkvPvz; (17)3.1. Geometry construction
The geometry of the system can be constructed in any system
CAD, CAE, or any three-dimensionalmodelling software pack-
age that can export data to a standard format. In this work
Blender3D was used (http://www.blender.org). This software
is available at no-cost under General Public License (GPL). It
is three-dimensional modelling software aimed at artistic
works, but it proved to be very efficient for constructing the
three-dimensional geometry of the storage bins. A user-inter-
face was developed in Lazarus (http://sourceforge.net/pro-
jects/lazarus/) to create geometry choosing basic storage bin
dimensions.
The storage bin geometry data were exported to smash file
format, which is a format that can be read by Tetgen (http://
tetgen.berlios.de/). A Perl script (http://www.perl.org/), used
for exporting data, wasmodified to include exporting facema-
terials. Different face materials were used in order to recog-3. Software description and development
The nonlinear partial differential equation for pressure Eq.
(18) was solved by the finite-element method (Segerlind,
1976) using an iterative process to calculate the permeability
coefficient k using Eq. (16) in each point of the integration do-
main and using the pressure distribution from the immedi-
ately previous iteration step.
The software, developed in ANSI C, consisted of toolsfor geometry construction, mesh generation, generation of
system matrix, solver of obtained system of linear algebraicwhere the y coordinate in m corresponds to vertical direction,
the x and z coordinates are located in the perforated floor plan.
Substituting Eq. (17) in Eq. (14), the nonlinear partial differ-
ential equation is obtained:
v
vx
k vP
vx
vvy
k vP
vy
vvz
k vP
vz
0: (18)
The boundary conditions for the problem considered have
the form:
P Pe Dirichlet condition for air entrance and exit; (19)
n grad P 0 Neumann condition on the walls and floor
of the silo; 20
where Pe is air entrance or exit pressure in Pa; and n is unit
vector normal to the wall or floor surface.
Eqs. (16)(18) along with the boundary conditions, Eqs. (19)nise the surfaces with different bounding conditions, such
as inlets and outlets.
4. Validation of the mathematical model fornon-homogeneous conditions in a grain mass
To validate the proposed mathematical model, the empirical
relationships between air velocity and static pressure drop
were obtained for compacted layers with several grain storage
depths. The coefficients a, b and c presented in themathemat-
ical model were obtained experimentally for soya bean,
maize, rice and wheat grains. In large storage bins, due to
compaction, grain mass is a non-homogeneous medium and
the permeability coefficient varies as a function of the grain
layer depth as well as pressure gradient. Therefore the influ-
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8 2293.2. Mesh generation
For mesh generation Tetgen, available under a GPL license
was used. It generates quality tetrahedral meshes using
Delaunay algorithms. Firstly, a coarse mesh was generated.
To refine the obtainedmesh, a qualitymesh filewas generated
by the solver, then, Tetgen was used to refine the mesh
according to the parameters indicated in this file. The best re-
sults were obtained by dynamic adaptive refinement of the
mesh, based on a tetrahedron size selection in inverse propor-
tion to the tetrahedron pressure gradient. Each tetrahedron
not satisfying the user specified ratio was recursively decom-
posed into eight new tetrahedral elements according to the
method shown in Liu and Joe (1996).
3.3. Problem solving and representation
The developed code is cross-platform and can be compiled in
any ANSI C compatible compiler. The input files to thesolver software are the output files from Tetgen which de-
scribes nodes, faces and tetrahedral elements, and generates
a file describing the boundary conditions and precision re-
quirements. Firstly, the solver software generates the local
matrix for each tetrahedron applying the finite-element
method. Using the local matrix information, the global system
matrix was generated. Since the system order was large and
thematrix was very sparse, a special class was created to han-
dle the matrix, optimising memory and also optimising the
time to access the elements. Instead of using standard sparse
matrix classes, the in-house programming of the special class
enables full advantage to be taken of the system peculiarities,
optimising both memory usage and processor time. The
successive over-relaxation (SOR) method (Hageman and
Young, 1981) was used for resolving the system of linear alge-
braic equations. The developed solver was shown to have
good performance.
