scholarly journals Analysis of Probability Distribution of Muzzle Velocity for Chrome Plated Barrel

2021 ◽  
Vol 24 (4) ◽  
pp. 401-407
Author(s):  
Jaekab Kim ◽  
Jaehoon Kim
Keyword(s):  
Probability Distribution ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Distribution Model ◽  
Statistical Parameters ◽  
Chrome Plating ◽  
Fitness Test ◽  
Muzzle Velocity ◽  
Probability Paper ◽  
Statistical Program

To confirm the change of muzzle velocity and the most suitable probability distribution model of the 155 mm K9 howitzer barrel with chrome plating and changed rifling. Using a statistical program, the muzzle velocity were plotted on a normal distribution, a 2-parameter and 3-parameter Weibull distribution on a probability paper. Also, statistical parameters were estimated and muzzle velocity fitness test and probability of K676 charge were plotted. In both the chrome-plated with standard rifling and changed rifling for K9 barrel, the 2-parameter and 3-parameter Weibull distribution were skewed to the left compared to the normal distribution. It was confirmed that the muzzle velocity of the K9 barrel with chromium-plated is suitable for the normal distribution and 3-parameter Weibull distribution model.

scholarly journals Estimation on Reliability Models of Bearing Failure Data

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xia Xintao ◽  
Chang Zhen ◽  
Zhang Lijun ◽  
Yang Xiaowei
Keyword(s):  
Probability Distribution ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Distribution Model ◽  
Bearing Failure ◽  
Analysis Model ◽  
Failure Data ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Two Parameter

The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.

Probability Distributions for Wave Runup on Offshore Platform Columns

2009 ◽  
Author(s):  
Amir H. Izadparast ◽  
John M. Niedzwecki
Keyword(s):  
Probability Distribution ◽  
Weibull Distribution ◽  
Empirical Model ◽  
Offshore Structures ◽  
Distribution Model ◽  
Wave Runup ◽  
Wave Impact ◽  
Quadratic Transformation ◽  
L Moments ◽  
Run Up

For the design of offshore structures it is important to accurately predict wave runup and thus avoid topside inundation and minimize the wave impact on the underside of the deck structure. In this paper a three-parameter probability distribution function for nonlinear wave run-up amplitudes is presented. It builds upon previous studies and utilizes the quadratic transformation of incident waves. The parameters of this probability distribution are estimated from the data using method of L-moments and the explicit relation between the parameters and L-moments is presented. The L-moments themselves are linear combinations of ordered data and consequently they are less influenced by outliers and unexpectedly large values. Earlier theoretical models, based on simplified diffraction theory, are presented and compared with the L-moments model. A three-parameter Weibull distribution model that utilizes the method of L-moments is derived and discussed. Run-up measurements from a mini-TLP model test program are used as the basis for comparison of the three methods. This study demonstrates that the new empirical model and Weibull distribution are more robust in representing the probability distribution of nonlinear runup amplitudes especially for the weakly nonlinear cases with moderate steepness. Although the new empirical model and Weibull distribution have different probability structure their estimates are found to be fairly close.

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

A new probability distribution model of turbulent irradiance based on Born perturbation theory

2010 ◽  
Vol 53 (10) ◽  
pp. 1811-1818
Author(s):  
HongXing Wang ◽  
Min Liu ◽  
Hao Hu ◽  
Qian Wang ◽  
XiGuo Liu
Keyword(s):  
Perturbation Theory ◽  
Probability Distribution ◽  
Distribution Model

Is the Gaussian Distribution Normal?

1965 ◽  
Vol 18 (1) ◽  
pp. 65-71 ◽  
Author(s):  
E. W. Anderson
Keyword(s):  
Normal Distribution ◽  
Gaussian Distribution ◽  
Mathematical Foundation ◽  
Probability Paper ◽  
Straight Line

It has been said that the mathematician accepts the gaussian distribution because he believes it to be supported in practice, and the practical man accepts it because he believes it has a sound mathematical foundation. Presumably it is for such reasons that the gaussian distribution is known as the ‘normal’ distribution and ‘probability paper’ has scales specially distorted so that a gaussian distribution will appear as a straight line.

CATI: A Computer Aided Tolerancing Interface

1992 ◽  
Author(s):  
Rikard Söderberg
Keyword(s):  
Normal Distribution ◽  
Tolerance Analysis ◽  
Distribution Model ◽  
Preliminary Design ◽  
Tolerance Limits ◽  
Cad System ◽  
System A ◽  
Computer Aided ◽  
2D Drawing ◽  
Normal Distribution Model

Abstract This work presents an interface for tolerance analysis in a CAD system. A method for picking up necessary information from a 2D drawing is developed and implemented as an interface in a commercial CAD system. The interface communicates with an external calculation program which determines unknown tolerance limits using the normal distribution model. Results from the calculation program is in the end used by the interface to present measures with tolerances on the drawing. The advantage of using CATI in preliminary design is discussed, and a strategy for treating interrelated tolerance chains is presented.

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