scholarly journals Two-way coupled stochastic model for dispersion of inertial particles in turbulence

2012 ◽  
Vol 700 ◽  
pp. 29-62 ◽  
Author(s):  
Madhusudan G. Pai ◽  
Shankar Subramaniam
Keyword(s):  
Kinetic Energy ◽  
Stochastic Model ◽  
Turbulent Kinetic Energy ◽  
Time Scales ◽  
Time Scale ◽  
Stokes Number ◽  
Particle Dispersion ◽  
Point Particle ◽  
Two Phase Flows ◽  
Two Phase

AbstractTurbulent two-phase flows are characterized by the presence of multiple time and length scales. Of particular interest in flows with non-negligible interphase momentum coupling are the time scales associated with interphase turbulent kinetic energy transfer (TKE) and inertial particle dispersion. Point-particle direct numerical simulations (DNS) of homogeneous turbulent flows laden with sub-Kolmogorov size particles report that the time scale associated with the interphase TKE transfer behaves differently with Stokes number than the time scale associated with particle dispersion. Here, the Stokes number is defined as the ratio of the particle momentum response time scale to the Kolmogorov time scale of turbulence. In this study, we propose a two-way coupled stochastic model (CSM), which is a system of two coupled Langevin equations for the fluctuating velocities in each phase. The basis for the model is the Eulerian–Eulerian probability density function formalism for two-phase flows that was established in Pai & Subramaniam (J. Fluid Mech., vol. 628, 2009, pp. 181–228). This new model possesses the unique capability ofsimultaneouslycapturing the disparate dependence of the time scales associated with interphase TKE transfer and particle dispersion on Stokes number. This is ascertained by comparing predicted trends of statistics of turbulent kinetic energy and particle dispersion in both phases from CSM, for varying Stokes number and mass loading, with point-particle DNS datasets of homogeneous particle-laden flows.

Effect of Particle Residence Time on Particle Dispersion in a Plane Mixing Layer

1993 ◽  
Vol 115 (4) ◽  
pp. 751-759 ◽  
Author(s):  
Tsuneaki Ishima ◽  
Koichi Hishida ◽  
Masanobu Maeda
Keyword(s):  
Gas Phase ◽  
Residence Time ◽  
Time Scale ◽  
Characteristic Time ◽  
Mixing Layer ◽  
Particle Dispersion ◽  
Laser Doppler Velocimeter ◽  
Characteristic Time Scale ◽  
Two Phase ◽  
Particle Residence Time

A particle dispersion has been experimentally investigated in a two-dimensional mixing layer with a large relative velocity between particle and gas-phase in order to clarify the effect of particle residence time on particle dispersion. Spherical glass particles 42, 72, and 135 μm in diameter were loaded directly into the origin of the shear layer. Particle number density and the velocities of both particle and gas phase were measured by a laser Doppler velocimeter with modified signal processing for two-phase flow. The results confirmed that the characteristic time scale of the coherent eddy apparently became equivalent to a shorter characteristic time scale due to a less residence time. The particle dispersion coefficients were well correlated to the extended Stokes number defined as the ratio of the particle relaxation time to the substantial eddy characteristic time scale which was evaluated by taking account of the particle residence time.

scholarly journals Equilibration dynamics in nuclear reactions

2019 ◽  
Vol 223 ◽  
pp. 01066
Author(s):  
A.S. Umar ◽  
C. Simenel ◽  
S. Ayik ◽  
K. Godbey
Keyword(s):  
Kinetic Energy ◽  
Time Scales ◽  
Time Scale ◽  
Nuclear Reactions ◽  
Total Kinetic Energy ◽  
Equilibration Time

We discuss the equilibration dynamics and time–scales for various quantities that are connected to the experimentally observable entities. These include the study of mass, isospin, and total kinetic energy (TKE)equilibration time–scales as well as the time–scale for fluctuations.

