SENSITIVITY AND UNCERTAINTY OF PROCESS DESIGNS TO THERMODYNAMIC MODEL PARAMETERS: A MONTE CARLO APPROACH

1993 ◽  
Vol 124 (1) ◽  
pp. 39-48 ◽  
Author(s):  
MICHAEL E. REED ◽  
WALLACE B. WHITING
Keyword(s):  
Monte Carlo ◽  
Thermodynamic Model ◽  
Model Parameters ◽  
Monte Carlo Approach

Application of A Monte Carlo Approach to Surface Complexation Modelling

2004 ◽  
Vol 824 ◽  
Author(s):  
M.M. Askarieh ◽  
T.G. Heath ◽  
W.M. Tearle
Keyword(s):  
Monte Carlo ◽  
Model Parameters ◽  
Surface Complexes ◽  
Monte Carlo Approach ◽  
Ion Sorption ◽  
Wide Range ◽  
Complex Models ◽  
Chemical Conditions ◽  
Parameter Values

AbstractA Monte Carlo-based approach has been adopted for development of a chemical thermodynamic model to describe the goethite surface in contact with sodium nitrate solutions. The technique involves the calculation of the goethite surface properties for the chemical conditions corresponding to each experimental data point. The representation of the surface was based on a set of model parameters, each of which was either fixed or was randomly sampled from a specified range of values. Thousands of such model representations were generated for different selected sets of parameter values with the use of the standard geochemical speciation computer program, HARPHRQ. The method allowed many combinations of parameter values to be sampled that might not be achieved with a simple least-squares fitting approach. It also allowed the dependence of the quality of fit on each parameter to be analysed. The Monte Carlo approach is most appropriate in the development of complex models involving the fitting of several datasets with several fitting parameters.Introduction of selenate surface complexes allowed the model to be extended to represent selenate ion sorption, selenium being an important radioelement in evaluation of the long-term safety of ILW disposal. The sorption model gave good agreement with a wide range of experimental sorption datasets for selenate.

Parameter estimation and confidence intervals for Xe-CT ventilation studies: a Monte Carlo approach

1998 ◽  
Vol 84 (2) ◽  
pp. 709-716 ◽  
Author(s):  
Brett A. Simon ◽  
Catherine Marcucci ◽  
Mansheung Fung ◽  
Subhash R. Lele
Keyword(s):  
Computed Tomography ◽  
Monte Carlo ◽  
Parameter Estimation ◽  
Confidence Intervals ◽  
Statistical Significance ◽  
Pulmonary Ventilation ◽  
Repeated Measurements ◽  
Model Parameters ◽  
Monte Carlo Approach ◽  
Ventilation Studies

Simon, Brett A., Catherine Marcucci, Mansheung Fung, and Subhash R. Lele. Parameter estimation and confidence intervals for Xe-CT ventilation studies: a Monte Carlo approach. J. Appl. Physiol. 84(2): 709–716, 1998.—Xenon-enhanced computed tomography (Xe-CT) is a technique for the noninvasive measurement of regional pulmonary ventilation from the washin and/or washout time constants of radiodense stable xenon gas, determined from serial computed tomography scans. Although the measurement itself is straightforward, there is a need for methods for the estimation of variability and confidence intervals so that the statistical significance of the information obtained may be evaluated, particularly since obtaining repeated measurements is often not practical. We present a Monte Carlo (MC) approach to determine the 95% confidence interval (CI) for any given measurement. This MC method was characterized in terms of its unbiasedness and coverage of the CI. In addition, 10 identical Xe-CT ventilation runs were performed in an anesthetized dog, and the time constant was determined for several regions of varying size in each run. The 95% CI, estimated from these repeated measurements as the mean ± 2 × SE, compared favorably with the CI obtained by the MC approach. Finally, a simulation was performed to compare the performance of three imaging protocols in estimating model parameters.

A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters

2018 ◽  
Vol 114 ◽  
pp. 164-179 ◽  
Author(s):  
Gaofeng Zhu ◽  
Xin Li ◽  
Jinzhu Ma ◽  
Yunquan Wang ◽  
Shaomin Liu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Hydrological Model ◽  
Model Parameters ◽  
Monte Carlo Approach

Wigner-Boltzmann Monte Carlo approach to nanodevice simulation: from quantum to semiclassical transport

2009 ◽  
Vol 8 (3-4) ◽  
pp. 324-335 ◽  
Author(s):  
Damien Querlioz ◽  
Huu-Nha Nguyen ◽  
Jérôme Saint-Martin ◽  
Arnaud Bournel ◽  
Sylvie Galdin-Retailleau ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Approach ◽  
Semiclassical Transport

scholarly journals Statistical Analysis of Tritium Breeding Ratio Deviations in the DEMO Due to Nuclear Data Uncertainties

2021 ◽  
Vol 11 (11) ◽  
pp. 5234
Author(s):  
Jin Hun Park ◽  
Pavel Pereslavtsev ◽  
Alexandre Konobeev ◽  
Christian Wegmann
Keyword(s):  
Monte Carlo ◽  
Fusion Reactor ◽  
Nuclear Data ◽  
Data File ◽  
Nuclear Model ◽  
Model Parameters ◽  
Tritium Breeding ◽  
Tritium Breeding Ratio ◽  
Nuclear Data Library ◽  
Breeding Ratio

For the stable and self-sufficient functioning of the DEMO fusion reactor, one of the most important parameters that must be demonstrated is the Tritium Breeding Ratio (TBR). The reliable assessment of the TBR with safety margins is a matter of fusion reactor viability. The uncertainty of the TBR in the neutronic simulations includes many different aspects such as the uncertainty due to the simplification of the geometry models used, the uncertainty of the reactor layout and the uncertainty introduced due to neutronic calculations. The last one can be reduced by applying high fidelity Monte Carlo simulations for TBR estimations. Nevertheless, these calculations have inherent statistical errors controlled by the number of neutron histories, straightforward for a quantity such as that of TBR underlying errors due to nuclear data uncertainties. In fact, every evaluated nuclear data file involved in the MCNP calculations can be replaced with the set of the random data files representing the particular deviation of the nuclear model parameters, each of them being correct and valid for applications. To account for the uncertainty of the nuclear model parameters introduced in the evaluated data file, a total Monte Carlo (TMC) method can be used to analyze the uncertainty of TBR owing to the nuclear data used for calculations. To this end, two 3D fully heterogeneous geometry models of the helium cooled pebble bed (HCPB) and water cooled lithium lead (WCLL) European DEMOs were utilized for the calculations of the TBR. The TMC calculations were performed, making use of the TENDL-2017 nuclear data library random files with high enough statistics providing a well-resolved Gaussian distribution of the TBR value. The assessment was done for the estimation of the TBR uncertainty due to the nuclear data for entire material compositions and for separate materials: structural, breeder and neutron multipliers. The overall TBR uncertainty for the nuclear data was estimated to be 3~4% for the HCPB and WCLL DEMOs, respectively.

scholarly journals On the stochastic uncertainties of thermally and optically stimulated luminescence signals: A Monte Carlo approach

2020 ◽  
Vol 219 ◽  
pp. 116945
Author(s):  
Vasilis Pagonis ◽  
Sebastian Kreutzer ◽  
Alex Roy Duncan ◽  
Ena Rajovic ◽  
Christian Laag ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Optically Stimulated Luminescence ◽  
Monte Carlo Approach ◽  
Stochastic Uncertainties
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