Multicanonical Monte Carlo evaluation of drag probability distribution functions

2018 ◽  
Vol 30 (3) ◽  
pp. 033605
Author(s):  
Ming Tong ◽  
David Yevick
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Distribution Functions ◽  
Probability Distribution Functions ◽  
Monte Carlo Evaluation

scholarly journals Uncertainty Analysis of Hydrological Drought due to Record Length, Time Scale, and Probability Distribution Functions Using Monte-Carlo Simulation Method

2021 ◽  
Author(s):  
Faezeh Ghasemnezhad ◽  
Ommolbanin Bazrafshan ◽  
Mehdi Fazeli ◽  
Mohammad Parvinnia ◽  
Vijay Singh
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Confidence Interval ◽  
Time Scale ◽  
Statistical Data ◽  
Distribution Functions ◽  
Hydrological Drought ◽  
Discharge Data ◽  
Probability Distribution Functions ◽  
Record Length

Abstract Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30 and 49 year length from Minab dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3, 6. 9, 12, 24 and 48 month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte-Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to normal and lognormal distributions and the minimum uncertainty to gamma and Weibull distributions. Further, the increase in both time scale and record length led to the decrease in uncertainty.

scholarly journals Three-dimensional Monte Carlo simulations of the dynamics of macromolecular particles in solutions flowing in mesopores

2010 ◽  
Vol 8 (5) ◽  
pp. 1009-1013 ◽  
Author(s):  
Ali Atwi ◽  
Antoine Khater ◽  
Abbas Hijazi
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Numerical Simulations ◽  
Solid Surface ◽  
Dynamic Equilibrium ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Depletion Layer ◽  
Probability Distribution Functions ◽  
Equilibrium Probability

AbstractNumerical simulations are developed to calculate the dynamic equilibrium probability distribution functions (PDF) for macromolecular rod-like particles suspended in a fluid under hydrodynamic flow inside mesopores. The simulations take into account the effects of Brownian and hydrodynamic forces acting on the particles, as well as diffusive collisions of the particles with the solid surface boundaries. An algorithm is developed for this purpose based on Jeffery’s equations for the dynamics of ellipsoidal objects in bulk fluids, and on a mechanism of restitution for the diffusive collisions. The results are presented with a focus on the depletion layer next to two types of solid boundaries, ideally flat and rough. They demonstrate the significance of numerical simulations in 3D compared to previous results based on a 2D approach. In particular, we are able to obtain a complete topography for the PDFs segmented as a hierarchy in the depletion layer.

scholarly journals Probabilistic Storm Surge Estimation for Landfalling Hurricanes: Advancements in Computational Efficiency Using Quasi-Monte Carlo Techniques

2021 ◽  
Vol 9 (12) ◽  
pp. 1322
Author(s):  
Aikaterini P. Kyprioti ◽  
Ehsan Adeli ◽  
Alexandros A. Taflanidis ◽  
Joannes J. Westerink ◽  
Hendrik L. Tolman
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Storm Surge ◽  
Storm Track ◽  
Distribution Functions ◽  
Alternative Formulation ◽  
Forecast Errors ◽  
Quasi Monte Carlo ◽  
Monte Carlo Techniques ◽  
Probability Distribution Functions

During landfalling tropical storms, predictions of the expected storm surge are critical for guiding evacuation and emergency response/preparedness decisions, both at regional and national levels. Forecast errors related to storm track, intensity, and size impact these predictions and, thus, should be explicitly accounted for. The Probabilistic tropical storm Surge (P-Surge) model is the established approach from the National Weather Service (NWS) to achieve this objective. Historical forecast errors are utilized to specify probability distribution functions for different storm features, quantifying, ultimately, the uncertainty in the National Hurricane Center advisories. Surge statistics are estimated by using the predictions across a storm ensemble generated by sampling features from the aforementioned probability distribution functions. P-Surge relies, currently, on a full factorial sampling scheme to create this storm ensemble, combining representative values for each of the storm features. This work investigates an alternative formulation that can be viewed as a seamless extension to the current NHC framework, adopting a quasi-Monte Carlo (QMC) sampling implementation with ultimate goal to reduce the computational burden and provide surge predictions with the same degree of statistical reliability, while using a smaller number of sample storms. The definition of forecast errors adopted here directly follows published NWS practices, while different uncertainty levels are considered in the examined case studies, in order to offer a comprehensive validation. This validation, considering different historical storms, clearly demonstrates the advantages QMC can offer.

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