An experimental study examined the probability density functions (PDFs) of flow turbulence in an asymmetric alluvial sinuous channel. Three-dimensional instantaneous velocity measurements were obtained using an acoust...
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An experimental study examined the probability density functions (PDFs) of flow turbulence in an asymmetric alluvial sinuous channel. Three-dimensional instantaneous velocity measurements were obtained using an acoustic doppler vectrino profiler (ADVP). Results showed higher streamwise velocity near the inner bend due to topographic steering. The streamwise-vertical and streamwise-lateral Reynolds shear stress (RSS) revealed the presence of secondary flow within the bend. Turbulence intensity found to be significant in the streamwise direction than in the vertical direction. The contours of vorticity and lateral velocity vector highlighted the development of helical flow in the bend. The secondary flow drives sediment transport from the outer to the inner bank. A clockwise vorticity core developed in the near-bed region, while counter-clockwise vorticity appeared in the main flow region. The positive vorticity magnitude increased at the bend apex and its upstream but decreased in its downstream. Circulation strength is found to be increased at the upstream of the apex, diminished at the apex, and then again increased in the downstream of the apex. PDFs for flow turbulence parameters, including RSS in the streamwise-vertical and streamwise-lateral directions and turbulence intensity, are compared to theoretical models based on the Gram-Charlier (GC) exponential distribution. Experimental results across all locations showed a strong agreement with the computed PDF distributions.
The study of fission yields has a major impact on the characterization and understanding of the fission process and its applications. For the latter, it is crucial to provide fission yields with their associated corre...
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The study of fission yields has a major impact on the characterization and understanding of the fission process and its applications. For the latter, it is crucial to provide fission yields with their associated correlation matrix in order to estimate precisely the uncertainties of crucial quantities, such as the reactivity loss. In the last decade, different works have been proposed to estimate the correlations of the independent fission yields satisfying the consistency of the cumulative yield evaluations. In these previous works, only model parameters and conservation laws have driven the correlations. The novelty of the present work consists of new complete and consistent evaluations of U-235(n(th),f) independent and cumulative yields and their correlation matrices, starting from experimental data. The characterization of the probability density functions of fission yields validates the multivariate Gaussian assumption and constitutes a major issue in the validation of the uncertainty propagation tools of the applications. In addition, the new U-235(n(th),f) fission yield covariance data have resulted in a decrease in nuclear data related to uncertainty in associated burnup calculations.
The class of location-scale finite mixtures is of enduring interest both from applied and theoretical perspectives of probability and statistics. We establish and prove the following results: to an arbitrary degree of...
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The class of location-scale finite mixtures is of enduring interest both from applied and theoretical perspectives of probability and statistics. We establish and prove the following results: to an arbitrary degree of accuracy, (a) location-scale mixtures of a continuous probabilitydensity function (PDF) can approximate any continuous PDF, uniformly, on a compact set;and (b) for any finite p >= 1, location-scale mixtures of an essentially bounded PDF can approximate any PDF in L-p, in the L-p norm.
The aim of this work is to forecast wind energy by fitting the wind speed logged data, that have been measured over a year period (Nov. 2019-Mar. 2021), on a unique probabilitydensity function selected among a number...
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The aim of this work is to forecast wind energy by fitting the wind speed logged data, that have been measured over a year period (Nov. 2019-Mar. 2021), on a unique probabilitydensity function selected among a number of similar probabilityfunctions, as it is not always possible to select one distribution function that fits all wind speed regimes. The wind speed and direction data were measured at Fujairah site, which are affected by long-term fluctuation of +/- 10% of wind speed, and short-term fluctuation of more than +/- 20%. Based on the foregoing measurements, five different probability density functions can be fitted, namely Weibull, Rayleigh, Gamma, Lognormal and Exponential, with their associated parameters. A procedural algorithm is proposed for wind speed forecasting with best selected fitting distribution function, using a procedural forecast-check method, in which forecasting is performed with time on the most suitable distribution function that fits the foregoing data, depending on minimum errors accumulated from preceded measurements. Different error estimation methods are applied. The algorithm of selecting different distribution functions with time, makes energy prediction more accurate depending on the fluctuation of wind speed. A detailed probabilistic analysis is carried out to predict probable wind speed, and hence wind energy, based on variations of the parameters of the selected fitting distribution function.
Recent developments in the probabilistic and statistical analysis of probability density functions are reviewed. densityfunctions are treated as data objects for which suitable notions of the center of distribution a...
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Recent developments in the probabilistic and statistical analysis of probability density functions are reviewed. densityfunctions are treated as data objects for which suitable notions of the center of distribution and variability are discussed. Special attention is given to nonlinear methods that respect the constraints densityfunctions must obey. Regression, time series and spatial models are discussed. The exposition is illustrated with data examples. A supplementary vignette contains expanded versions of data analyses with accompanying codes. (C) 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.
Parametric decomposition techniques including single-sample unmixing and parametric end-member model-ling are routine mathematical methods used for interpreting grain-size distributions. In this study, transformed pro...
