Random Velocity Field Research Articles

Stochastic simulation of the high-frequency wave propagation in a random medium

A stochastic model is proposed to simulate the propagation of an acoustic wave in a random medium characterized by weak velocity fluctuations. After the acoustic wave propagation through a random velocity field, the propagation field becomes itself a random field. In the developed stochastic approach, the wave field in such random medium is modeled by the combination of the wave field in a mean homogeneous medium and of fluctuation corrections. These corrections are provided by a random field generator whose inputs are the statistical moments of the travel times. Using this stochastic modeling, the propagation of both an incident plane wave and an acoustic realistic beam generated by a real transducer in a random velocity field is calculated and the corresponding simulations are validated by comparison with those obtained with a deterministic model based on geometrical optics.

Stochastic dynamo in critical situations

Based on the functional method of consecutive approximations, we consider the problem of magnetic field excitation (stochastic dynamo) by a random velocity field with a finite temporal correlation radius. In critical situations, in the first (diffusion) approximation, the Lyapunov characteristic parameter of the magnetic field energy vanishes. This implies the absence of structure formation (clustering) in realizations of the magnetic field in that approximation. Critical situations occur in problems of magnetic field diffusion in an equilibrium thermal and random pseudoequilibrium and acoustic (in the absence of dissipation) velocity fields. The sign of the Lyapunov characteristic parameter in the second-order approximation determines the possibility of clustering of the magnetic field energy. We show that energy clustering does not occur in a thermal velocity field. In the cases of pseudoequilibrium and acoustic velocity fields, clustering occurs with probability one, i.e., in almost every realization. We evaluate the characteristic time for clustering to be established.

Renormalization Group Analysis of Nonlinear Diffusion Equations with Time Dependent Coefficients and Marginal Perturbations

In this paper we use a Renormalization Group (RG) method to study the long-time asymptotics of nonlinear diffusion equations with time-dependent diffusion coefficients and nonlinearities which are marginal (or critical) with respect to the RG operator. These equations describe the time evolution of the average concentration of a passive scalar being advected by a random velocity field. We prove that, besides the expected diffusive behavior, there is an extra logarithmic correction which is the imprint of the critical nonlinearity.

Reduction of mean-square advection in turbulent passive scalar mixing

Direct numerical simulation data show that the variance of the coupling term in passive scalar advection by a random velocity field is smaller than it would be if the velocity and scalar fields were statistically independent. This effect is analogous to the “depression of nonlinearity” in hydrodynamic turbulence. We show that the trends observed in the numerical data are qualitatively consistent with the predictions of closure theories related to Kraichnan's direct interaction approximation. The phenomenon is demonstrated over a range of Prandtl numbers. In the inertial-convective range the depletion is approximately constant with respect to wavenumber. The effect is weaker in the Batchelor range.

Effects of turbulent mixing on critical behaviour: renormalization-group analysis of the Potts model

The critical behaviour of a system, subjected to strongly anisotropic turbulent mixing, is studied by means of the field-theoretic renormalization group. Specifically, the relaxational stochastic dynamics of a non-conserved multicomponent order parameter of the Ashkin–Teller–Potts model, coupled to a random velocity field with prescribed statistics, is considered. The velocity is taken to be Gaussian, white in time, with a correlation function of the form ∝δ(t − t′)/|k⊥|d − 1 + ξ, where k⊥ is the component of the wave vector, perpendicular to the distinguished direction (‘direction of the flow’)—the d-dimensional generalization of the ensemble was introduced by Avellaneda and Majda (1990 Commun. Math. Phys. 131 381) within the context of passive scalar advection. This model can describe a rich class of physical situations. It is shown that, depending on the values of the parameters that define the self-interaction of the order parameter and the relation between the exponent ξ and the space dimension d, the system exhibits various types of large-scale scaling behaviour, associated with different infrared attractive fixed points of the renormalization-group equations. In addition to known asymptotic regimes (critical dynamics of the Potts model and passively advected field without self-interaction), the existence of a new, non-equilibrium and strongly anisotropic, type of critical behaviour (universality class) is established, and the corresponding critical dimensions are calculated to the leading order of the double expansion in ξ and ε = 6 − d (one-loop approximation). The scaling appears to be strongly anisotropic in the sense that the critical dimensions related to the directions parallel and perpendicular to the flow are essentially different.