The software executes three iterative processes: (1) it cal-
culates the permeability coefficient in each point of the inte-
gration domain, using the pressure distribution in the
immediately previous iteration step, (2) it searches the system
design point, located in the performance curve of the aerator
fan, and (3) it adaptively refines themesh according to the tet-
rahedron size per pressure gradient ratio.
After the system is solved, an output file is generated in
VTK (Visualization toolkit, http://www.vtk.org/) format. This
file includes the nodes and tetrahedral elements. For each
node the value of pressure and for each tetrahedron the velocity
vector is exported. Paraview software (http://www.paraview.
org/), which is open source and available free-of-charge, was
used for to visualise the results.
The velocity vector for the used scheme of a finite-
element method is constant inside the simplex element
(tetrahedron). Using theory of consistent conjugate approxi-
mation (Oden and Reddy, 1973) velocities in all vertices of
the tetrahedrons were calculated, i.e. a continuous vector
field was obtained. Further, for each vertex, the full airflow
trajectory length (from inlet up to outlet) was calculated.
The received values were then used to calculate the local cri-terion introduced in this work to estimate the ventilation
system performance.ence of the grain mass compaction factor on the permeability
coefficient was investigated.
4.1. Experimental equipment
To simulate the aerated grain storage characteristics, the
equipment, described by Khatchatourian and Savicki (2004),
was used to experimentally determine the grain mass com-
paction factor caused by the weight of layers above. The grain
mass porosity varied as a function of the layer depth. The in-
fluence of compaction on the relationship between the airflow
velocity and the static pressure drop was analysed.
Fig. 2 shows the experimental equipment which consisted
of a centrifugal fan, an orifice-plate and small silo composed
of a polyvinyl chloride tube (inside diameter of 0.2 m and
height of 1 m) or a steel tube (inside diameter of 0.11 m and
height of 1 m). Tomodel the conditions at the bottomof a grain
store, a compacting device was developed with a lever, which
made it possible to apply enough force to simulate the depth
up to 50 m. In the tests, soya beans, maize, and wheat had
a moisture content of 1213% and rice had a moisture content
of 10%. Impurities were less than 2%, as determined by the
Laboratory of Seeds Analysis, Department of Agrarian Studies,
Regional University of the Northwest, Rio Grande do Sul UNI-
JUI, Brazil.
4.2. Experimental results
The experimental results, presented in Fig. 3, show the rela-
tionship between airflow velocity and static pressure drop in
the soya beans, shelled maize, rice and wheat mass. Table 1
shows the values of empirical model coefficients a, b and c,Fig. 2 Sketch of the experimental equipment.
-2
-1
0
(dP
/d
y), P
a m
-1
0.2
0.3
0.4
0.5
Velo
city, m
s
-1
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8230obtained by minimising the residual error between experi-
mental and simulated data. The simulations based on these
coefficients satisfactorily described the experimental data
(Fig. 3).
Experimental data in Fig. 4 show the significant influence
of the storage layer depth on the aerodynamic resistance of
the grain mass over the studied depths (from 1 m up to 50 m).
Fig. 5 presents a reduction of themeasured porosity factor 3
with storage depths for soya bean, maize and rice, where 3 is
the ratio of the void volume to the total bed volume. Experi-
mental porosity valuesweremeasured using a specially devel-
oped and adjusted pycnometer. The relationship between the
reduction in porosity and layer depth H can be presented as
3 4 5 6 7 8 9-4
-3
ln
lnV, m s-1
Fig. 3 Relationship between air velocity (V) in m sL1 and
air pressure drop (dP/dy) in PamL1; ,, soya bean,
coefficient of correlation R2[ 0.9954; 6, shelled maize
R2[ 0.9982; B, rice, R2[ 0.9934;>, wheat, R2[ 0.9972; d,
predicted by Eq. (13).3
30 eSH50
n
; (21)
where 30 is the porosity factor for H 1 m dimensionless. Theempirical coefficients S and n, which were obtained by least-
squares method, are presented in Table 2.
