Direct numerical simulation on strained turbulent flows and particles within

Gylfason, Ármann; Lee, C-M Chung-min; Perlekar, Prasad; Toschi, Federico

doi:10.1088/1742-6596/318/5/052003

Cited by 1 publication

(2 citation statements)

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“…As in Gylfason et al (2011), in order to solve (3) we apply a pseudo-spectral method with Rogallo's algorithm (Rogallo (1981)) where the following variable transformations are performed:…”

Section: Flow Equations and Flow Simulationmentioning

confidence: 99%

“…We then apply the pseudo-spectral method to equations in (4). More details about the numerical method can be found in Gylfason et al (2011). Numerical simulations of this axisymmetric expansion flow were carried out on a Cartesian grid with 1024×256×256 and 2048×512×512 grid points in the x, y and z directions, respectively.…”

Section: Flow Equations and Flow Simulationmentioning

confidence: 99%

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Inertial particle acceleration in strained turbulence

Lee¹,

Gylfason

Perlekar³

et al. 2015

J. Fluid Mech.

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The dynamics of inertial particles in turbulence is modelled and investigated by means of direct numerical simulation of an axisymmetrically expanding homogeneous turbulent strained flow. This flow can mimic the dynamics of particles close to stagnation points. The influence of mean straining flow is explored by varying the dimensionless strain rate parameter Sk 0 / 0 from 0.2 to 20, where S is the mean strain rate, k 0 and 0 are the turbulent kinetic energy and energy dissipation rate at the onset of straining. We report results relative to the acceleration variances and probability density functions for both passive and inertial particles. A high mean strain is found to have a significant effect on the acceleration variance both directly by an increase in the frequency of the turbulence and indirectly through the coupling of the fluctuating velocity and the mean flow field. The influence of the strain on the normalized particle acceleration probability distribution functions is more subtle. For the case of a passive particle we can approximate the acceleration variance with the aid of rapid-distortion theory and obtain good agreement with simulation data. For the case of inertial particles we can write a formal expression for the accelerations. The magnitude changes in the inertial particle acceleration variance and the effect on the probability density function are then discussed in a wider context for comparable flows, where the effects of the mean flow geometry and of the anisotropy at small scales are present.

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“…As in Gylfason et al (2011), in order to solve (3) we apply a pseudo-spectral method with Rogallo's algorithm (Rogallo (1981)) where the following variable transformations are performed:…”

Section: Flow Equations and Flow Simulationmentioning

confidence: 99%

Section: Flow Equations and Flow Simulationmentioning

confidence: 99%