Constraints on the ICM velocity power spectrum from the X-ray lines width and shift

Future X-ray observations of galaxy clusters by high spectral resolution missions will provide spatially resolved measurements of the energy and width for the brightest emission lines in the intracluster medium (ICM) spectrum. In this paper we discuss various ways of using these high resolution data to constrain velocity power spectrum in galaxy clusters. We argue that variations of these quantities with the projected distance R in cool core clusters contain important information on the velocity field length scales in the ICM. The effective length $l_{\rm eff}$ along the line of sight, which provides dominant contribution to the line flux, increases with R, allowing one to probe the amplitude of the velocity variations at different spatial scales. In particular, we show that the width of the line as a function of R is closely linked to the structure function of the 3D velocity field. Yet another easily obtainable proxy of the velocity field length scales is the ratio of the amplitude of the projected velocity field (line energy) variations to the dispersion of the velocity along the line of sight (line width). Finally the projected velocity field can be easily converted into 3D velocity field, especially for clusters like Coma with an extended flat core in the surface brightness. Under assumption of a homogeneous isotropic Gaussian 3D velocity field we derived simple expressions relating the power spectrum of the 3D velocity field (or structure function) and the observables. The uncertainties in the observables, caused by stochastic nature of the velocity field, are estimated by making multiple realizations of the random Gaussian velocity field and evaluating the scatter in observables. If large scale motions are present in the ICM these uncertainties may dominate the statistical errors of line width and shift measurements.

Calculating the macrodispersion coefficient of the ensemble averaged solute transport equation in the discrete domain

AbstractRecognizing the spatial heterogeneity of hydraulic parameters, many researchers have studied the solute transport by both groundwater and channel flow in a stochastic framework. One of the methodologies used to up‐scale the stochastic solute transport equation, from a point‐location scale to a grid scale, is the cumulant expansion method combined with the calculus for the time‐ordered exponential and the calculus for the Lie operator. When the point‐location scale transport equation is scaled up to the grid scale, using the cumulant expansion method, a new dispersion coefficient emerges in the dispersive term of the solute transport equation in addition to the molecular dispersion coefficient. This velocity driven dispersion is called ‘macrodispersion’. The macrodispersion coefficient is the integral function of the time‐ordered covariance of the random velocity field. The integral is calculated over a Lagrangian trajectory of the flow. The Lagrangian trajectory depends on the following: (i) the spatial origin of the particle; (ii) the time when the macrodispersion is calculated; and (iii) the mean velocity field along the trajectory itself. The Lagrangian trajectory is a recursive function of time because the location of the particle along the trajectory at a particular time depends on the location of the particle at the previous time. This recursive functional form of the Lagrangian trajectory makes the calculation of the macrodispersion coefficient difficult. Especially for the unsteady, spatially non‐stationary, non‐uniform flow field, the macrodispersion coefficient is a highly complex expression and, so far, calculated using numerical methods in the discrete domains. Here, an analytical method was introduced to calculate the macrodispersion coefficient in the discrete domain for the unsteady and steady, spatially non‐stationary flow cases accurately and efficiently. This study can fill the gap between the theory of the ensemble averaged solute transport model and its numerical implementations. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical theories versus statistical mechanics of homogeneous isotropic turbulence

The present paper is concerned with the “closure problem of turbulence,” which constituted the main subject of the Session on the “Energy transfer in homogeneous turbulence” in the last Turbulence Colloquium Marseille (TCM) 1961. It is usual in statistical theory of turbulence to express the random velocity field of turbulence in terms of the velocity correlations of various orders and deal with the set of dynamical equations governing these velocity correlations. This way is, however, associated with the difficulty of unclosedness since each equation involves two velocity correlations of different orders as unknowns according to the nonlinearity of the Navier–Stokes equation, and hence we have to introduce some relationship between the velocity correlations of different orders in order to make the set of equations closed. Among various closuretheories proposed so far, the quasi-normal closure by Proudman and Reid (Proudman and W.H. Reid, Phil. Trans. R. Soc. Lond. A 247 (1954), pp. 163–189) and Tatsumi (T. Tatsumi, Proc. Roy. Soc. Lond. A 239 (1957), p. 16.) and the direct-interaction closure by Kraichnan (R.H. Kraichnan, J. Fluid Mech. 5 (1959), pp. 497–543.) have been taken up in the Session and critical survey and evaluation have been made on these theories. Apart from particular merits and demerits of the theories, it has been recognized that they still remain in approximate levels with either partial or no fulfillment of Kolmogorov’s nonzero inertial energy-dissipation hypothesis. More recently, Tatsumi (T. Tatsumi, in Geometry and Statistics of Turbulence, T. Kambe ed., Kluwer, 2001, pp. 3–12.) formulated statistical mechanics of fluid turbulence in terms of the Lundgren–Monin equations (T.S. Lundgren, Phys. Fluids, 10 (1967), pp. 969–975; A.S. Monin, PMM J. Appl. Math. Mech. 31 (1967), pp. 1057–1068.) for the multipoint velocity distributions of turbulence and the cross-independence closure of these equations. It has been shown by Tatsumi (T. Tatsumi, J. Fluid Mech. 670 (2011), pp. 365–403.) that this closure provides us with exactclosure of the Lundgren–Monin equations and thus exact solution to any problem concerning the mean velocity products.

