Probability Density Function Of Displacement Research Articles

Non-Gaussian, transiently anomalous, and ergodic self-diffusion of flexible dumbbells in crowded two-dimensional environments: Coupled translational and rotational motions.

We employ Langevin-dynamics simulations to unveil non-Brownian and non-Gaussian center-of-mass self-diffusion of massive flexible dumbbell-shaped particles in crowded two-dimensional solutions. We study the intradumbbell dynamics of the relative motion of the two constituent elastically coupled disks. Our main focus is on effects of the crowding fraction ϕ and of the particle structure on the diffusion characteristics. We evaluate the time-averaged mean-squared displacement (TAMSD), the displacement probability-density function (PDF), and the displacement autocorrelation function (ACF) of the dimers. For the TAMSD at highly crowded conditions of dumbbells, e.g., we observe a transition from the short-time ballistic behavior, via an intermediate subdiffusive regime, to long-time Brownian-like spreading dynamics. The crowded system of dimers exhibits two distinct diffusion regimes distinguished by the scaling exponent of the TAMSD, the dependence of the diffusivity on ϕ, and the features of the displacement-ACF. We attribute these regimes to a crowding-induced transition from viscous to viscoelastic diffusion upon growing ϕ. We also analyze the relative motion in the dimers, finding that larger ϕ suppress their vibrations and yield strongly non-Gaussian PDFs of rotational displacements. For the diffusion coefficients D(ϕ) of translational and rotational motion of the dumbbells an exponential decay with ϕ for weak and a power-law variation D(ϕ)∝(ϕ-ϕ^{★})^{2.4} for strong crowding is found. A comparison of simulation results with theoretical predictions for D(ϕ) is discussed and some relevant experimental systems are overviewed.

On the transfer of theoretical multipole parameters for restoring static electron density and revealing and treating atomic anharmonic motion. Features of chemical bonding in crystals of an isocyanuric acid derivative

The crystal and electronic structure of an isocyanuric acid derivative was studied by high-resolution single-crystal X-ray diffraction within the Hansen–Coppens multipole formalism. The observed deformation electron density shows signs of thermal smearing. The experimental picture meaningfully assigned to the consequences of unmodelled anharmonic atomic motion. Straightforward simultaneous refinement of all parameters, including Gram–Charlier coefficients, resulted in more significant distortion of apparent static electron density, even though the residual density became significantly flatter and more featureless. Further, the method of transferring multipole parameters from the model refined against theoretical structure factors as an initial guess was employed, followed by the subsequent block refinement of Gram–Charlier coefficients and the other parameters. This procedure allowed us to appropriately distinguish static electron density from the contaminant smearing effects of insufficiently accounted atomic motion. In particular, some covalent bonds and the weak π...π interaction between isocyanurate moieties were studied via the mutual penetration of atomic-like kinetic and electrostatic potential φ-basins with complementary atomic ρ-basins. Further, local electronic temperature was applied as an advanced descriptor for both covalent bonds and noncovalent interactions. Total probability density function (PDF) of nuclear displacement showed virtually no negative regions close to and around the atomic nuclei. The distribution of anharmonic PDF to a certain extent matched the residual electron density from the multipole model before anharmonic refinement. No signs of disordering of the sulfonyl group hidden in the modelled anharmonic motion were found in the PDF.

Uncertainty Estimation for Ensemble Particle Image Velocimetry

We present a novel approach to estimate the uncertainty in ensemble particle image velocimetry (PIV) measurements. Ensemble PIV is widely used when the cross-correlation signal-to-noise ratio (SNR) is insufficient to perform a reliable instantaneous velocity measurement. Despite the utility of ensemble PIV, uncertainty quantification for this type of measurement has not been studied. The existing uncertainty quantification algorithms for PIV are developed and used only for instantaneous PIV measurement and do not account for the improved SNR in ensemble PIV. Existing instantaneous uncertainty quantification methods can be divided into direct and indirect categories. Indirect methods require calibration based on the effect of various image parameters (such as noise, particle size, density, velocity gradient, etc.) on the correlation SNR. Indirect methods have not been calibrated for error sources relevant in an Ensemble PIV measurement. Also, they have lower sensitivity to the error sources compared to direct approaches. Direct methods, such as the moment of correlation (MC) and Image Matching (IM), find the uncertainty based on the images and correlation planes without any calibration and are more reliable (Bhattacharya et al., 2018; Sciacchitano et al., 2013). Ensemble PIV is based on ensemble correlations; therefore, MC, which uses the generalized cross-correlation (GCC) plane as a measure of uncertainty, is the most suitable method to be modified to be applicable for the ensemble PIV. The GCC plane is the inverse Fourier transform of the phase correlation and represents the probability density function (PDF) of particles’ displacements (Bhattacharya et al., 2018; Eckstein and Vlachos, 2009). We replaced instantaneous GCC with ensemble GCC and modified MC’s normalization factor to account for the number of ensembles. The MC’s primary limitation is that it assumes a Gaussian shape for the PDF of displacements and estimate the standard deviation of the underlying PDF using a fitted Gaussian. However, the PDF deviates from Gaussian distribution due to velocity gradient or non-Gaussian random displacements. Therefore, MC’s reliability and applicability are reduced for flow fields with non-Gaussian PDFs. Also, our analysis shows that ensemble MC consistently underestimates the uncertainty. So, a generalized and reliable method for uncertainty quantification for ensemble PIV is needed.