Statistical Modeling of Stratified Two-Phase Flow

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
M. Benz ◽  
T. Schulenberg
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulence Model ◽  
Input Parameter ◽  
Wave Number ◽  
Energy Potential ◽  
Two Phase ◽  
Turbulent Length Scale ◽  
Inner Region ◽  
Velocity Turbulence

A new numerical model for stratified two-phase flows with wavy interface is derived in this study. Assuming an equilibrium condition between turbulent kinetic energy, potential energy, and surface energy, the turbulent length scale in the inner region of a two-layer turbulence approach can be described by a statistical model to account for the influence of the waves. The average wave number, which is an input parameter to this model, is taken from wave spectra. They can be predicted from a Boltzmann statistic of turbulent kinetic energy. The new turbulence model is compared with the two-phase k–ϵ turbulence model. Time-averaged flow properties calculated by the new approach, such as velocity, turbulence, and void profiles, are shown to be in good agreement with experimental data.

Data-driven stochastic model for cross-interacting processes with different time scales

2021 ◽  
Author(s):  
Andrey Gavrilov ◽  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Alexander Feigin
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Time Scales ◽  
Time Scale ◽  
Sampling Interval ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Middle Pleistocene ◽  
Model Based ◽  
Different Time Scales

<p>The problem of modeling interaction between processes with different time scales is very important in geoscience. In this report, we propose a new form of empirical evolution operator model based on the analysis of multiple time series representing processes with different time scales. We assume that the time series are given on the same time interval.</p><p>To construct the model, we extend the previously developed general form of nonlinear stochastic model based on artificial neural networks and designed for the case of time series with constant sampling interval [1]. This sampling interval is related to the main time scale of the process under consideration, which is described by the deterministic component of the model, while the faster time scales are modeled by its stochastic component, possibly depending on the system’s state. This model also includes slower processes in the form of weak time-dependence, as well as external forcing. The structure of the model is optimized using Bayesian approach [1]. The model has proven its efficiency in a number of applications [2-4].</p><p>The idea of modeling time series with different time scales is to formulate the above-described model individually for each time scale, and then to include the parameterized influence of the other time scales in it. Particularly, the influence of “slower” time series is included in the form of parameter trends, and the influence of “faster” time series is included by time-averaging their statistics. The algorithm and first results of comparison between the new model and the model without cross-interactions will be discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056).</p><p>1. Gavrilov, A., Loskutov, E., & Mukhin, D. (2017). Bayesian optimization of empirical model with state-dependent stochastic forcing. Chaos, Solitons & Fractals, 104, 327–337. http://doi.org/10.1016/j.chaos.2017.08.032</p><p>2. Mukhin, D., Kondrashov, D., Loskutov, E., Gavrilov, A., Feigin, A., & Ghil, M. (2015). Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models. Journal of Climate, 28(5), 1962–1976. http://doi.org/10.1175/JCLI-D-14-00240.1</p><p>3. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2019). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics, 52(3–4), 2199–2216. http://doi.org/10.1007/s00382-018-4255-7</p><p>4. Mukhin, D., Gavrilov, A., Loskutov, E., Kurths, J., & Feigin, A. (2019). Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition. Scientific Reports, 9(1), 7328. http://doi.org/10.1038/s41598-019-43867-3</p>

scholarly journals On the turbulent Prandtl number in homogeneous stably stratified turbulence

2010 ◽  
Vol 644 ◽  
pp. 359-369 ◽  
Author(s):  
SUBHAS K. VENAYAGAMOORTHY ◽  
DEREK D. STRETCH
Keyword(s):  
Kinetic Energy ◽  
Prandtl Number ◽  
Turbulent Kinetic Energy ◽  
Decay Time ◽  
Time Scale ◽  
Richardson Number ◽  
Stratified Flows ◽  
Length Scale ◽  
Stratified Turbulence ◽  
Turbulent Prandtl Number