Statistical Fluid Mechanics and Statistical Mechanics of Fluid Turbulence

Turbulence in a fluid has two mutually contradictory aspects, that is, the apparent ran-domeness as a whole and the intrinsic determinicity due to the fluid-dynamical equations. In the traditional approarch to this subject, the velocity field of turbulence has been represented by the mean products of the turbulent velocities at several points and a time, and the equations governing the dynamical system composed of the mean velocity products are solved to obtain the mean velocity products as the deterministic functions in time. The works along this approach constitute the main body of turbulence research as outlined by Monin and Yaglom (1975) under the title of 'Statistical Fluid Mechanics'. It should be noted, however, that this approach has the diffculty of unclosedness of the equations for the mean velocity products due to the nonlinearity of the Navier-Stokes equations and an appropriate closure hypothesis is still awaited.In the statistical approach to turbulence, on the other hand, the random velocity field of turbulence is represented by the joint-probability distributions of the multi-point turbulent velocities. This approach has been taken by Lundgren (1967) and Monin (1967) independently, and the system of equations for the probability distributions of the multi-point velocities has been formulated. This system of equations is also unclosed, but in this case the problem is much easier to deal with since the unclosedness is not due to the nonlinearity but to the higher-order derivatives in the viscous term of the Navier-Stokes equations. Tatsumi (2001) proposed the cross-independence closure hypothesis for this purpose and proved its validity for homogeneous isotropic turbulence. More recently, the theory has extended by Tatsumi (2011) to general inhomogeneous turbulence and the closure is shown to provide an exact closure. This theory is outlined and discussed in the later part of this paper.

On the linear advection equation subject to random velocity fields

This paper deals with the random linear advection equation for which the time-dependent velocity and the initial condition are independent random functions. Expressions for the density and joint density functions of the solution are given. We also verify that in the Gaussian time-dependent velocity case the probability density function of the solution satisfies a convection–diffusion equation with a time-dependent diffusion coefficient. Some exact examples are presented.

Orthogonal expansion of Gaussian wind velocity field and PDEM-based vibration analysis of wind-excited structures

An effective procedure for simulation of random wind velocity field by the orthogonal expansion method is proposed in this paper. The procedure starts with decomposing the fluctuating wind velocity field into a product of a stochastic process and a random field, which represent the time property and the spatial correlation property of wind velocity fluctuations, respectively. By an innovative orthogonal expansion technology, the stochastic process for wind velocity fluctuations may be represented as a finite sum of deterministic time functions with corresponding uncorrelated random coefficients. Similarly, the random field can be expressed as a combination form with only a few random variables by the Karhunen–Loeve decomposition. This approach actually simulates the wind velocity field with stochastic functions other than methods such as spectral representation and proper orthogonal decomposition. In the second part of the paper, the probability density evolution method (PDEM) is employed to predict the stochastic dynamic response of structures subjected to wind excitations. In the PDEM, a completely uncoupled one-dimensional partial differential equation, the generalized density evolution equation, plays a central role in governing the stochastic responses of structures. The solution of this equation will give rise to instantaneous probability density function of the responses. Finally, the accuracy and effectiveness of the approach in representing the random wind velocity field and PDEM-based dynamic response of wind-excited building are investigated.

