<p>Grain kernel damage during harvesting and handling continues
to be a challenge in grain postharvest operations. The damage causes physical and physiological changes to grain,
which reduces the grain quality and leads to significant yield loss. During harvesting and handling, grain
kernels are subject to complex loading conditions consisting of a
combination of impact, shear, and compression forces that can result in
mechanical damage. Although there is
considerable empirical data focused on kernel damage, there is a lack of
generalizable mechanics-based predictive models. Mechanics-based models are
desirable since they would be useful for
providing guidance on designing and operating grain handling processes to
minimize kernel damage and, thus, improve grain quality. The objective
of the current study is to develop a mechanics-based model for predicting damage of corn and wheat kernels
using the discrete element method (DEM).</p>
<p>The first step in DEM modeling is to determine the model
input parameter values. This step is critical since the accuracy of the DEM
simulations model is greatly affected by these parameters. The input parameters
for the model developed in this current study are the physical and mechanical
properties of corn and wheat kernels. These properties were determined by either
direct measurement or calibration tests and validated with bulk material tests.
X-ray micro-CT scanning method was used to acquire the grain kernel particle
shape representation. The coefficient of friction (COF) was measured using a reciprocating
pin tribometer. The coefficient of restitution (COR) was measured using the
calibration method with a box containing multiple bins. The measured model
parameter values were used to simulate common bulk material tests, i.e. bulk
density and angle of repose. A comparison was made between the simulated
results and the experimental measurements. The low
percent error between experimental and simulated values indicate the accurate
model parameter values estimation.</p>
<p>The damage resistance of corn and wheat
kernels to compression, friction, and repeated impacts were measured using the
universal testing machine, pin-on-disk tribometer, and Wisconsin breakage
tester, respectively. Lognormal distribution was used to model the compression test data, and
three-parameter Weibull
distribution was used to model the single and repeated impact test data. The
statistical models were able accurately predict the damage probability based on
the loading force or input energy. The wear damage was insignificant for
corn-acrylic, corn-steel, and wheat-acrylic wear tests. For wheat-steel wear
test, the average work done by the friction force to cause pericarp damage was
3.85
1.50 J.
The test results showed that the corn kernels were more susceptible to impact
loading, while wheat kernels were more susceptible to compression loading. Both
corn and wheat kernels had high resistance to wear damage.</p>
<p>The statistical model that
predicts the impact damage probability based on impact energy was implemented
in DEM. Stein breakage tester was used to validate the developed model. The
damage level of the samples was then evaluated and compared with the predicted
damage level output by the DEM simulation using the measured input parameters.
However, it was found that the DEM simulation prediction error of damage level
was high when the input parameters characterized by the Wisconsin breakage
tester were used. The parameters were then recalibrated using Stein breakage
tester. The model was able to give a good
prediction on the damage fraction at different sample size and time levels when
the recalibrated parameter values were used.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9034313 |
| Date | 12 October 2021 |
| Creators | Zhengpu Chen (7036694) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Developing_a_Validated_Model_for_Predicting_Grain_Damage_Using_DEM/9034313 |
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