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CW-EGM-RPI-2014.pdf
Analysis of Mash Tun Flow: Recommendations for Home Brewers
Conor J. Walsh1 and Ernesto Gutierrez-Miravete*2
1
General Dynamics-Electric Boat, Groton, CT
2
Rensselaer at Hartford, Hartford, CT
*Corresponding author: 275 Windsor Street, Hartford, CT 06120 , [email protected]
Abstract: The paper describes work designed to
help home brewers better understand the
fundamental aspects of fluid dynamics inside
mash tun reactors. Beer can be manufactured at
home rather simply using a picnic cooler as mash
tun reactor but the production of a good quality
brew requires continuous experimentation and
understanding of the fundamental processes
inside the reactor. This paper describes work
devised to investigate the variations in
performance of home-made mash tun reactor
designs associated with the physics of the flow
of water through the porous bed of malted
grains. Using the model, various proposed mash
tun reactor configurations were examined and
evaluated and a set of recommendations for
home brewers was developed.
Keywords: Mash tun reactors, brewing, porous
media flow, reactor design
1. Introduction
The major steps in the beer making process are
rather simple a quite good brew can be produced
using a common picnic cooler. First, grain
(usually barley) is wetted and allowed to
partially germinate before dried in a kiln
(malting) in order to convert starch reserves
within the grain into more easily fermentable
sugars. Next, the malted grains are soaked in hot
water to extract the fermentable sugars and then
rinsed slowly to ensure as much sugar is
removed as possible. This step is the called
“mashing,” and the device in which it is
conducted is called a “mash tun.” The mashing
process is critical to the final taste, aroma, color,
and body of the beer, and provides an excellent
opportunity to use science and technology to
improve the home brewing experience.
Mashing can be further broken down into the
initial steeping step (where the grains simply sit
in hot water) and the subsequent rinsing step
(called sparging). After the malted grains are
crushed in a mill in order to facilitate complete
hydration and expose the inner grain material,
they are combined with hot water (usually at a
temperature of 160 to 165°F) in the mash tun and
allowed to sit there for some period of time,
usually around 1 hour. The mash temperature
should settle at a temperature between 150 and
155°F, and kept there for the entire steeping
time. Any off-the-shelf plastic cooler will
provide the thermal insulation required of a
beginner to intermediate homebrewed.
To calculate the extraction efficiency of a beer
one must know/measure the specific gravity.
This is readily done using a hydrometer. Specific
gravity of beer generally varies between 1.005
and 1.150, and is usually expressed in “points,”
where the leading 1 is dropped and the remainder
is multiplied by 1000. For example, a specific
gravity reading of 1.040 would be 40 points.
The concentration of sugar in the wort is
calculated in ppg (specific gravity points per
pound per gallon), called cwort [1]:
c wort =
In this equation,
Vwort  BG
mmalt
Vwort is the total volume of
wort in gallons, BG
specific gravity of the
specific gravity of the
mash tun), and mmalt
represents the original
boil in points (also the
wort after it leaves the
is the mass of the malt
used in the mash tun in pounds. Once this value
is determined, it can be compared to the
maximum potential yield from the malt. The
following equation defines the extraction
efficiency of a beer:
eextract =
(where
c wort
c max
eextract is the extraction efficiency and
cmax is the maximum yield for the given malt in
ppg). Typical home brewer extraction efficiency
values average around 75%.
A mash tun reactor must always include a filter
of some kind to allow for hot water to run
through the grain and carry the sugar out while
leaving the grain husks. The differences in how
this filter is designed are the primary drivers of
the efficiency of a mash tun. For home brewers,
a popular design for filtering is the slotted pipe
manifold.
Highly efficient setups are desirable since a
highly efficient mash produces a higher sugar
output per grain input. While continuous
sparging
techniques
increase
extraction
efficiency, they also bring with them an
increased risk of over extraction and poor wort
quality due to the presence of undesirable flavor
compounds. Interestingly, wort quality has been
found to correlate to the uniformity of flow of
water through the mash tun [2]. In order to
analyze the extraction efficiency and wort
quality of a mash tun, computational fluid
dynamics simulations were performed using the
COMSOL Multiphysics software. The results of
this experience are described in this paper.
Flow anywhere outside of the grain bed (in the
pipe manifold, as well as in the water level above
the grain bed) was assumed laminar flow and
evaluated using the Navier-Stokes equation.