The analysis of the measurements of the porosity factor 3
for various bed depths indicated that the effective velocity in-
crease due to reductions in the porosity factor in the deepest
layers was not sufficient to explain and calculate the pressure
losses under these conditions. The values in Fig. 4, calculated
by using a porosity reduction for H 50 m, are significantlydifferent from the corresponding experimental points.
Table 1 The empirical coefficients a, b and c with 95% confidecoefficient of determination (R2) and root mean squared error (
a b
Soya bean 0.82 0.12 3.57 0.66 Maize 0.61 0.07 2.92 0.39 Rice 0.51 0.13 3.08 0.82 Wheat 0.86 0.15 5.49 0.98 However, the greater porosity did not guarantee smaller re-
sistance to airflow in the grain mass. For example, the rice in
the husk (or paddy) had a resistance greater than themaize al-
though the rice porosity was greater. It is possible that free
volumes of air between husk and the grain increased porosity
but did not increase cross-sectional area for airflow.
It must be concluded that, besides the global non-unifor-
mity defined by the alteration of the mean porosity factor
with the depth variation, there is local non-uniformity caused
by the seed form and that this does not significantly alter the
porosity factor value of themedium. Probably, the compaction
of non-spherical seeds creates local dense regions through
0 500 1000 1500 2000 2500 30000.0
0.1
dP/dy, Pa m-1
Fig. 4 Influence of bed depth (H ) on the air velocity (V) in
m sL1 as function of air pressure drop (dP/dy) in PamL1
(one-dimensional storage), shelled maize: -, H[ 1 m; ,,
H[ 10 m; C, H[ 20 m; B, H[ 30 m; :, H[ 40 m; 6,
H[ 50 m;d predicted; $, predicted by porosity reduction
for H[ 50 m; - - -, predicted by Eq. (25).which airflow is hindered.
The experimental data presented in Fig. 6 show that for the
studied velocity and depth variation intervals, the relative
pressure gradient increment C (jgrad PHj jgrad P0j)/jgrad P0jcan be considered as being independent of air velocity and de-
pends only on the storage layer depth H, where H is a distance
between the upper seed surface (free surface) and the layer
under consideration. This hypothesis was confirmed by
multi-factorial analysis of variance and by a nonparametric
association test of the Spearman rank order correlation
(Table 3).
The function C C(H ) relates to the initial pressure gradi-ent jgrad P0j, where P0 corresponds to grain depth H 1 m,and the pressure gradient jgrad PHj for considered depth H at
nce bounds for different seeds, sum squared error (SSE),standard error) for Eq. (13)
c SSE R2 RSME
2.77 0.12 0.5013 0.9954 0.04802.75 0.08 0.1304 0.9982 0.02892.23 0.13 0.3526 0.9934 0.05252.18 0.06 0.1296 0.9972 0.0348
effect of bed depth on resistance to airflow of grain for all
range of airflow with permanent values X1, X2 and X3 and
0.95
0.96
0.97
0.98
0.99
1.00
0.1 0.2 0.3 0.4 0.50.0
0.1
0.2
0.3
0.4
0.5
C=
(g
radP
- g
radP
0)/g
radP
0
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8 231same velocity. This function, designated as the compaction
function in this work, was presented in the form
CH G1 eaH; (22)where G and a are empirical product-dependent constants
presented in Table 4 for soya bean, maize and rice.
These constants were obtained by minimising
mina;G
XMi1
G1 eaHi grad Pi grad P0
grad P0
2; (23)
where M is the total number of experimental points for se-
lected grain type.
