The algebra of probability density functions
Algebra of probability density functions of variable known as arbitrary function of one or more random variables
Grunebaum, B. G.↗
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The use of generalized probability density functions in random vibration theory.
Probability density functions used in random vibration theory
TIME SERIES↗
A probability density function for reflected beam molecules
Probability density function for reflected beam molecules
Heybey, W. H.↗
A Tomographic Method for the Reconstruction of Local Probability Density Functions
A method of obtaining the probability density function (PDF) of local properties from path integrated measurements is described. The approach uses a discrete probability function (DPF) method to infer the PDF of the local extinction coefficient from measurements of the PDFs of the path integrated transmittance. The local PDFs obtained using the method are compared with those obtained from direct intrusive measurements in propylene/air and ethylene/air diffusion flames. The results of this comparison are good.
Sivathanu, Y. R.↗
The probability density function of a hardware performance parameter
Probability density function of hardware performance parameter and incentive contracting
Gilchriest, C. E.↗
Probability density functions in turbulent channel flow
The probability density functions (pdf's) of the fluctuating velocity components, as well as their first and second derivatives, are calculated using data from the direct numerical simulations (DNS) of fully developed turbulent channel flow. It is observed that, beyond the buffer region, the pdf of each of these quantities is independent of the distance from the channel wall. It is further observed that, beyond the buffer region, the pdf's for all the first derivatives collapse onto a single universal curve and those of the second derivatives also collapse onto another universal curve, irrespective of the distance from the wall. The kinetic-energy dissipation rate exhibits log normal behavior.
Dinavahi, Surya P. G.↗
Ensemble Averaged Probability Density Function (APDF) for Compressible Turbulent Reacting Flows
In this paper, we present a concept of the averaged probability density function (APDF) for studying compressible turbulent reacting flows. The APDF is defined as an ensemble average of the fine grained probability density function (FG-PDF) with a mass density weighting. It can be used to exactly deduce the mass density weighted, ensemble averaged turbulent mean variables. The transport equation for APDF can be derived in two ways. One is the traditional way that starts from the transport equation of FG-PDF, in which the compressible Navier- Stokes equations are embedded. The resulting transport equation of APDF is then in a traditional form that contains conditional means of all terms from the right hand side of the Navier-Stokes equations except for the chemical reaction term. These conditional means are new unknown quantities that need to be modeled. Another way of deriving the transport equation of APDF is to start directly from the ensemble averaged Navier-Stokes equations. The resulting transport equation of APDF derived from this approach appears in a closed form without any need for additional modeling. The methodology of ensemble averaging presented in this paper can be extended to other averaging procedures: for example, the Reynolds time averaging for statistically steady flow and the Reynolds spatial averaging for statistically homogeneous flow. It can also be extended to a time or spatial filtering procedure to construct the filtered density function (FDF) for the large eddy simulation (LES) of compressible turbulent reacting flows.
Shih, Tsan-Hsing↗
Probability Density Functions of Observed Rainfall in Montana
The question of whether a rain rate probability density function (PDF) can vary uniformly between precipitation events is examined. Image analysis on large samples of radar echoes is possible because of advances in technology. The data provided by such an analysis easily allow development of radar reflectivity factors (and by extension rain rate) distribution. Finding a PDF becomes a matter of finding a function that describes the curve approximating the resulting distributions. Ideally, one PDF would exist for all cases; or many PDF's that have the same functional form with only systematic variations in parameters (such as size or shape) exist. Satisfying either of theses cases will, validate the theoretical basis of the Area Time Integral (ATI). Using the method of moments and Elderton's curve selection criteria, the Pearson Type 1 equation was identified as a potential fit for 89 percent of the observed distributions. Further analysis indicates that the Type 1 curve does approximate the shape of the distributions but quantitatively does not produce a great fit. Using the method of moments and Elderton's curve selection criteria, the Pearson Type 1 equation was identified as a potential fit for 89% of the observed distributions. Further analysis indicates that the Type 1 curve does approximate the shape of the distributions but quantitatively does not produce a great fit.