Gravity was the only body force applied. All
fluid regions of the model were held at 155
degrees Fahrenheit, a common sparging
temperature.
A further assumption is that the sparge velocity
was 0.18 [gal/min/ft^2]. This value has been
recommended for optimal starch conversion.
3. Use of COMSOL Multiphysics
COMSOL Multiphysics was used to model fluid
flow through the grain bed and pipe manifold.
Ideally a single, accurate 3-dimensional model
including all relevant features would be used to
evaluate different mash tun configurations (see
Error! Reference source not found.), but due
to computation limitations this was not feasible.
2. Description of the System
The mash tun reactor of this study had the
dimensions of an average rectangular cooler
often used by home brewers (~ 12” x 12” x 21”).
The reactor was assumed to be initially full with
a mixture of malted grains and water. Along the
bottom of the reactor a pipe manifold filter was
used. The investigation of the effects of various
pipe manifold designs on the extraction
efficiency was one of the key objectives of this
study.
Flow through the grain bed can be analyzed
using Darcy’s law for flow through porous
media:
q= 
K

 p
where K is the permeability of the porous
medium (in units of “Darcy” or square meters),
 is the dynamic viscosity of the fluid, and p
is the pressure gradient vector [3].
Figure 1. Schematic representation of the home
brewer mash tun reactor.
In place of a single 3D model, two types of 2dimensional models were used to model the
system. These models were constructed by by
taking various x-y plane or y-z plane cuts (see
Error! Reference source not found.) and
modifying individual features. These different
planar cuts were evaluated separately, but they
approximate the 3-dimensional parameter
modification when all results are viewed in
aggregate. In this way, no model considered all
of the applicable factors at once, but the results
can be combined from the various models to
develop an ideal mash tun design.
Figure 2. x-y and y-z cuts of the 3D geometry for
the 2D models.
Design parameters investigated included the
number of pipes in the manifold and the number
of slots in each pipe (See figure 3).
Figure 3. x-y and y-z (detail) cuts of the 3D
geometry for the 2D models showing a four pipe
manifold and two pipe slots .
To incorporate the potentially significant effects
of sugar concentration within the wort and
subsequent density and viscosity increase, two
separate materials were used for the fluid regions
in the COMSOL models. The small fluid region
sitting on top of the grain bed was given
properties of pure water, using the built-in Water
option from the COMSOL material database.
For the fluid through the remainder of the model,
a custom material called Wort was created and
given average fluid property values for wort:
specifically, a viscosity of 1.5 mPa*s and
density of 1050 kg/m^3 (equal to a specific
gravity of approximately 1.050, which is an
average pre-boil gravity).
To model the flow in the reactor the COMSOL
Multiphyscis module Free and Porous Flow was
used that easily allowed coupling the two flow
regions. The permeability of the bed was
assumed K = 9.87 e-11 m^2 [4] and the porosity
was assumed uniform and equal to 0.3. To model
the pipe manifold the built-in copper material
properties from the COMSOL material was used.
To simulate the hindering effect of the slots on
the flow in the x-y models a permeability 100
times smaller than in the grain bed was assumed
to apply.
For boundary conditions, at the inlet (the free
upper surface of the reactor) an incoming flow of
water equal to 0.18 [gal/min/ft^2] was
prescribed. This converts to approximately
1.2224 e-4 [m/s]. The outlet was given a
pressure condition of 0 Pa, with no viscous
stress.
The method used to compute extract efficiency
and wort quality was fairly simple. Concerning
extraction efficiency, when using a continuous
sparging technique with constant flow at the inlet
and outlet, flow through the grain bed is
distributed about the ideal velocity recommended
by Narziss, about 1.224 e-4 [m/s]. After running
a simulation, a histogram plot can created
showing frequency of velocities through the
grain bed. These plots were then used to
evaluate extraction efficiency and wort quality.
By making the assumption that the ideal Narziss
velocity provides 100% extraction of sugars
from the grain and into the wort. It follows that
any velocity in the grain bed that exceeds this
value will also provide 100% extraction while
any velocity below this value would provide less
than 100% extraction.
Regarding wort quality, for the purposes of this
project, areas of the grain bed with velocities in
excess of 100% are considered oversparged.
These oversparged regions are where the danger
of poor wort quality lies. Exposed to high flow,
these areas of grain will be subject to tannin
extraction. The histogram plots generated by
COMSOL were used to determine the percent of
the grain bed that was oversparged, and this
provided another performance indicator for
evaluating the various mash tun configurations.