Stochastic inference of surface-induced effects using Brownian motion

Brownian motion in confinement and at interfaces is a canonical situation, encountered from fundamental biophysics to nanoscale engineering. Using the Lorenz-Mie framework, we optically record the thermally-induced tridimensional trajectories of individual microparticles, within salty aqueous solutions, in the vicinity of a rigid wall, and in the presence of surface charges. We construct the time-dependent position and displacement probability density functions, and study the non-Gaussian character of the latter which is a direct signature of the hindered mobility near the wall. Based on these distributions, we implement a novel, robust and self-calibrated multifitting method, allowing for the thermal-noise-limited inference of diffusion coefficients spatially-resolved at the nanoscale, equilibrium potentials, and forces at the femtoNewton resolution.

Approximate Fokker–Planck–Kolmo-gorov equation analysis for asymmetric multistable energy harvesters excited by white noise

Due to the broadband response characteristics at low levels of excitations, nonlinear multistable systems have garnered a great deal of attention in the area of energy harvesting. Moreover, various performance enhancement strategies of multistable harvesters have been proposed and discussed extensively for systems with perfectly symmetric potentials. However, it is very difficult or even impossible in practice to modulate a nonlinear system with completely symmetric potentials. Therefore, this paper investigates the stochastic response characteristics of asymmetric potential multistable harvesters excited by Gaussian white noise. Approximate Fokker–Planck–Kolmogorov equation for multistable harvester is provided, and solved by the method of detailed balance. Numerical simulations are carried out to characterize the probability distribution and power output of the response of the harvesters. Results indicate that the theoretical method could well predict the response probability distribution of the systems under white noise excitations. To be more specific, the probability distribution of velocity response at a given excitation level is not affected by the asymmetry of potentials, while the probability density function of displacement is very dependent on the shape of potentials. In terms of the power output, the numerical results agree well with the theoretical results. However, the error between numerical and theoretical results increases with the increase in the number of the steady states and the degree of asymmetry of the harvesters.

Exact distributions of the maximum and range of random diffusivity processes

We study the extremal properties of a stochastic process x t defined by the Langevin equation , in which ξ t is a Gaussian white noise with zero mean and D t is a stochastic ‘diffusivity’, defined as a functional of independent Brownian motion B t . We focus on three choices for the random diffusivity D t : cut-off Brownian motion, D t ∼ Θ(B t ), where Θ(x) is the Heaviside step function; geometric Brownian motion, D t ∼ exp(−B t ); and a superdiffusive process based on squared Brownian motion, . For these cases we derive exact expressions for the probability density functions of the maximal positive displacement and of the range of the process x t on the time interval t ∈ (0, T). We discuss the asymptotic behaviours of the associated probability density functions, compare these against the behaviour of the corresponding properties of standard Brownian motion with constant diffusivity (D t = D 0) and also analyse the typical behaviour of the probability density functions which is observed for a majority of realisations of the stochastic diffusivity process.

Dynamic Response of Asymmetric and Nonlinear Packaging System under Random Excitation

This paper focuses on the dynamic response of three asymmetric and nonlinear packaging systems (ANPSs) of practical transport package under random excitation. Next to presenting the displacement probability density functions (PDFs) of ANPSs derived through FPK equations, the influences of the excitation grades and the characteristic parameters (such as the damping, the nonlinear stiffness, and the strain) of packaging cushion materials on the response are discussed in detail. Then, the generalized PDFs of the response peaks are defined and examined by three common distributions. The investigation shows that “inverse excitation cushion factors” (IECFs) have a significance on effecting the displacement PDFs approaching different distributions. Most PDFs of the unilateral response peaks approach Rayleigh distributions and present non-Gaussian characters. The present methods are verified through the validation numerical solutions. Furthermore, the application of the scheme for fatigue damage evaluation of the transport package is carried out.

