Modeling Turbulent Multiphase Flow in Design of Nut Harvesters with Reduced Dust Emission and Low Power Demand

Abstract

Computational fluid dynamics (CFD) modeling was evaluated for a physical prototype nut harvester as a method to determine model effectiveness for predicting dust emissions and reduced power demand in terms of pressure drop. Particle-laden gas was dilute and highly turbulent with an estimated particle volume fraction of 0.24% and gas flow Reynolds number of >105. Airflow simulations were based on the realizable k-e and Reynolds stress models; the stochastic Lagrangian discrete phase model determined particle collection and tracking characteristics. The predicted results of the gas flows from CFD simulations followed the trends of experimental data. CFD-guided design reduced airflow pressure drop by 43% to 54%, resulting in a corresponding reduction in power demand based on pressure drop measurements and simulations. Particle collection efficiencies for particle diameters of 10 m were increased by 3.6 to 5.4 times. The particle flow model was partially validated, although additional measurements of the particle collection efficiencies and particle locations may be required. The results indicated that nut harvester design modifications can be guided by CFD modeling.