Using the 1D plot feature in COMSOL, results
from each new model were mapped to a
histogram plot that quantified the percentage of
the grain bed experiencing specific velocity
levels. For simplicity, the histogram was broken
up into 20 sections between 0 m/s and the ideal
velocity. Figure 5 shows such a histogram for the
single pipe reactor.
4. Results
Figure 4 show the computed streamlines and
constant pressure lines on the x-y plane for the
single pipe mash tun reactor.
Figure 5. Histogram showing the distribution of
velocities through the grain bed.
The relative area of the bed that is subject to
each particular velocity was then multiplied by
the extraction efficiency at that velocity and the
results added together to give a representation of
the total extraction efficiency. The efficiency for
this example configuration was calculated to be
about 95%.
The figure shows that the velocity of the flow in
some areas of the bed is relatively large. These
areas may be considered oversparged and be the
source of poor wort quality. The percentage of
the grain bed that is oversparged can be
evaluated by simply plotting the percentage of
the bed experiencing a velocity greater than the
ideal velocity. It can be easily seen from the
resulting histogram that about 42% of the bed is
oversparged.
Figure 4. Computed streamlines and pressure
contours for the single pipe mash tun reactor.
While the exact percentages found through the
simulations may not directly correlate to realworld testing, predictions of significantly
improved performance metrics due to certain
design variations are expected to transfer well to
real-world mash tun builds.
Mash tun reactor designs containing four and
eight pipe manifolds were also investigated.
Figure 6 is a schematic representation of a four
pipe manifold reactor and figure 7 shows the
computed streamlines for this configuration. This
figure should be compared with figure 4 above.
Note that there is a large percentage of low-
velocity regions in the y-z type model compared
to the x-y type model. This is due to the lack of
flow around the manifold legs in the y-z type
model that is captured in the x-y models. The
result is that the low flow in the region below the
manifold is exaggerated in the y-z model.
However, the area below the manifold still
experiences relatively low flow in the x-y model,
which meshes with actual experiences of home
brewers. One of the primary sources of water
lost when brewing comes from the percentage of
water that remains trapped in the mash tun below
the manifold and cannot be drained.
Table 1 summarizes the results of investigations
of mash tun reactors with different number of
pipes.
Table 1: Calculated efficiency and wort quality for
one, four and eight pipe manifold mash tun reactors.
Figure 6. Schematic representation of mash tun
reactor with four pipe manifold..
Pipes
1
4
8
Efficiency
(%)
89.9
91.2
88.4
Oversparged
(%)
45.8
42.4
28.5
According to these results, in terms of wort
quality, the ideal mash tun design would include
as many manifold legs as can possibly be fit into
the mash tun. However, there is a slight drop in
efficiency with high numbers of legs. Moreover,
multiple pipe manifolds are more difficult to
construct and maintain.
A detailed description of many other
computational experiments performed and their
results can be found in the Mr. Walsh’s RPI
Final Master’s Project Report [5].
5. Conclusions
Figure 7. Computed streamlines for the x-y and x-z
planes of the four pipe mash tun reactor.
COMSOL Multiphysics provided a convenient
and easy to use environment to carry out
computer experiments designed to compare the
effectiveness of various proposed mash tun
reactor designs. Together with some basic
empirical understanding of the brewing process,
finite element modeling with COMSOL allowed
the testing of ideas and intuition and helped
generate insight and know-how useful to the
home brewer.
6. References
1. Narziss, Ludwig. The Technology of Brewing
Beer. Ferdinand Enke Verlag, Stuttgart,
Germany, 1992.
2. Palmer, John J. How to Brew. 3rd ed. Brewers
Publications, 2006.
3. Vafai, Kambiz. Handbook of Porous Media,
Second Edition. CRC Press, 2005.
4. Hirasaki, G. J. “Lecture Notes on
Adsorption.” CENG 402, Chapter 3.
<http://www.owlnet.rice.edu/~ceng402/Hirasaki/
CHAP3D.pdf>
5. C. J. Walsh, Design Recommendations for
Homebrewers based on CFD Analysis of Mash
Tun, Master of Mechanical Engineering Final
Project Report, Rensselaer Polytechnic Institute
at Hartford, Hartford, CT, 2013.
7. Acknowledgements
Mr. C.J. Walsh thanks GD-EB for support during
his studies at RPI.
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