Estimation of the probability density function of random displacements from images

We introduce an image-based algorithm to find the probability density function (PDF) of particle displacements from a sequence of images. Conventionally methods based on cross correlation (CC) of image ensembles estimate the standard deviation of an assumed Gaussian PDF from the width of the CC peak. These methods are subject to limiting assumptions that the particle intensity profile and distribution of particle displacements are both Gaussian. Here, we introduce an approach to image-based probability estimation of displacement (iPED) without making any assumptions about the shape of particles' intensity profile or the PDF of the displacements. In addition, we provide a statistical convergence criterion for iPED to achieve an accurate estimate of the underlying PDF. We compare iPED's performance with the previous CC method for both Gaussian and non-Gaussian particle intensity profiles undergoing Gaussian or non-Gaussian processes. We validate iPED using synthetic images and show that it accurately resolves the PDF of particle displacements with no underlying assumptions. Finally, we demonstrate the application of iPED to real experimental data sets and evaluate its performance. In conclusion, this work presents a method for the estimation of the probability density function of random displacements from images. This method is generalized and independent of any assumptions about the underlying process and is applicable to any moving objects of any arbitrary shape.

Identification of nonlinear aerodynamic damping of wind-excited flexible structures by curve-fitting non-Gaussian response probability density function

This study presents a new approach for identification of nonlinear aerodynamic damping of wind-excited flexible structures from stochastic response time history that can be modeled as the output of a single-degree-of-freedom system under a white noise excitation. The amplitude-dependent aerodynamic damping ratio is described as a polynomial function of vibration displacement or velocity. Based on the equivalent nonlinear equation approach, the probability density function (PDF) of displacement can be given in terms of damping model parameters, which generally follows a hardening non-Gaussian distribution. It is proposed to identify the damping model parameters by curve-fitting displacement PDF determined from the response data using a nonlinear least-square optimization technique. Firstly, the stochastic response of a tall building with a known nonlinear aerodynamic model is simulated and the performance of the proposed approach is investigated. This method is then applied to a square-shaped tall building model and a bridge deck section using wind tunnel measured data. Finally, it is used for a traffic-signal-support structure using field measurement response data. The results are also compared with those from other existing approaches. The proposed approach permits determination of nonlinear aerodynamic damping from single stochastic response history and is very easy to use in practice.

Probabilistic analysis on parametric random vibration of a marine riser excited by correlated Gaussian white noises

A path integration method is used to discuss the stochastic response of a marine riser excited parametrically and externally by correlated Gaussian white noises. On the basis of the nonlinear vibration equation of a marine riser, the first-order vibration mode is studied and its governing equation is formulated. The response probability density function (PDF) is further developed by a path integration method. During the solution procedure, the Gaussian closure method and the short-time Gaussian approximation method are used to develop the transition probability density function. The results of different time intervals are compared with the results of Monte-Carlo simulation (MCS) to determine the adequate time interval. After that, the path integration method with a Gauss–Legendre scheme is used to obtain the stationary PDF solution of displacement and velocity. The path integration solutions (PIS) are compared with those of the equivalent linearization method (EQL) and MCS. Five demonstrative cases are analyzed to evaluate the effectiveness of the path integration method. The influences of nonlinearity and excitations are considered. It shows that the results of PIS coincide better with the results of MCS than those of EQL. The analysis shows that parametric excitation causes the softening behavior of the PDF distribution compared with those of EQL. The increase of excitation correlation results in the nonsymmetrical PDF distribution of response. The increase of nonlinearity in displacement can lead the PDF of displacement to showing a hardening behavior.

Inference for cluster point processes with over- or under-dispersed cluster sizes

Cluster point processes comprise a class of models that have been used for a wide range of applications. While several models have been studied for the probability density function of the offspring displacements and the parent point process, there are few examples of non-Poisson distributed cluster sizes. In this paper, we introduce a generalization of the Thomas process, which allows for the cluster sizes to have a variance that is greater or less than the expected value. We refer to this as the cluster sizes being over- and under-dispersed, respectively. To fit the model, we introduce minimum contrast methods and a Bayesian MCMC algorithm. These are evaluated in a simulation study. It is found that using the Bayesian MCMC method, we are in most cases able to detect over- and under-dispersion in the cluster sizes. We use the MCMC method to fit the model to nerve fiber data, and contrast the results to those of a fitted Thomas process.