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Parametric decomposition techniques including single-sample unmixing and parametric end-member model-ling are routine mathematical methods used for interpreting grain-size distributions. In this study, transformed probability density functions for the Lognormal, Weibull, Skew Normal, and Skewed Generalized Normal distri-butions are derived and efficient open-source numerical programs applying these functions are presented. These new functions contain two free shape parameters characterizing the peak position and magnitude of a unimodal distribution, and they can be more easily initialized and constrained compared to their original counterparts, as the two shape parameters can be estimated directly from the grain-size distribution or its derivatives and can only vary within very narrow intervals. This enables the decomposition to converge to both reproducible/stable and structurally/genetically reasonable solutions. The transformed functions are applied to grain-size distribu-tions of aeolian sediments collected from around the Tengger Desert, using both the single-sample unmixing and parametric end-member modelling methods, and the results of different functions are compared. The implications of the results for parametric decomposition of sediment grain-size distributions using unimodal mathematical distributions are discussed.(c) 2023 Elsevier B.V. All rights reserved.
The condition-based maintenance of marine propulsion systems is attracting increasing interest in safety-related, financial, and environmental terms. Many researchers have studied different marine diesel engine models...
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The condition-based maintenance of marine propulsion systems is attracting increasing interest in safety-related, financial, and environmental terms. Many researchers have studied different marine diesel engine models and fault identification techniques. However, the thresholds between a healthy and a faulty engine have not been thoroughly analysed. Thus, this study aims to determine healthy engine threshold values for multiple parameters of a marine diesel engine. To this end, an operative commercial fishing vessel was considered, and multiple engine performance variables were measured through 2020 and the first half of 2021, totalling 5181 operating hours of the main engine without any fault occurrence. Preliminary correlation and relative deviation studies suggested the analysis of some constant trend parameters with alternative modelling techniques. Hence, probability density functions (PDFs) were used to establish confidence intervals for such parameters with data from the entire year of 2020. The parameters with the highest correlation and deviation were alternatively modelled using artificial neural networks (ANNs). Four different ANNs were trained, validated, and tested with data from 2020, calculating the mean absolute percentage errors for all the predicted parameters. Finally, data from 2021 were used to validate both the PDF and ANN modelled parameter thresholds set in 2020. For the 2021 data, the confidence interval set with the PDF showed a maximum failure rate of 1.21%. Alternatively, the ANN model parameters exhibited maximum percentage errors of 1.1%, 1.22%, and 1.95% for the engine performance, cooling, and cylinder subsystems, respectively. Finally, all the obtained thresholds were summarised, providing a good source for establishing faulty engine threshold values in future fault detection studies.
Cluster analysis can also detect abnormalities besides building a basis for identifying elements into clusters. Detecting abnormalities is a highly developed feature in the field of unsupervised learning. However, exi...
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Cluster analysis can also detect abnormalities besides building a basis for identifying elements into clusters. Detecting abnormalities is a highly developed feature in the field of unsupervised learning. However, existing studies have mainly focused on discrete data, not probability density functions. This paper enables a possibilistic approach to solving the clustering for probability density functions dealing with abnormal elements. First, the data are extracted using the density function. Then, they are passed through the proposed algorithm to produce a possibilistic partition. Finally, a decision rule is established to recognize which function is abnormal. We compare the proposed algorithm with baseline algorithms in clustering PDFs, such as k-means, FCF, and Self-Updated Clustering. The results of three numerical examples applied to the image are typical for this new method. Furthermore, The proposed algorithm reaches accuracy at 100% over simulated benchmark data and outperforms baseline methods. Additionally, two last examples apply to image data reaching G-mean up from 96 to 100% (Sensitivity: 92-100% and Specificity: 100%). The proposed method can be researched and used to understand the internal structures of big data in the digital age through the probability density functions.
Saturated hydraulic conductivity is a key soil mechanics parameter which has widespread use in many geotechnical applications. In order to set up stochastic analyses, geotechnical modellers require databases to calibr...
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Saturated hydraulic conductivity is a key soil mechanics parameter which has widespread use in many geotechnical applications. In order to set up stochastic analyses, geotechnical modellers require databases to calibrate the parameter ranges and distributions employed. This letter uses a recently compiled database of saturated hydraulic conductivity measurements called FG/KSAT-1358 and reports on the fitting of various probability density functions to the data of void ratio, liquid limit, water content ratio and the negative natural logarithm of k(sat). It is shown that the best fit distribution is the lognormal for void ratio, while the loglogistic distribution is most favoured for liquid limit and water content ratio, and the best fit distribution for -ln[k(sat)(m/s)] is the logistic function. The data of -ln[k(sat)(m/s)] is then subdivided according to liquid limit level, silt or clay classification, type of hydraulic conductivity test used and sample preparation/condition. When some subdivisions of the database are analysed, the best fit distribution is more variable with GEV and logistic being the most favoured for most of the studied subsets.
The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probabilitydensity f...
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The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experimentally.
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