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1 - Pack Factor Study
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The major variables affecting stored-grain packing are grain type, moisture content, test weight, and bin geometry and dimensions. Variations in packing across different regions of the U.S. must also be investigated as well as other minor factors. In order to avoid the excessive cost of experimentally determining packing factors for all grains and conditions, we will use the preliminary, science-based model mentioned previously to reduce the total number of measurements required to achieve valid results. Physical properties will be measured in the laboratory to use as inputs for modeling. Uniaxial compression will be measured for the bulk grain using established methods and equipment in the University of Kentucky granular mechanics laboratory (McNeill et al., 2004).


The preliminary model (Thompson et al., 1987) will be calibrated in this study. This model employs the differential form of Janssen's equation to estimate the pressure and in-bin bulk density for a given depth of grain in a bin (Ross et al., 1979). We will calibrate and validate this model by measuring packing for the six grains: wheat, corn, grain sorghum, soybeans, oats, and barley. Calibrating this model instead of developing packing factors from field measurements alone will allow us to reduce the number of bins measured from tens of thousands, which would be required to cover the needed range for all variables in a strictly empirical model, to hundreds to validate the science-based model over that same range of variables. Validation data will be gathered from several hundred commercial and on-farm bins of different sizes. Storage bins constructed of steel (welded and corrugated) and concrete will be used. Data will be collected from all of the major grain-producing locations within the U.S., emphasizing the areas producing the most grain: the Midwest, the southern Mississippi River valley, the Central Plains, and the Northern Plains. Measured packing values will be compared to model results to determine bias and accuracy of the predictions. Model bias will be evaluated and corrected by comparing means and evaluating the correlation of packing factor to moisture content and test weight for each grain. Standard errors of calibration will be calculated to evaluate the accuracy of the corrected model. This accuracy and confidence-interval information alone will be a major improvement over the current methods for which the errors are not known. In addition, the new model should have better accuracy than the old methods because it accounts for the many important variables in grain and bin properties that affect the final packing but were not taken into account by the old method. The predictions of the new model will be compared to predictions from the old method using calculated standard errors and the model will be refined to reduce the overall standard error compared to the old method.

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