scholarly journals Kinetic Analysis of Weakly Coupled Systems Using Probability Density Function of Coupling Coefficient Obtained by Monte Carlo Method

2011 ◽  
Vol 2 (0) ◽  
pp. 263-266 ◽  
Author(s):  
Toru OBARA ◽  
Hiroki TAKEZAWA
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Density Function ◽  
Kinetic Analysis ◽  
Probability Density ◽  
Density Function ◽  
Coupling Coefficient ◽  
Coupled Systems ◽  
Weakly Coupled ◽  
Weakly Coupled Systems

Monte-Carlo Method of Calculating Weakly Coupled Systems Using the PRIZMA-DSP Code

Atomic Energy ◽  
2018 ◽  
Vol 123 (6) ◽  
pp. 412-417
Author(s):  
Ya. Z. Kandiev ◽  
L. V. Lobanova ◽  
V. G. Orlov ◽  
E. V. Serova
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Coupled Systems ◽  
Weakly Coupled ◽  
Weakly Coupled Systems

scholarly journals Uncertainty evaluation of the conducted emission measurements

2013 ◽  
Vol 28 (2) ◽  
pp. 182-190 ◽  
Author(s):  
Aleksandar Kovacevic ◽  
Dejan Despotovic ◽  
Zoran Rajovic ◽  
Koviljka Stankovic ◽  
Ana Kovacevic ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Density Function ◽  
Measurement Uncertainty ◽  
Probability Density ◽  
Density Function ◽  
Least Squares Method ◽  
Combined Method ◽  
Conducted Emission ◽  
Number Of Classes

For the evaluation of measurement uncertainty in measuring the conduction emission, in this paper we propose a new model which uses mixed distribution. Evaluation of probability density function for the measurand has been done using Monte Carlo method and a modified least-squares method (combined method). In addition, the number of data n and the number of classes of histogram k which were used for simulation, were varied.

Estimation of the four-wave mixing noise probability-density function by the multicanonical Monte Carlo method

2005 ◽  
Vol 30 (1) ◽  
pp. 11 ◽  
Author(s):  
Ioannis Neokosmidis ◽  
Thomas Kamalakis ◽  
Aristides Chipouras ◽  
Thomas Sphicopoulos
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Four Wave Mixing ◽  
Wave Mixing ◽  
Mixing Noise

Monte Carlo Probability Density Function Method for Gas Turbine Combustor Flowfield Predictions

1997 ◽  
Vol 13 (2) ◽  
pp. 218-225 ◽  
Author(s):  
Anil K. Tolpadi ◽  
Sanjay M. Correa ◽  
David L. Burrus ◽  
Hukam C. Mongia
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Gas Turbine ◽  
Density Function ◽  
Function Method ◽  
Gas Turbine Combustor

scholarly journals Application of Monte Carlo Method for Evaluation of Uncertainties of ITS-90 by Standard Platinum Resistance Thermometer

2017 ◽  
Vol 17 (3) ◽  
pp. 108-116 ◽  
Author(s):  
Rudolf Palenčár ◽  
Peter Sopkuliak ◽  
Jakub Palenčár ◽  
Stanislav Ďuriš ◽  
Emil Suroviak ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Density Function ◽  
Fixed Points ◽  
Probability Density ◽  
Platinum Resistance Thermometer ◽  
Platinum Resistance ◽  
Resistance Thermometer ◽  
Standard Platinum Resistance Thermometer ◽  
The Monte Carlo Method

AbstractEvaluation of uncertainties of the temperature measurement by standard platinum resistance thermometer calibrated at the defining fixed points according to ITS-90 is a problem that can be solved in different ways. The paper presents a procedure based on the propagation of distributions using the Monte Carlo method. The procedure employs generation of pseudo-random numbers for the input variables of resistances at the defining fixed points, supposing the multivariate Gaussian distribution for input quantities. This allows taking into account the correlations among resistances at the defining fixed points. Assumption of Gaussian probability density function is acceptable, with respect to the several sources of uncertainties of resistances. In the case of uncorrelated resistances at the defining fixed points, the method is applicable to any probability density function. Validation of the law of propagation of uncertainty using the Monte Carlo method is presented on the example of specific data for 25 Ω standard platinum resistance thermometer in the temperature range from 0 to 660 °C. Using this example, we demonstrate suitability of the method by validation of its results.

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