Stochastic dynamics of the FitzHugh-Nagumo neuron model through a modified Van der Pol equation with fractional-order term and Gaussian white noise excitation

The stochastic response of the FitzHugh-Nagumo model is addressed using a modified Van der Pol (VDP) equation with fractional-order derivative and Gaussian white noise excitation. Via the generalized harmonic balance method, the term related to fractional derivative is splitted into the equivalent quasi-linear dissipative force and quasi-linear restoring force, leading to an equivalent VDP equation without fractional derivative. The analytical solutions for the equivalent stochastic equation are then investigated through the stochastic averaging method. This is thereafter compared to numerical solutions, where the stationary probability density function (PDF) of amplitude and joint PDF of displacement and velocity are used to characterized the dynamical behaviors of the system. A satisfactory agreement is found between the two approaches, which confirms the accuracy of the used analytical method. It is also found that changing the fractional-order parameter and the intensity of the Gaussian white noise induces P-bifurcation.

Comparative study of stochastic slope stability analysis based on conditional and unconditional random field

Abstract In this study, by coupling the conditional and unconditional random field with finite element methods, the stability of a real slope is investigated. In the first method, which is used for the case of an Unconditional Random Field (URF), the analysis is carried out similar to the approach of the Random Finite Element Method (RFEM) using the mean and standard deviation (std.) of soil parameters derived from all boreholes. In the second, which is a Conditional Random Field (CRF), the soil parameters are mapped onto the selected analysis section and are then predicted on the section mesh elements based on geostatistical estimations. As outputs of the stochastic analysis, the probability density functions of safety factor and maximum lateral displacement of the slope from the two methods are compared with each other. The results of this case study indicate that the use of the CRF affects not only the std. of the safety factor and maximum lateral displacement distribution but can also cause variations in the mean value. Furthermore, the system reliability index of the slope stability decreases when the analysis is done based on the URF.

Continuous-time random walks under power-law resetting.

We study continuous-time random walks (CTRW) with power-law distribution of waiting times under resetting which brings the walker back to the origin, with a power-law distribution of times between the resetting events. Two situations are considered. Under complete resetting, the CTRW after the resetting event starts anew, with a new waiting time, independent of the prehistory. Under incomplete resetting, the resetting of the coordinate does not influence the waiting time until the next jump. We focus on the behavior of the mean-squared displacement (MSD) of the walker from its initial position, on the conditions under which the probability density functions of the walker's displacement show universal behavior, and on this universal behavior itself. We show, that the behavior of the MSD is the same as in the scaled Brownian motion (SBM), being the mean-field model of the CTRW. The intermediate asymptotics of the probability density functions (PDF) for CTRW under complete resetting (provided they exist) are also the same as in the corresponding case for SBM. For incomplete resetting, however, the behavior of the PDF for CTRW and SBM is vastly different.

Universal spectral features of different classes of random-diffusivity processes

Stochastic models based on random diffusivities, such as the diffusing-diffusivity approach, are popular concepts for the description of non-Gaussian diffusion in heterogeneous media. Studies of these models typically focus on the moments and the displacement probability density function. Here we develop the complementary power spectral description for a broad class of random-diffusivity processes. In our approach we cater for typical single particle tracking data in which a small number of trajectories with finite duration are garnered. Apart from the diffusing-diffusivity model we study a range of previously unconsidered random-diffusivity processes, for which we obtain exact forms of the probability density function. These new processes are different versions of jump processes as well as functionals of Brownian motion. The resulting behaviour subtly depends on the specific model details. Thus, the central part of the probability density function may be Gaussian or non-Gaussian, and the tails may assume Gaussian, exponential, log-normal, or even power-law forms. For all these models we derive analytically the moment-generating function for the single-trajectory power spectral density. We establish the generic 1/f2-scaling of the power spectral density as function of frequency in all cases. Moreover, we establish the probability density for the amplitudes of the random power spectral density of individual trajectories. The latter functions reflect the very specific properties of the different random-diffusivity models considered here. Our exact results are in excellent agreement with extensive numerical simulations.

Probabilistic solutions of a variable-mass system under random excitations

The stationary probability density function (PDF) solution of a variable-mass system is calculated under Gaussian white noises and Poisson white noises, respectively. For small mass disturbance, the corresponding Fokker–Planck–Kolmogorov equation and Kolmogorov–Feller equation of the system are derived. The solution procedure based on the exponential–polynomial closure (EPC) method is formulated to obtain and study the probabilistic solutions of the strongly nonlinear variable-mass system subjected to Gaussian white noises and Poisson white noises. Both odd and even nonlinear variable-mass systems are considered. Compared with Monte Carlo simulation results, good agreement is achieved with the EPC method in the case of sixth-order polynomial. For large mass disturbance, the PDFs and logarithmic PDFs of displacement and velocity are numerically calculated via the fourth-order Runge–Kutta algorithm.