Larsen, Scott D.↗
Time-dependent probability density function analysis of H-mode transitions
Abstract The first application of time-dependent probability density function (PDF) analysis to the L-H transition in fusion plasmas is presented. PDFs are constructed using Doppler Backscattering data of perpendicular fluctuation velocity, , and turbulence from the edge region of the DIII-D tokamak. These raw time-series data are sliced into millisecond-long sliding time-windows to create PDFs. During the transition, the PDFs develop strong right tails, indicative of turbulence-suppressing localised flows in the plasma edge; such features and other subtle behaviours are explored using novel information geometry techniques. This letter examines the applicability of these techniques to predict L-H transitions and investigate predator-prey self-regulation theories between turbulence and perpendicular velocity.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Establishing Models for Digital Twin of Hydropower Systems Using Probability Density Function Shaping
This paper introduces a digital twin modeling method for hydropower systems with Kaplan turbines using probability density function (PDF) shaping. We first use multilayer perceptron (MLP) model to build the discretized openloop Kaplan unit, where the MLP is trained by historical data. Then we use a proportional integral double derivative (PIDD) controller and a lead-lag exciter to test the obtained digital twin model in a closed-loop fashion. Simulation results show that the proposed digital twin modeling method can accurately capture the dynamics of the Kaplan hydropower unit. Finally, we show that the obtained digital twin can help to optimize the PIDD parameters. Compared with the original PIDD controller, the optimized one can achieve an over 90% improvement on the mean square tracking error.
Yin, Zhun [New York University]↗
Probability density function approach for compressible turbulent reacting flows
The objective of the present work is to extend the probability density function (PDF) tubulence model to compressible reacting flows. The proability density function of the species mass fractions and enthalpy are obtained by solving a PDF evolution equation using a Monte Carlo scheme. The PDF solution procedure is coupled with a compression finite-volume flow solver which provides the velocity and pressure fields. A modeled PDF equation for compressible flows, capable of treating flows with shock waves and suitable to the present coupling scheme, is proposed and tested. Convergence of the combined finite-volume Monte Carlo solution procedure is discussed. Two super sonic diffusion flames are studied using the proposed PDF model and the results are compared with experimental data; marked improvements over solutions without PDF are observed.
Hsu, A. T.↗
The probability density function for phase- difference estimates obtained from the envelopes of the sum and difference of two sine waves.
Probability density function of phase difference magnitude, using estimator having gain matched amplifiers
PHASE DEVIATION↗
Downlink Probability Density Functions for EOS-McMurdo Sound
The visibility times and communication link dynamics for the Earth Observations Satellite (EOS)-McMurdo Sound direct downlinks have been studied. The 16 day EOS periodicity may be shown with the Goddard Trajectory Determination System (GTDS) and the entire 16 day period should be simulated for representative link statistics. We desire many attributes of the downlink, however, and a faster orbital determination method is desirable. We use the method of osculating elements for speed and accuracy in simulating the EOS orbit. The accuracy of the method of osculating elements is demonstrated by closely reproducing the observed 16 day Landsat periodicity. An autocorrelation function method is used to show the correlation spike at 16 days. The entire 16 day record of passes over McMurdo Sound is then used to generate statistics for innage time, outage time, elevation angle, antenna angle rates, and propagation loss. The levation angle probability density function is compared with 1967 analytic approximation which has been used for medium to high altitude satellites. One practical result of this comparison is seen to be the rare occurrence of zenith passes. The new result is functionally different than the earlier result, with a heavy emphasis on low elevation angles. EOS is one of a large class of sun synchronous satellites which may be downlinked to McMurdo Sound. We examine delay statistics for an entire group of sun synchronous satellites ranging from 400 km to 1000 km altitude. Outage probability density function results are presented three dimensionally.
