Computational fluid dynamics analysis of the Venturi Dustiness Tester

Abstract

Dustiness quantifies the propensity of a finely divided solid to be aerosolized by a prescribed mechanical stimulus. Dustiness is relevant wherever powders are mixed, transferred or handled, and is important in the control of hazardous exposures and the prevention of dust explosions and product loss. Limited quantities of active pharmaceutical powders available for testing led to the development (at University of North Carolina) of a Venturi-driven dustiness tester. The powder is turbulently injected at high speed (Re ~ 2 × 104) into a glass chamber; the aerosol is then gently sampled (Re ~ 2 × 103) through two filters located at the top of the chamber; the dustiness index is the ratio of sampled to injected mass of powder. Injection is activated by suction at an Extraction Port at the top of the chamber; loss of powder during injection compromises the sampled dustiness. The present work analyzes the flow inside the Venturi Dustiness Tester, using an Unsteady Reynolds-Averaged Navier-Stokes formulation with the k-ω Shear Stress Transport turbulence model. The simulation considers single-phase flow, valid for small particles (Stokes number Stk <1). Results show that ~ 24% of fluid-tracers escape the tester before the Sampling Phase begins. Dispersion of the powder during the Injection Phase results in a uniform aerosol inside the tester, even for inhomogeneous injections, satisfying a necessary condition for the accurate evaluation of dustiness. Simulations are also performed under the conditions of reduced Extraction-Port flow; results confirm the importance of high Extraction-Port flow rate (standard operation) for uniform distribution of fluid tracers. Simulations are also performed under the conditions of delayed powder injection; results show that a uniform aerosol is still achieved provided 0.5 s elapses between powder injection and sampling.

On the Relationship between Exposure to Particles and Dustiness during Handling of Powders in Industrial Settings

“…In this framework, dustiness has risen as a valuable tool for occupational safety (Hamelmann & Schmidt, 2003), as it is a measure of a material's tendency to generate airborne dust during handling. However, discussions are ongoing about the use of the dustiness index as a direct predictor for worker exposure (Dubey et al, 2017;Fonseca et al, 2018) because dust emissions are known to depend on powder properties, the amount of material handled, the process, and local controls (Fransman et al, 2011). Certain authors (Brouwer, 2006;Class et al, 2001;Fonseca et al, 2018;Heitbrink et al, 1990) found no clear or limited relationship between exposure and dustiness, although they pointed out the importance of this parameter for risk assessment.…”

Evaluation of total and inhalable samplers for the collection of carbon nanotube and carbon nanofiber aerosols

“…The VDT was selected due to its ability to utilize small quantities of powder (5 mg) under confined conditions (Boundy, Leith, and Polton 2006;Evans et al 2013). This small aerosol chamber is ideal for dealing with hazardous powders, as it uses small quantities of material to generate a reproducible aerosol within an enclosed set-up and has been validated experimentally to provide reproducible results (Boundy, Leith, and Polton 2006;Evans et al 2013;Evans et al 2014;Dubey, Ghia, and Turkevich 2017). The VDT aerosolization method involves more aggressive airflows than those typically encountered in large-scale workplace activities, but is expected to resemble energetic dust dispersion activities such as the use of compressed air to clean contaminated protective clothing or work surfaces (Evans et al 2013).…”

Direct Comparison of Immersion and Cold-Plate Based Cooling for Automotive Li-Ion Battery Modules

“…The first type of uncertainty estimation is conducted through the use of Grid Convergence Index (GCI) method proposed by Celik et al [29]. This method has been evaluated and implemented in several hundred CFD studies [30][31][32][33]. In order to estimate GCI, Case 3 (Table 4) was simulated on three different grids (Cases 4 & 5), which are shown in Table 7.…”