Fig. 7 shows the variation of compaction function with
layer depth for soya bean and maize. The compaction func-
tion allowed the influence of the depth H to be included in
0 10 20 30 40 500.92
0.93
0.94
Bed Depth, m
Fig. 5 Porosity reduction with bed depth; -, soya bean;
:, maize; d, predicted; - -, 95% confidence bounds for
prediction.the model through the intermediate argument U, substituting
the pressure gradient without compaction jgrad P0j for the ex-pression jgrad PH /(1 C )j:U a lnjgrad PH =1 Cj b: (24)As a result, Eqs. (13) and (22) and with the intermediate argu-
ment in Eq. (24) relate the air velocity for the storage layer lo-
cated in the depth H, and the necessary pressure gradient.
Fig. 4 shows close agreement between observed and predicted
data.
To take account of the grain bulk density (and in implicit
form the bed depth) the ASAE Standards 2000 recommends
using the equation obtained by Bern and Charity (1975):
Table 2 Porosity factor 30 for H[ 1 m and empiricalcoefficients S and n of Eq. (21) for different seeds
Seed type 30 S n Coefficient ofdetermination (R2)
Soya bean 0.43 0.0680 0.5261 0.9985
Maize 0.44 0.0736 0.4683 0.9997
Rice 0.61 0.0664 0.5134 0.9989DPL X1 X2
rb
rk
2V
1 rb
rk
3 X3rbrk
V2
1 rb
rk
3; (25)
where DP is pressure drop, Pa; L is bed depth, m; rb is product
bulk density, kgm3; rk is product kernel density, kgm3; X1,
X2 and X3 are constants.
Eq. (25), based on the equation from Ergun (1952), repre-
sents a three-parameter model and describes the relationship
between airflow and pressure drop better than Eqs. (7) and (8).
Unfortunately, as Fig. 4 shows, it is impossible to describe the
Velocity, m s-1
Fig. 6 Variation of the compaction function
C[(jgradPHjL jgradP0j)/jgradP0j for shelled maize with beddepth H at various air velocities V in m;,, H[ 10 m; C,
H[ 20 m; B, H[ 30 m; :, H[ 40 m; 6, H[ 50 m;
d predicted.Eq. (25). The empirical coefficients X1, X2 and X3 for this simu-
lation were obtained by minimisation of the residual error be-
tween observed and simulated data, using Eq. (21) and the
relationship
rb=rk 1 3: (26)Navarro and Noyes (2001) recommended calculating the aver-
age value of the grain bulk density during filling a silo by
Table 3 Influence of bed depth H and air velocity V oncompaction factor C
Variable F-value Probability> F R2
Soya bean
Depth, m 76.6 0.002 0.958
Velocity, m s1 0.54 0.855 0.035
Shelled corn
Depth, m 221.6 0.001 0.954
Velocity, m s1 5.05 0.103 0.031
Rice
Depth, m 147.2 0.001 0.958
Velocity, m s1 4.03 0.138 0.045
means of known weight of loaded grain and the calculated
volume which will occupy this grain in a silo. Representing
the depth-static pressure of the grain using a nomograph is
another recommended way of estimating static pressure re-
quirements. The basic values for loose grain were increased
as follows: 30% for wheat, 34% formaize, and 41% for sorghum
and soya beans.
For silo aeration systems this procedure gives admissible
results. For large grain storage bins, for bins with significant
variation in cross-sectional area, and for bins using aeration
in parts, such estimations can be unacceptable.
was made for case 1 (air inlet ducts installed in the base of
the storage system). Although in this case the storage bin
has two axes of symmetry and it is possible to consider only
Table 4 The empirical coefficients G and a of compactionfunction C, Eq. (22), for different seeds
Seed type G a Coefficient ofdetermination (R2)
Standard error(RSME)
Soya bean 0.6865 0.0345 0.9943 0.0111Maize 0.8514 0.0155 0.9858 0.0176Rice 0.7652 0.0171 0.9872 0.0155
Fig. 8 Outline sketch of simulated store bin.
