Efficient Statistical Transport Model for Turbulent Particle Dispersion in Sprays

Litchford, Ron J.; Jeng, San‐Mou

doi:10.2514/3.59965

Cited by 61 publications

(14 citation statements)

References 8 publications

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“…The level of internal dispersion may increase due to turbulence or decrease due to aggregation events. Litchford and Jeng (1991) suggest that each parcel position is represented by a normal distribution with a mean µ and standard deviation σ, which is representative of the parcel radius and is expressed in terms of a two-particle velocity correlation function. Alternatively, Johannessen et al (2001), modelled the effects of the flow field on the parcel by a 'dilution' factor.…”

Section: The Interaction Volume and Aggregation Time-stepmentioning

confidence: 99%

Stochastic modelling of particle aggregation

Pesmazoglou

Kempf

Navarro‐Martinez

2016

International Journal of Multiphase Flow

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Section: The Interaction Volume and Aggregation Time-stepmentioning

confidence: 99%

Stochastic modelling of particle aggregation

Pesmazoglou

Kempf

Navarro‐Martinez

2016

International Journal of Multiphase Flow

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“…On the contrary, the latter approach involves tracking a statistical representation of some clusters of particles. Here we adopted this statistical approach, namely the Particle Cloud Tracking (PCT) model, first proposed by Baxter [23], and then developed and used by a number of researches [24][25][26][27][28][29]. Although the SPT provides more accurate results, the computational effort required is between one and two orders of magnitude larger than that required for PCT, thus for industrial applications the PCT is more suitable [28][29][30][31].…”

Section: Modelling Approachmentioning

confidence: 99%

Predicting the Performance of an Industrial Centrifugal Fan Incorporating Cambered Plate Impeller Blades

Cardillo¹,

Corsini²,

Delibra³

et al. 2014

Period. Polytech. Mech. Eng.

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Application of computational methods to industrial fan de- IntroductionThe industrial centrifugal fans used in cement, steel and power applications are typically tailored to the applications' specific flow and pressure demands [1]. Although aerodynamic performance has historically been scaled from previous laboratory test data, the impeller, shaft and housing mechanical design, bearing selection and rotor-dynamic analysis are invariably unique. A consequence of the need for a unique design for each fan has resulted in a historic focus on automating the processes associated with mechanical design and rotor-dynamic analysis [1]. Design methods that embed previously established limits into a computer code are coupled with parametric three-dimensional Computer Aided Design (CAD) models that have associated two-dimensional drawing packs. The result are design methods that engineers can use to deliver a full set of manufacturing drawings for a centrifugal industrial fan with less than one man week of engineering time required for each design.A historic focus on the automation and control of industrial centrifugal fan order related engineering and production of manufacturing drawings has helped to ensure that in-service failures are rare; however, this ultimately limits the resultant design's aerodynamic efficiency. Scaling and interpolating fan performance using methods developed in the 1950's [2,3] results in reliably predicting aerodynamic performance, but inevitably also sub-optimal performance.

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“…However, particular attention should be paid to computing the radial component of the cumulative distribution function for axisymmetric flow configurations. The axisymmetric cumulative function at any radius r is defined as the volume swept out by the planar region of the PDF bounded between −r and r as it is revolved 360°about the axis of symmetry [23]. Detailed discussion of this issue can be found in Reference [24].…”

Section: Stochastic -Probabilistic Particle Dispersion Modelmentioning

confidence: 99%

“…The Gaussian PDF may be one of the adequate PDFs to represent a variety of flows. However, Litchford and Jeng [23] carried out a sensitivity study of the effects of PDF shapes on Lagrangian computations and found that a simplified isosceles triangle PDF may replace the Gaussian PDF, thus enhancing the code efficiency. Therefore the isosceles triangle PDF, as shown in Figure 2, is used for the present study.…”

Section: Stochastic -Probabilistic Particle Dispersion Modelmentioning

confidence: 99%