Christopher, P.↗
Probability Density Function Control of Frequency Fluctuations in Renewable-Rich Power Systems
The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Here, based upon the stochastic distribution control theory originated by the second author, this paper proposes a novel stochastic controller to improve the frequency PDF in power grids when integrating a large amount of RESs, thereby minimizing the effects of uncertainties and enhancing overall system stability. The key idea is to manipulate the controllable power generation resources so that the frequency PDF is make to follow a target PDF by using the stochastic distribution control theory originated by the second author. The proposed method can easily be plugged into existing automatic generation controls for multi-area transmission grids. The proposed method is validated via a modified Kundar's two area system and 240-bus Western Electricity Coordinating Council systems. The simulation results show that the proposed control shapes the frequency PDF narrower and sharper, leading to a notable improvement toward minimizing the effects of randomness and uncertainty during grid operation.
frequency↗
Modulation Based on Probability Density Functions
A proposed method of modulating a sinusoidal carrier signal to convey digital information involves the use of histograms representing probability density functions (PDFs) that characterize samples of the signal waveform. The method is based partly on the observation that when a waveform is sampled (whether by analog or digital means) over a time interval at least as long as one half cycle of the waveform, the samples can be sorted by frequency of occurrence, thereby constructing a histogram representing a PDF of the waveform during that time interval.
Williams, Glenn L.↗
Visualisation and outlier detection for probability density function ensembles
Abstract Exploratory data analysis (EDA) for functional data—data objects where observations are entire functions—is a difficult problem that has seen significant attention in recent literature. This surge in interest is motivated by the ubiquitous nature of functional data, which are prevalent in applications across fields such as meteorology, biology, medicine and engineering. Empirical probability density functions (PDFs) can be viewed as constrained functional data objects that must integrate to one and be nonnegative. They show up in contexts such as yearly income distributions, zooplankton size structure in oceanography and in connectivity patterns in the brain, among others. While PDF data are certainly common in modern research, little attention has been given to EDA specifically for PDFs. In this paper, we extend several methods for EDA on functional data for PDFs and compare them on simulated data that exhibit different types of variation, designed to mimic that seen in real‐world applications. We then use our new methods to perform EDA on the breakthrough curves observed in gas transport simulations for underground fracture networks.
97 MATHEMATICS AND COMPUTING↗
Evaluation of joint probability density function models for turbulent nonpremixed combustion with complex chemistry
Two types of mixing sub-models are evaluated in connection with a joint-scalar probability density function method for turbulent nonpremixed combustion. Model calculations are made and compared to simulation results for homogeneously distributed methane-air reaction zones mixing and reacting in decaying turbulence within a two-dimensional enclosed domain. The comparison is arranged to ensure that both the simulation and model calculations a) make use of exactly the same chemical mechanism, b) do not involve non-unity Lewis number transport of species, and c) are free from radiation loss. The modified Curl mixing sub-model was found to provide superior predictive accuracy over the simple relaxation-to-mean submodel in the case studied. Accuracy to within 10-20% was found for global means of major species and temperature; however, nitric oxide prediction accuracy was lower and highly dependent on the choice of mixing sub-model. Both mixing submodels were found to produce non-physical mixing behavior for mixture fractions removed from the immediate reaction zone. A suggestion for a further modified Curl mixing sub-model is made in connection with earlier work done in the field.
Smith, N. S. A.↗
Eulerian Mapping Closure Approach for Probability Density Function of Concentration in Shear Flows
The Eulerian mapping closure approach is developed for uncertainty propagation in computational fluid mechanics. The approach is used to study the Probability Density Function (PDF) for the concentration of species advected by a random shear flow. An analytical argument shows that fluctuation of the concentration field at one point in space is non-Gaussian and exhibits stretched exponential form. An Eulerian mapping approach provides an appropriate approximation to both convection and diffusion terms and leads to a closed mapping equation. The results obtained describe the evolution of the initial Gaussian field, which is in agreement with direct numerical simulations.
He, Guowei↗