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8232In our view, estimating the influence of bed depth on pres-
sure increase by means of Eq. (22) is preferable to using only
one formula. Also, calculations and experiment have shown
that this dependence is very useful for inverse problem solu-
tion, when for known integral parameters of airflow (e.g. air
consumption and total pressure head) define the characteris-
tics of grain mass which influence aerodynamic resistance.
Coefficient G in Eq. (22) can be single variable parameter since
the sensitivity of coefficient a in Eq. (22) is insignificant in
comparison with sensitivity G.
To simulate the storage bins of complex layouts, the soft-
ware was operated using an iterative process which deter-
mines the equilibrium between the fan output and the
resistance of the aeration system to airflow, i.e. the operating
point of the aerator fan. The software then calculates: (1) the
pressure needed to get the required airflow rate (estimating
static pressure requirements); (2) the airflow rate, knowing
0.2
0.3
0.4
0.5
0.6
C10 20 30 40 500.0
0.1
Layer Depth, m
Fig. 7 Variation of the compaction function
C[ (jgradPHjL jgradP0j)/jgradP0j with bed depth (H ) in mfor soya bean and shelled maize: C, soya bean, observed
data; 6, shelled maize, observed data; , 95% confidence
bounds for prediction; - -, non-simultaneous bounds for
observation; , predicted.14 th of total storage, the simulation was carried out for com-
plete domain, because generally symmetry conditions do not
exist.the initial pressure; and (3) the pressure and airflow rate in
an iterative process for the chosen fan and electric motor (by
estimating system design point).
5. Numerical simulations
Fig. 8 shows the structural layout of real V-form floor storage
bin, used in the state of Rio Grande do Sul, Brazil. The storage
bin has a maximum width of 30 m and length of 95 m. Three
air inlet systems were analysed: (1) a central inlet system; (2)
a systemwith central and upper lateral inlets; and (3) a system
with central, lower lateral and upper lateral inlets. The aera-
tion simulations in storage bins, for different layouts, were
generated using the global airflow rate of Q 9 m3 h1 t1(2.5 106 m3 s1 kg1), which is the most commonly recom-mended value for aerated grain storage.
Firstly, airflow simulation in the V-form floor storage binFig. 9 Surface wireframe of the tetrahedral mesh.
Fig. 9 shows part of the computational mesh used. The grid
had a higher density in regions where the pressure gradient
was greater. For the layout under consideration the number
of tetrahedrons was approximately 500,000.
Isobaric surfaces for storage bin section with central, lower
lateral and upper lateral inlet systems are shown in Fig. 10. It
can be seen that airflow in lower storage section was three-
dimensional in character. In the upper section of the storage,
the character of the airflow approached the two-dimensional
case.
The simulation results of three aeration systems under
consideration are shown in Fig. 11. Analysis of the pressure
distribution (left column) showed that the installation of
Fig. 10 Isobaric surfaces in storage bin section with
central, lower lateral and upper lateral inlet systems.Fig. 12 Schematic model for determination of the local
specific airflow rate.
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8 233Fig. 11 Comparison of three simulated aeration systems: distr
(right column).ibution of pressure (left column) and risk regions
between the total airflow rate and the total productmass. This
Fig. 13 Visualisation of regions with inadequate ventilation (qL< 4.5) for three air inlet systems: (1) central inlet system; (2)central and upper lateral inlet systems; and (3) central, lower lateral and upper lateral inlet systems; Q[ 9 m3 tL1 hL1.
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8234criterion is suitable for simple silo designs with constant
cross-sectional area, when the air velocity is uniform through-
out the storage. If variations in the cross-sectional area are
significant, or the aeration distribution system is complex
(e.g. case 3), this criterion is not suitable.