Study of anomalous kinetics of the annihilation reaction A + A → Ø

Using the perturbative renormalization group, we study the influence of a random velocity field on the kinetics of the single-species annihilation reaction A+A->0 at and below its critical dimension d_c=2. We use the second-quantization formalism of Doi to bring the stochastic problem to a field-theoretic form. We investigate the reaction in the vicinity of the space dimension d=2 using a two-parameter expansion in $\epsilon$ and $\Delta$, where $\epsilon$ is the deviation from the Kolmogorov scaling parameter and $\Delta$ is the deviation from the space dimension d=2. We evaluate all the necessary quantities, including fixed points with their regions of stability, up to the second order of the perturbation theory.

A new level-set based approach to shape and topology optimization under geometric uncertainty

Geometric uncertainty refers to the deviation of the geometric boundary from its ideal position, which may have a non-trivial impact on design performance. Since geometric uncertainty is embedded in the boundary which is dynamic and changes continuously in the optimization process, topology optimization under geometric uncertainty (TOGU) poses extreme difficulty to the already challenging topology optimization problems. This paper aims to solve this cutting-edge problem by integrating the latest developments in level set methods, design under uncertainty, and a newly developed mathematical framework for solving variational problems and partial differential equations that define mappings between different manifolds. There are several contributions of this work. First, geometric uncertainty is quantitatively modeled by combing level set equation with a random normal boundary velocity field characterized with a reduced set of random variables using the Karhunen---Loeve expansion. Multivariate Gauss quadrature is employed to propagate the geometric uncertainty, which also facilitates shape sensitivity analysis by transforming a TOGU problem into a weighted summation of deterministic topology optimization problems. Second, a PDE-based approach is employed to overcome the deficiency of conventional level set model which cannot explicitly maintain the point correspondences between the current and the perturbed boundaries. With the explicit point correspondences, shape sensitivity defined on different perturbed designs can be mapped back to the current design. The proposed method is demonstrated with a bench mark structural design. Robust designs achieved with the proposed TOGU method are compared with their deterministic counterparts.

Scaling laws of peripheral mixing of passive scalar in a wall-shear layer

The scaling laws governing the concentration moments of a passive scalar released from a ground-level localized source in a neutrally stratified wall-shear layer are investigated using a theoretical framework recently formulated by Lebedev and Turitsyn [Phys. Rev. E 69, 036301 (2004)]. For the current application, this theoretical framework is generalized from the smooth random velocity field applicable in the viscous sublayer to the nonsmooth random velocity field that applies to the bulk of the wall-shear layer. Theoretical relationships for the passive scalar concentration moments are compared to a water-channel simulation of turbulent diffusion from a ground-level source in a wall-shear layer. The diffusion measurements in the wall-shear layer are shown to be consistent with the theoretical description and also imply the robustness of the identified scaling laws for the scalar concentration moments.

Distribution- Versus Correlation-Induced Anomalous Transport in Quenched Random Velocity Fields

We study mechanisms of anomalous transport in quenched random media. Broad disorder point distributions and strong disorder correlations cause anomalous transport and can lead to the same anomalous scaling laws for the mean and variance of the particle displacements. The respective mechanisms, however, are fundamentally different. This difference is reflected in the spatial particle densities and first passage time distributions, which provide an indicator to identify the origins of anomalous transport.

Probability density functions for passive scalars dispersed in random velocity fields

Spatial and temporal heterogeneity of ambient natural environments play a significant role in large scale transport phenomena. Uncertainty about spatio‐temporal fluctuations in system parameters (e.g., flow velocity) make deterministic predictions of macroscopic system states (e.g., solute concentration) elusive. Distributions of system states generally exhibit highly non‐Gaussian behavior, which cannot be captured solely by the corresponding mean and variance. Instead, these features of transport are described by the probability density function (PDF) of a system state, e.g., the PDF of concentration at a certain point in space and time. We study the PDF of a passive scalar that disperses in a random velocity field. We derive an explicit map between the velocity distribution and the scalar PDF, and obtain approximate solutions for the PDF of the normalized scalar. These solutions enable one to quantify explicitly the impact of dispersion on the evolution of the passive scalar PDF without recurrence to classical closure approximations used in mixing models.

