Highly parallel particle-laden flow solver for turbulence research

Ireland, Peter J.; Vaithianathan, T.; Sukheswalla, Parvez; Ray, Bidhan Chandra; Collins, Lance R.

doi:10.1016/j.compfluid.2013.01.020

Cited by 44 publications

(43 citation statements)

References 29 publications

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“…We now turn to test the theoretical results using data from a Direct Numerical Simulation (DNS) of the incompressible NSE. The NSE are solved using the Highly Parallel Particle-laden flow Solver for Turbulence Research (HiPPSTR) psuedo-spectral code [30] on a triply-periodic domain of length 2π with 2048 3 grid points. The Taylor Reynolds number of the statistically steady and isotropic flow is R λ = 597.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Is vortex stretching the main cause of the turbulent energy cascade?

Carbone

Bragg

2020

J. Fluid Mech.

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In three dimensional turbulence there is on average a cascade of kinetic energy from the largest to the smallest scales of the flow. While the dominant idea is that the cascade occurs through the physical process of vortex stretching, evidence for this is debated. In the framework of the Karman-Howarth equation for the two point turbulent kinetic energy, we derive a new result for the average flux of kinetic energy between two points in the flow that reveals the role of vortex stretching. However, the result shows that vortex stretching is in fact not the main contributor to the average energy cascade; the main contributor is the self-amplification of the strain-rate field. We emphasize the need to correctly distinguish and not conflate the roles of vortex stretching and strain-self amplification in order to correctly understand the physics of the cascade, and also resolve a paradox regarding the differing role of vortex stretching on the mechanisms of the energy cascade and energy dissipation rate. Direct numerical simulations are used to confirm the results, as well as provide further results and insights on vortex stretching and strain-self amplification at different scales in the flow. Interestingly, the results imply that while vortex stretching plays a sub-leading role in the average cascade, it may play a leading order role during large fluctuations of the energy cascade about its average behavior.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Is vortex stretching the main cause of the turbulent energy cascade?

Carbone

Bragg

2020

J. Fluid Mech.

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“…The DNS simulations were of statistically stationary, homogeneous and isotropic turbulence at Re λ = 224, where Re λ is the Taylor microscale Reynolds number. Details on the DNS can be found in Ireland et al (2013).…”

Section: Tests Using Dnsmentioning

confidence: 99%

On the relationship between the non-local clustering mechanism and preferential concentration

2015

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'Preferential concentration ' (Phys. Fluids A3:1169-78, 1991 refers to the clustering of inertial particles in the high-strain, low-rotation regions of turbulence. The 'centrifuge mechanism' of Maxey (J. Fluid Mech. 174:441-65, 1987) appears to explain this phenomenon. In a recent paper, Bragg & Collins (New J. Phys. 16:055013, 2014) showed that the centrifuge mechanism is dominant only in the regime St 1, where St is the Stokes number based on the Kolmogorov time scale. Outside this regime, the centrifuge mechanism gives way to a non-local, path-history symmetry breaking mechanism. However, despite the change in the clustering mechanism, the instantaneous particle positions continue to correlate with high-strain, low-rotation regions of the turbulence. In this paper, we analyze the exact equation governing the radial distribution function and show how the non-local clustering mechanism is influenced by, but not dependent upon, the preferential sampling of the fluid velocity gradient tensor along the particle path-histories in such a way as to generate a bias for clustering in high-strain regions of the turbulence. We also show how the non-local mechanism still generates clustering, but without preferential concentration, in the limit where the timescales of the fluid velocity gradient tensor measured along the inertial particle trajectories approaches zero (such as whitenoise flows or for particles in turbulence settling under strong gravity). Finally, we use data from a direct numerical simulation of inertial particles suspended in Navier-Stokes turbulence to validate the arguments presented in this study.

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“…We advance the particles following (2.6) and (2.7) using the second-order accurate exponential integrator defined in Ireland et al (2012). However, this reduction in the phase space of the system generates another issue.…”

Section: Inertial Particle Motionmentioning

confidence: 98%

“…These particles are introduced into the stationary flow field at random positions and with the fluid velocity at those locations. Particles are advanced in time according to (2.4) and (2.5) using an improved numerical scheme that was recently developed in our group (Ireland et al 2012). This new algorithm, based on exponential integrators, is second-order accurate in time and can simulate particles with arbitrarily small St accurately, allowing us to use the fluid time step (dictated by the CFL condition) to advance the inertial particles, irrespective of St, thereby significantly reducing the run times for low-St particles.…”

Section: Inertial Particle Motionmentioning

confidence: 99%

Investigation of sub-Kolmogorov inertial particle pair dynamics in turbulence using novel satellite particle simulations

Ray

Collins

2013

J. Fluid Mech.

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Clustering (or preferential concentration) of weakly inertial particles suspended in a homogeneous isotropic turbulent flow is driven primarily by the smallest eddies at the so-called Kolmogorov scale. In particle-laden large-eddy simulations (LES), these small scales are not resolved by the grid and hence their effect on both the resolved flow scales and the particle motion have to be modelled. In order to predict clustering in a particle-laden LES, it is crucial that the subgrid model for the particles captures the mechanism by which the subgrid scales affect the particle motion (Ray & Collins, J. Fluid Mech., vol. 680, 2011, pp. 488-510). In this paper, we describe novel satellite particle simulations (SPS), in which we study the clustering and relative velocity statistics of inertial particles at separation distances well below the Kolmogorov length scale. SPS is designed to isolate pairwise interactions of particles, and is therefore well suited for developing two-particle models. We show that the power-law dependence of the radial distribution function (RDF), a statistical measure of clustering, is predicted by the SPS in excellent agreement with direct numerical simulations (DNS) for Stokes numbers up to 3, implying that no explicit information from the inertial range is required to accurately describe particle clustering. This result further explains our successful prediction of the RDF power using the drift-diffusion model of Chun et al. (J. Fluid Mech., vol. 536, 2005, pp. 219-251) for St 0.4. We also consider the second-order longitudinal relative velocity structure function for the particles; we show that the SPS is able to capture its power-law exponent for St 0.5 and attribute the disagreement at larger St to the effect of the larger scales of motion not captured by the SPS. Further, the SPS is able to capture the 'caustic activation' of the structure function at zero separation and predict the critical St and rate of activation in agreement with the DNS (Salazar & Collins, J. Fluid. Mech., vol. 696, 2012, pp. 45-66). We show comparisons between filtered DNS and equivalently filtered SPS, and the findings are similar to the unfiltered case. Overall, SPS is an efficient and accurate computational tool for investigating particle pair dynamics at small separations, as well as an interesting platform for developing LES subgrid models designed to accurately reproduce particle clustering.

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Highly parallel particle-laden flow solver for turbulence research

Cited by 44 publications

References 29 publications

Is vortex stretching the main cause of the turbulent energy cascade?

Is vortex stretching the main cause of the turbulent energy cascade?

On the relationship between the non-local clustering mechanism and preferential concentration

Investigation of sub-Kolmogorov inertial particle pair dynamics in turbulence using novel satellite particle simulations

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