In this paper, we derive a general relationship for the turbulent Prandtl number Prt for homogeneous stably stratified turbulence from the turbulent kinetic energy and scalar variance equations. A formulation for the turbulent Prandtl number, Prt, is developed in terms of a mixing length scale LM and an overturning length scale LE, the ratio of the mechanical (turbulent kinetic energy) decay time scale TL to scalar decay time scale Tρ and the gradient Richardson number Ri. We show that our formulation for Prt is appropriate even for non-stationary (developing) stratified flows, since it does not include the reversible contributions in both the turbulent kinetic energy production and buoyancy fluxes that drive the time variations in the flow. Our analysis of direct numerical simulation (DNS) data of homogeneous sheared turbulence shows that the ratio LM/LE ≈ 1 for weakly stratified flows. We show that in the limit of zero stratification, the turbulent Prandtl number is equal to the inverse of the ratio of the mechanical time scale to the scalar time scale, TL/Tρ. We use the stably stratified DNS data of Shih et al. (J. Fluid Mech., vol. 412, 2000, pp. 1–20; J. Fluid Mech., vol. 525, 2005, pp. 193–214) to propose a new parameterization for Prt in terms of the gradient Richardson number Ri. The formulation presented here provides a general framework for calculating Prt that will be useful for turbulence closure schemes in numerical models.

scholarly journals The Dynamics of Phase Locking

2005 ◽  
Vol 62 (8) ◽  
pp. 2952-2964 ◽  
Author(s):  
T. N. Krishnamurti ◽  
D. R. Chakraborty
Keyword(s):  
Kinetic Energy ◽  
Frequency Domain ◽  
Time Scales ◽  
Time Scale ◽  
Southern Oscillation ◽  
Phase Locking ◽  
Low Frequency ◽  
Transfer Energy ◽  
Energy Exchanges ◽  
Boundary Layer Dynamics

Abstract Many low-frequency phenomena such as the Madden–Julian oscillation (MJO) or the El Niño–Southern Oscillation (ENSO) exhibit rapid growth where they appear to be undergoing a phase locking with other time scales such as the annual cycle. The purpose of this paper is to illustrate an example of phase locking of two different time scales. In this instance it is shown that during such epochs of phase locking a large increase in nonlinear energy exchange occurs from one time scale to the other. This paper utilizes the ECMWF Re-Analysis (ERA-40) datasets for the year 2001 to examine this problem. This study is a sequel to a recent modeling study where the maintenance of the MJO time scale was examined from scale interactions, especially with synoptic-scale waves with ∼2–7 day periods. It was shown that a pair of waves on the synoptic time scale can satisfy certain selection rules and undergo triad interactions (kinetic energy to kinetic energy exchanges) and transfer energy. This present study illustrates the fact that during epochs of phase locking such nonlinear interactions can become very large, thus portraying the importance of phase locking. These explosive exchanges are shown from two perspectives: an approach based on kinetic energy exchanges in the frequency domain and another that invokes the boundary layer dynamics in the frequency domain.

Direct numerical simulation of turbulence modulation by particles in compressible isotropic turbulence

2017 ◽  
Vol 832 ◽  
pp. 438-482 ◽  
Author(s):  
Qi Dai ◽  
Kun Luo ◽  
Tai Jin ◽  
Jianren Fan
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Rotational Motion ◽  
Isotropic Turbulence ◽  
Volume Fraction ◽  
Stokes Number ◽  
Systematic Investigation ◽  
Turbulence Modulation ◽  
Physical Mechanisms ◽  
Preferential Concentration

In this paper, a systematic investigation of turbulence modulation by particles and its underlying physical mechanisms in decaying compressible isotropic turbulence is performed by using direct numerical simulations with the Eulerian–Lagrangian point-source approach. Particles interact with turbulence through two-way coupling and the initial turbulent Mach number is 1.2. Five simulations with different particle diameters (or initial Stokes numbers, $St_{0}$) are conducted while fixing both their volume fraction and particle densities. The underlying physical mechanisms responsible for turbulence modulation are analysed through investigating the particle motion in the different cases and the transport equations of turbulent kinetic energy, vorticity and dilatation, especially the two-way coupling terms. Our results show that microparticles ($St_{0}\leqslant 0.5$) augment turbulent kinetic energy and the rotational motion of fluid, critical particles ($St_{0}\approx 1.0$) enhance the rotational motion of fluid, and large particles ($St_{0}\geqslant 5.0$) attenuate turbulent kinetic energy and the rotational motion of fluid. The compressibility of the turbulence field is suppressed for all the cases, and the suppression is more significant if the Stokes number of particles is close to 1. The modifications of turbulent kinetic energy, the rotational motion and the compressibility are all related with the particle inertia and distributions, and the suppression of the compressibility is attributed to the preferential concentration and the inertia of particles.

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