Mixing-dynamics of a passive scalar in a three-dimensional microchannel

The mixing of a diffusive passive-scalar driven by electro-osmotic fluid motion in a micro-channel is studied numerically. Secondary time-dependent periodic or random electric fields, orthogonal to the main stream, are applied to generate cross-sectional mixing. This investigation focuses on the mixing dynamics and its dependence on the frequency (period) of the driving mechanism. For periodic flows, the probability density function (PDF) of the scaled concentration settles into a self-similar curve showing spatially repeating patterns. In contrast, for random flows there is a lack of self-similarity in the PDF for the time interval considered. An exponential decay of the variance of the concentration, and associated moments, is found to exist for both periodic and random velocity fields. The numerical results also indicate that measures of chaoticity (in a deterministic chaotic system) decay exponentially in the frequency – at large frequencies – in agreement with the theory.

Extreme Events and the General Circulation: Observations and Stochastic Model Dynamics

Abstract This study explores the dynamical role of non-Gaussian potential vorticity variability (extreme events) in the zonally averaged circulation of the atmosphere within a stochastic framework. First the zonally averaged skewness and kurtosis patterns of relative and potential vorticity anomalies from NCEP–NCAR reanalysis data are presented. In the troposphere, midlatitude regions of near-zero skewness coincide with regions of maximum variability. Equatorward of the Northern Hemisphere storm track positive relative/potential vorticity skewness is observed. Poleward of the same storm track the vorticity skewness is negative. In the Southern Hemisphere the relation is reversed, resulting in negative relative/potential vorticity skewness equatorward, and positive skewness poleward of the storm track. The dynamical role of extreme events in the zonally averaged general circulation is then explored in terms of the potential enstrophy budget by linking eddy enstrophy fluxes to a stochastic representation of non-Gaussian potential vorticity anomalies. The stochastic model assumes that potential vorticity anomalies are advected by a random velocity field. The assumption of stochastic advection allows for a closed expression of the meridional enstrophy flux: the potential enstrophy flux is proportional to the potential vorticity skewness. There is some evidence of this relationship in the observations. That is, potential enstrophy fluxes might be linked to non-Gaussian potential vorticity variability. Thus, extreme events may presumably play an important role in the potential enstrophy budget and the related general circulation of the atmosphere.

A Fast Explicit Operator Splitting Method for Passive Scalar Advection

The dispersal and mixing of scalar quantities such as concentrations or thermal energy are often modeled by advection-diffusion equations. Such problems arise in a wide variety of engineering, ecological and geophysical applications. In these situations a quantity such as chemical or pollutant concentration or temperature variation diffuses while being transported by the governing flow. In the passive scalar case, this flow prescribed and unaffected by the scalar. Both steady laminar and complex (chaotic, turbulent or random) time-dependent flows are of interest and such systems naturally lead to questions about the effectiveness of the stirring to disperse and mix the scalar. The development of reliable numerical methods for advection-diffusion equations is crucial for understanding their properties, both physical and mathematical. In this paper, we extend a fast explicit operator splitting method, recently proposed in (A. Chertock, A. Kurganov, G. Petrova, Int. J. Numer. Methods Fluids 59:309---332, 2009), for solving deterministic convection-diffusion equations, to the problems with random velocity fields and singular source terms. A superb performance of the method is demonstrated on several two-dimensional examples.

Spatially inhomogeneous transition probabilities as memory effects for diffusion in statistically homogeneous random velocity fields

Whenever one uses translation invariant mean Green's functions to describe the behavior in the mean and to estimate dispersion coefficients for diffusion in random velocity fields, the spatial homogeneity of the transition probability of the transport process is implicitly assumed. This property can be proved for deterministic initial conditions if, in addition to the statistical homogeneity of the space-random velocity field, the existence of unique classical solutions of the transport equations is ensured. When uniqueness condition fails and translation invariance of the mean Green's function cannot be assumed, as in the case of nonsmooth samples of random velocity fields with exponential correlations, asymptotic dispersion coefficients can still be estimated within an alternative approach using the Itô equation. Numerical simulations confirm the predicted asymptotic behavior of the coefficients, but they also show their dependence on initial conditions at early times, a signature of inhomogeneous transition probabilities. Such memory effects are even more relevant for random initial conditions, which are a result of the past evolution of the process of diffusion in correlated velocity fields, and they persist indefinitely in case of power law correlations. It was found that the transition probabilities for successive times can be spatially homogeneous only if a long-time normal diffusion limit exits. Moreover, when transition probabilities, for either deterministic or random initial states, are spatially homogeneous, they can be explicitly written as Gaussian distributions.