Unsteady chains of wave structures and anomalous transport of а passive scalar in a barotropic jet flow

The onset of anomalous transport of a passive scalar at the excitation of unsteady chains of wave structures with closed streamlines in a barotropic jet flow modeling zonal flows in the Earths atmosphere and ocean and in laboratory experiments is investigated. The analysis is performed within a dynamical model describing saturation of the barotropic instability in a plane-parallel channel flow with allowance for the beta-effect and external friction. The equations of a quasi-two-dimensional flow are solved numerically with the aid of a pseudospectral method. It is found that the generation of high modes in a jet with a two-hump velocity profile leads to accelerated transition to the complex dynamics, at which an increase in supercriticality first gives rise to а multiharmonic regime with a discrete spectrum. The exponents of the power dependence on the time of the averaged (over the ensemble) tracer particle displacement and its variance are computed for the basic generation regimes, which confirms the occurrence of anomalous diffusion of the scalar. A self-similar probability density function of tracer displacements is obtained and the dependence of the transition to complex dynamics on the number of vortices in the chain and on the strength of the beta-effect is elucidated. Numerical estimates are presented, which confirm the possibility of generation of unsteady vortex chains and the related anomalous transport of the scalar.barotropic flow; chains of wave structures; dynamical chaos; anomalous advection and diffusion

Probability and output analysis of asymmetric bistable energy harvesters subjected to Gaussian white noise

Due to their excellent broadband response and high sensitivity to low-amplitude excitations, there is significant interest in the theoretical analysis and experimental validation of bistable energy harvesters (BEHs). However, it is difficult in practice to obtain a perfectly symmetric bistable potential energy function, and our current understanding of the influence of asymmetric potentials on the response of BEHs is limited. As a result, this paper sheds light on the influence of asymmetric potentials on the response probability and electrical outputs of BEHs driven by Gaussian white noise. Firstly, the influence of potential well depth on the power outputs and response probability of symmetric BEHs is illustrated. When a quadratic nonlinearity is introduced to characterize the asymmetry, numerical simulations demonstrate that it has a negative effect on the output of BEHs when the noise intensity is relatively low, and the negative influence becomes great with an increase in the degree of asymmetry. From the probability analysis, it is concluded that the probability density function of displacement strongly depends on the degree of asymmetry of the potential function and it is also affected by the excitation intensity. Finally, experiments are carried out which demonstrate that the average output power is indeed influenced by the asymmetric potential of the BEHs under different excitation levels.

Unsteady Chains of Wave Structures and Anomalous Transport of Passive Admixture in a Barotropic Jet Flow

The onset of anomalous transport of a passive scalar at the excitation of unsteady chains of wave structures with closed streamlines in a barotropic jet flow modeling zonal flows in the Earth’s atmosphere and ocean and in laboratory experiments is investigated. The analysis is performed within a dynamical model describing saturation of the barotropic instability in a plane-parallel channel flow with allowance for the beta-effect and external friction. The equations of a quasi-two-dimensional flow are solved numerically with the aid of a pseudospectral method. It is found that the generation of high modes in a jet with a “two-hump” velocity profile leads to accelerated transition to the complex dynamics, at which an increase in supercriticality first gives rise to а multiharmonic regime with a discrete spectrum. The exponents of the power dependence on the time of the averaged (over the ensemble) tracer particle displacement and its variance are computed for the basic generation regimes, which confirms the occurrence of anomalous diffusion of the scalar. A self-similar probability density function of tracer displacements is obtained and the dependence of the transition to complex dynamics on the number of vortices in the chain and on the strength of the beta-effect is elucidated. Numerical estimates are presented, which confirm the possibility of generation of unsteady vortex chains and the related anomalous transport of the scalar.barotropic flow; chains of wave structures; dynamical chaos; anomalous advection and diffusion

Modeling Molecular Communications in Tubes With Poiseuille Flow and Robin Boundary Condition

Accurately modeling the molecular channel is crucial for the design and analysis of molecular communications. In this letter, we propose a Markovian-based channel model for molecular communications in tubes with Poiseuille flow and the Robin boundary condition. The cross section of the tube is divided into rings and the continuously varying flow velocity field is approximated by the mean of the flow velocity on each ring. The radial displacement of molecules versus time is modeled by a Markov chain, and then the probability density function of the axial displacement is modeled using this Markov chain and the approximated flow velocity field. The accuracy of the proposed model is verified through particle-based simulations.