To evaluate aeration efficiency for storage bins with vari-
able cross-sectional area and with complex air distribution
system, a local specific airflow rate is proposed. For simplelateral ducts essentially equalised the airflowwhen compared
with the same storage bin without lateral ducts and reduced
the initial pressure head. To analyse the distribution of pa-
rameters, the software was used to show the storage sections,
which satisfied certain conditions. For example, the frame-
works in Fig. 11 (right column) show only cells where velocity
is 18) for three air inlet systems: (1) centralcentral, lower lateral and upper lateral inlet systems;
Fig. 15 Distribution of local specific airflow rate, Q[ 9 m3 hL1 tL1 (2.53 10L6 m3 sL1 kgL1): (a) central inlet and (b) lower
lateral inlet.
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8 235A local criterion multiplied by aeration time has additive
properties. This allows the quality of aeration to be calcu-
lated for all parts of the storage bin even if the ventilation
is carried out separately at each of the inlets over different
periods of time.
5.2. Numerical simulation results
To visualise risk domains in the grain storage bin, the distribu-
tion of local specific airflow rates was studied (Figs. 1319).Fig. 13 shows the visualisation of domains with inadequate
ventilation (qL< 4.5) for three air inlet systems: (1) a central
Fig. 16 Distribution of local specific airflow rate, Q[ 9 m3 hL1
central, lower lateral and upper lateral inlets with identical initinlet; (2) central and upper lateral inlets; and (3) central, lower
lateral and upper lateral inlets. As simulations show, the sys-
temwith central, lower lateral and upper lateral inlets consid-
erably improved the conditions of storage in regions close to
walls when compared with other inlet systems. For all cases
considered there was an area of risk in the uppermost part
of grain mass.
The regionswith the raised intensity of ventilation (qL> 18)
are shown in Fig. 14. The results obtained show that the sec-
ond system (central and upper lateral inlets) has a smaller vol-
ume with excessive intensity of ventilation in comparisonwith others, i.e. has improved efficiency.
tL1 (2.53 10L6 m3 sL1 kgL1): (a) upper lateral inlet and (b)
ial pressures.
Fig. 17 Distribution of local specific airflow rate, Q[ 9 m3hL1 tL1 (2.53 10L6 m3 sL1 kgL1): (a) central and lower lateral
inlets with different initial pressures and (b) central, lower lateral and upper lateral inlets with different initial pressures.
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8236Additional more detailed comparative analyses of the effi-
ciency of different aeration systems were made for the same
grain storage bin with the same global specific airflow rate
Q 9 m3 h1 t1 (2.5 106 m3 s1 kg1). The number of inputs(from one up to three), their position (upper lateral, lower lat-
eral and central inlets), and ratio of pressure between various
inputs were varied. Using the additive property of the local
specific airflow rate, estimations of ventilation system efficiency
were carried out separately using each of inlets during the
different periods of time. Relationships between the durationFig. 18 Distribution of resultant local specific airflow rate with
(2.53 10L6 m3 sL1 kgL1): (a) upper lateral, lower lateral and cen
lateral, lower lateral and central inlets with different applicatioof ventilation time through each inlet and the airflow rates
were chosen so that the global specific airflow rate Q was equal
to 9m3h1 t1 (2.5 106 m3 s1 kg1). The simulation resultsfor the distribution of local specific airflow rates in a plane of
symmetry in the grain storage bin are presented in Figs. 1518.
As the simulation results presented in Figs. 15 and 16(a)
show, if only one airflow inlet is used, there is always a large
area with superfluous ventilation. Since the airflow tends to
leave the grainmass through the line of least resistance, mov-
ing the airflow inlet from the lower position to the upperseparated functioning inlets, Q[ 9 m3hL1 tL1
tral inlets with equal application times (1:1:1) and (b) upper
n times (1:2:2).
of
f si
:2)
b i o s y s t em s e n g i n e e r i n g 1 0 1 ( 2 0 0 8 ) 2 2 5 2 3 8 237position provokes a pressure reduction and a deterioration in
ventilation uniformity. Therefore, there is area with excessive
ventilation close to the upper lateral inlet if all of three inlets
operate together with identical pressure (Fig. 16(b)).
