• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.963-972
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• 2009

The objective of this study is to evaluate an application of stochastic continuous storage function model with ensemble Kalman filter technique. The case study is performed at the upstream basin of Jibo streamflow gauge including Andong and Imha dam. Test period is for the rainy season during 2006 and 2007. Long term runoff analysis is feasible in the case of using deterministic model. Ensemble members for input data and parameters are generated using Monte Carlo simulation for the purpose of applying ensemble Kalman filter technique. The cumulative absolute errors of stochastic model to the deterministic one are improved for the amount of 17.5 %, 18.3 % and more than 40.0 % for Andong dam, Imha dam and Jibo station, respectively. The results indicate that the stochastic model improves the accuracy of the simulated discharge considerably.

• Wind and Structures
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• v.22 no.1
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• pp.17-41
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• 2016

To address the uncertainty of the flight trajectories caused by the turbulence and gustiness of the wind field over the roof and in the wake of a building, a 3-D probabilistic trajectory model of flat-type wind-borne debris is developed in this study. The core of this methodology is a 6 degree-of-freedom deterministic model, derived from the governing equations of motion of the debris, and a Monte Carlo simulation engine used to account for the uncertainty resulting from vertical and lateral gust wind velocity components. The influence of several parameters, including initial wind speed, time step, gust sampling frequency, number of Monte Carlo simulations, and the extreme gust factor, on the accuracy of the proposed model is examined. For the purpose of validation and calibration, the simulated results from the 3-D probabilistic trajectory model are compared against the available wind tunnel test data. Results show that the maximum relative error between the simulated and wind tunnel test results of the average longitudinal position is about 20%, implying that the probabilistic model provides a reliable and effective means to predict the 3-D flight of the plate-type wind-borne debris.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.353-362
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• 2014

The effects of nuclear data uncertainties are studied on a typical PWR fuel assembly model in the framework of the OECD Nuclear Energy Agency UAM (Uncertainty Analysis in Modeling) expert working group. The "Fast Total Monte Carlo" method is applied on a model for the Monte Carlo transport and burnup code SERPENT. Uncertainties on $k_{\infty}$, reaction rates, two-group cross sections, inventory and local pin power density during burnup are obtained, due to transport cross sections for the actinides and fission products, fission yields and thermal scattering data.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1543-1550
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• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

• Bulletin of the Korean Mathematical Society
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• v.59 no.2
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• pp.507-528
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• 2022

We propose a path integral method to construct a time stepwise local volatility for the stock index market under Dupire's model. Our method is focused on the pricing with the Monte Carlo Method (MCM). We solve the problem of randomness of MCM by applying numerical integration. We reconstruct this task as a matrix equation. Our method provides the analytic Jacobian and Hessian required by the nonlinear optimization solver, resulting in stable and fast calculations.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.650-659
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• 2016

As a type of accident-tolerant fuel, fully ceramic microencapsulated (FCM) fuel was proposed after the Fukushima accident in Japan. The FCM fuel consists of tristructural isotropic particles randomly dispersed in a silicon carbide (SiC) matrix. For a fuel element with such high heterogeneity, we have proposed a two-temperature homogenized model using the particle transport Monte Carlo method for the heat conduction problem. This model distinguishes between fuel-kernel and SiC matrix temperatures. Moreover, the obtained temperature profiles are more realistic than those of other models. In Part I of the paper, homogenized parameters for the FCM fuel in which tristructural isotropic particles are randomly dispersed in the fine lattice stochastic structure are obtained by (1) matching steady-state analytic solutions of the model with the results of particle transport Monte Carlo method for heat conduction problems, and (2) preserving total enthalpies in fuel kernels and SiC matrix. The homogenized parameters have two desirable properties: (1) they are insensitive to boundary conditions such as coolant bulk temperatures and thickness of cladding, and (2) they are independent of operating power density. By performing the Monte Carlo calculations with the temperature-dependent thermal properties of the constituent materials of the FCM fuel, temperature-dependent homogenized parameters are obtained.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3528-3542
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• 2021

A detailed geometrical model of a Krško reactor core was developed using a Monte Carlo neutron transport code MCNP. The main goal of developing an MCNP core model is for it to be used in future research focused on ex-core calculations. A script called McCord was developed to generate MCNP input for an arbitrary fuel cycle configuration from the diffusion based core design package CORD-2, taking advantage of already available material and temperature data obtained in the nuclear core design process. The core model was used to calculate 3D power density profile inside the core. The applicability of the calculated power density distributions was tested by comparison to the CORD-2 calculations, which is regularly used for the nuclear core design calculation verification of the Krško core. For the hot zero power and hot full power states differences between MCNP and CORD-2 in the radial power density profile were <3%. When studying axial power density profiles the differences in axial offset were less than 2.3% for hot full power condition. To further confirm the applicability of the developed model, the measurements with in-core neutron detectors were compared to the calculations, where differences of 5% were observed.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.719-727
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• 2004

Water quality models can be applied to manage the regional water quality problems and to estimate the target and allowable pollution load in watershed effectively. The optimization of state variables in the given water quality model Is necessary to build up more effective model. The least-squares method is applied to fit field observations in QUAL2E developed by U.S. EPA, which is most widely used one in the world to simulate the stream water quality, and the optimization model with constraints is constructed to estimate the parameters. The objective function of the optimization model is solved by Solver in Microsoft Excel and Monte Carlo simulation is conducted to know the influence of parameter in conventional pollutants. It is found that this technique is easily implemented and rapidly convergent computational procedure to calibrate the parameters after appling this approach in Anyang stream located in Kyonggi province mainly.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.463-474
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• 2002

A two-dimensional water quality management model, Unsteady/Uncertainty Water Quality Model(UUWQM), is developed for a hydrodynamic analysis, an advection-diffusion analysis, and a reliability analysis by using uncertainty technique. The model is applied to the 35 km reach of Sungju to Hyunpoong in the midstream of Nakdong River. 2-D hydrodynamic and water quality analyses are peformed in this reach. Important input variables are decided by sensitivity analysis and verified by Monte Carlo method. Frequency distributions of water quality concentrations are computed from MFOSM method and Monte Carlo method at several locations in this study area. A water quality management system is constructed by calculating the violation probabilities of existing water quality standards.