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A simple model to estimate emission of wind-blown particles from a granular bed in comparison to wind tunnel experiments

  • Ferreira, M.C.S.
  • Furieri, B.
  • Ould El Moctar, A
  • Harion, J.-L.
  • Valance, Alexandre
  • Dupont, P.
  • Reis, N.C.
  • Santos, J.M.
Publication Date
Jan 01, 2019
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Dust emissions due to aeolian erosion of exposed granular materials are strongly influenced by grain size distribution. Non-erodible particles that are too heavy to be lifted into the air play a protective role in the aeolian erosion process attenuating emission, which is known as the pavement phenomenon. To date, there is no approach that reliably predicts the reduction in emissions caused by their presence on an aggregate surface. In this work, an analytical model was developed to quantify emissions from particle beds with a wide size distribution. As non-erodible particles accumulate, changes in surface characteristics create increasing shelter for the erodible portion of the bed until the shear on the erodible surface reaches a minimum and emissions cease. The proposed emission model describes the relationship between this minimum value of wind shear and the eroded depth of the bed after the pavement, which in turn gives the emitted mass. In addition, wind tunnel experiments were carried out in order to broaden knowledge of the pavement phenomenon and validate the modelling. A bimodal particle size distribution of sand with erodible and non-erodible particles was used for the tested velocities. Three experimental measurements were carried out (i) continuous weighing of the emitted mass, (ii) eroded depth of the bed at regular time intervals and (iii) final cover rates of the non-erodible particles using digital analysis of sand bed pictures after experiments. Good agreement between the modelling and experimental results was found. The emission model proposed herein is a simple algebraic expression that demands low computational effort. This approach may serve as a base for an emission model for application in granular materials stockpiles. © 2019 Elsevier B.V.

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