By selecting the appropriate pressures ratio for the inlets it
is possible to considerably improve the system of air distribu-
tion in the storage bin. This is demonstrated in Fig. 17(a) for
two inlets and in Fig. 17(b) for three inlets.
In high capacity storage, the grain ventilation is usually
carried out stage by stage, serially using air inlets located in
different storage sections. Under these conditions the advan-
tage of using of local specific airflow rates for ventilation effi-
ciency estimation is especially great. In these cases the
resultant local specific airflow rate qL in each point of storage
bin can be calculate by the expression
qL Pn
i1 tiqiPni1 ti
; (29)
where qi is the local specific airflow rate corresponding to ven-
tilation with only one inlet (order number i); ti is ventilation
Fig. 19 Visualisation of domains with the lowered intensity
ventilation (qL> 18; left); qL was obtained by superposition olateral and central inlets with different application times (1:2timewith only one inlet (i); n is total number of inlets; i is order
number of corresponding inlet.
For example, Fig. 18 shows the distribution of resultant lo-
cal specific airflow rates with separated operation of the upper
lateral, lower lateral or central inlets. In case (a) the applica-
tion time is the same for each inlet, and the resultant local
specific airflow rate at each point of the storage bin can be cal-
culate by
qL 13q1 13q2 13 q3; (30)
where q1, q2 and q3 are local specific airflow rates correspond-
ing to upper lateral, lower lateral or central inlets.
The simulations presented in Figs. 16(b) and 18(a) indicate
the significant advantage of ventilation carried out in turn by
each of inlets in comparison with the simultaneous use of all
inlets at equal pressures. This improvement is caused because
the capacity for air to penetrate to all zones under the domi-
nant influence of each inlet results in amore uniformdistribu-
tion of qL. By varying the duration of aeration for each inlet, itis possible to find a optimumdistribution of qL for a given stor-
age bin design.
For example, Fig. 18(b) shows the distribution of qL in the
grain storage bin with alternate use of upper lateral, lower lat-
eral or central inlets for durations of aeration varying as
t1:t2:t3 1:2:2. As results showed, this distribution had thefewest regions with insufficient or excessive aeration. These
regions are shown in Fig. 19 for whole grain storage bin.
Unfortunately, where only one inlet was used there was an
inevitable increase in head pressure.
6. Conclusions
Amathematical model of three-dimensional airflow in an aer-
ated grain storage system was developed for non-uniform
conditions of the seed mass. Experiments were conducted to
obtain the relationship between air velocity and pressure gra-
dient and the values of the porosity factors for different seed
types and different storage layer depths. A local criterion
was proposed to estimate the efficiency of complex aeration
ventilation (qL< 4.5; right) and with excessive intensity ofmulations for separated functioning of upper lateral, lower
; Q[ 9 m3hL1 tL1 (2.53 10L6 m3 sL1 kgL1).system in grain storage bins.
Software was developed to determine the velocity, pres-
sure and local specific airflow rates distributions, the global
airflow rate or initial pressure head in the grain mass store
for three-dimensional cases. The aeration system efficiency
of several stored seeds was analysed to provide the airflow
distribution uniformity and the static pressure head values
that generate the appropriate airflow rate for safe storage.
It was shown that the aeration system of grain storage bin
can be essentially improved by the use of inlets system with
different initial pressures selected for each inlet. Also, it was
shown that it is possible to optimise air distribution in a grain
storage bin by operating each inlet in turn and by selecting
a suitable aeration period for each inlet.
Acknowledgements
The authors would like to thank CNPq for the financial sup-
port for this work (process No. 464380/00-6).
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Simulation of three-dimensional airflow in grain storage binsIntroductionMathematical modelSoftware description and developmentGeometry constructionMesh generationProblem solving and representation
Validation of the mathematical model for non-homogeneous conditions in a grain massExperimental equipmentExperimental results
Numerical simulationsCriterion for describing the efficiency of aeration systemNumerical simulation results
ConclusionsAcknowledgementsReferences