Airflow Resistance of Seeds at Different Bulk Densities Using Ergun’s Equation

Abstract

Airflow resistance of grains and oilseeds has been extensively studied. Traditionally the data has been presented using Shedd's curves. However, this assumes that airflow resistance is independent of grain depth. Grain undergoes compaction during storage that changes the bulk density, porosity, and therefore the airflow resistance. Ergun's equation is a function of particle size and porosity of the granular material. Airflow resistance by Ergun's equation was used to predict the pressure drop across a column of corn, soft white winter wheat, soft red winter wheat, and soybeans at three moisture content levels and two bulk densities. The maximum root mean square error when predicting airflow resistance using Ergun's equation was less than 23 Pa/m when the pressure drop was less than 500 Pa/m. If all data was included up to a pressure drop of 1800 Pa/m, the average root mean square error for calculating airflow resistance was 76 Pa/m. The effect of grain orientation that would be typical in storage bins was negligible, less than a 10% increase in airflow resistance over a range of kernel orientations that varied between −10°, +10°, and 20° from the angle of repose. However, the fill method and resulting bulk density increased the airflow resistance by an order of magnitude. Ergun's equation, with an appropriate model of porosity variation within a storage bin, could be utilized for the design and analysis of grain aeration systems.

Prediction of pressure drop through packed beds of granular materials: influence of moisture reduction and air velocity

“…The increase in ΔP with moisture reduction was greater in packed beds of lentils (42.0%), soy (41.0%) and barley (39.2%), and more discrete in packed beds of alumina (30.1%) and oats (24.6%). The overall patterns found in this study agree with previous data reported for biological products (Al-Yahya & Moghazi, 1998;Giner & Denisienia, 1996;Górnicki & Kaleta, 2015;Kobus et al, 2011;Molenda et al, 2005;Sokhansanj et al, 1990). With a view to incorporating the effect of the moisture content of the solids in Equation 1, κ and c were linearly regressed to the moisture content of the solids for each product, and the fitted equations inserted into Equation 1, resulting in a modified empirical equation, Equation 15, as follows:…”

“…The fitted equations were verified using experimental data reported in the literature for products and operational conditions similar to those used in the present study. Figure 8 shows a comparison of the experimental and predicted values for / ΔP L and further information on the data set used can be found elsewhere (Al-Yahya & Moghazi, 1998;Kobus et al, 2011;Molenda et al, 2005;Sokhansanj et al, 1990). Very good agreements were observed between the experimental and predicted values for / ΔP L through packed beds of soy, lentils, barley and oats, with 2 R > 0.9 and low RMSE .…”

Prediction of Temperature Distributions in Peaked, Leveled and Inverted Cone Grain Mass Configurations during Aeration of Corn

“…The value 1.75 was used in equation 3 instead of 3.5 because the value ½ was in equation 2. The average particle diameter of 0.00623 m for corn at 14.5% moisture (wet basis) was used for the simulations in this study (Molenda et al, 2005).…”

A Light-Weight Vibrational Motor Powered Recoil Robot That Hops Rapidly Across Granular Media

“…The lines in Fig. 9 don't go through the origin, so we do see evidence of non-linearity in the flow velocity vs pressure relation (for extensions to Darcy's law for airflow through agricultural grains see [38,39,40]). However a vertical mechanism platform velocity Aω = 40 cm/s is well above the maximum measured flow velocity ∼ 18 cm/s measured on the granular media.…”