• Macromolecular Research
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• v.11 no.5
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• pp.393-397
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• 2003

By applying a configurational-bias Gibbs ensemble Monte Carlo algorithm, priority simulation results regarding the conformation of non-dilute polyelectrolytes in solvents are obtained. Solutions of freely-jointed chains are considered, and a new method termed strandwise configurational-bias sampling is developed so as to effectively overcome a difficulty on the transfer of polymer chains. The structure factors of polyelectrolytes in the bulk as well as in the confined space are estimated with variations of the polymer charge density.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• Electrical & Electronic Materials
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• v.10 no.10
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• pp.1000-1004
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• 1997

In this paper the current-voltage characteristics of a submicron GaAs MESFET is simulated by using the self-consistent ensemble Monte Carlo method. The numerical algorithm employed in solving the two-dimensional Poisson equation is the successive over-relaxation(SOR) method. The total number of employed superparticles is about 1000 and the field adjusting time is 10fs. To obtain the steady-state results the simulation is performed for 10ps at each bias condition. The simulation results show the average electron velocity is modified by the gate voltage.

• Bulletin of the Korean Chemical Society
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• v.23 no.6
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• pp.811-817
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• 2002

Gibbs ensemble Monte Carlo simulations were performed to calculate the vapor-liquid coexistence properties for the binary mixtures $CO_2/CH_3OH$, $CO_2/C_2H_5OH$, and $CO_2/CH_3CH_2CH_2OH.$ The configurational bias Monte Carlo method was used in the simulation of alcohol. Density of the mixture, composition of the mixture, the pressure-composition diagram, and the radial distribution function were calculated at vapor-liquid equilibrium. The composition and the density of both vapor and liquid from simulation agree considerably well with the experimental values over a wide range of pressures. The radial distribution functions in the liquid mixtures show that $CO_2$ molecules interact more stogly with methyl group than methylene group of $C_2H_5OH$ and $CH_3CH_2CH_2OH$ due to the steric effects of the alcohol molecules.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.6
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• pp.423-427
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• 1998

In this paper the electron mobility in $Ga{1-X}In_xAs$alloy semiconductors is simulated by using the ensemble Monte Carlo method. The simulations for Ga\ulcornerIn\ulcornerAs with In mole fraction, doping concentration and temperature as parameters are performed. The electron mobility for alloys which perfectly orderd alloys without the alloy scattering mechanism are assumed, the results show that mobility in Ga\ulcornerIn\ulcornerAs is improved by 11%, 12% and 7% for 0.25, 0.53 and 0.75. In mole fractions, respectively, We reported the theoretical results of electron mobility in $Ga{1-X}In_xAs$alloys, so those will contribute to the research and development into materials for high-speed semiconductor devices.

• Electrical & Electronic Materials
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• v.10 no.3
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• pp.268-273
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• 1997

In this paper the transient electron transport in GaAs bulk is simulated by using ensemble Monte Carlo method. To analyze the transient electron transport the 10000 electrons in the .GAMMA. valley are simulated simultaneously for 10 picoseconds. The electric field-velocity relation is obtained. The high impurity density reduces the negative differential resistance effect. The result of transient average velocity shows the electron velocity in the transient state is faster than that in the steady state. This transient velocity overshoot is caused by the intervalley scattering mechanism. And we confirmed the fact that the energy relaxation time is longer than the momentum relaxation time.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• Korean Chemical Engineering Research
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• v.49 no.3
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• pp.361-366
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• 2011

By using Monte Carlo simulation method we developed a new molecular simulation software which can be used to predict the thermodynamic properties of organic compounds. Starting from molecular structure and intermolecular potential function, rigorous statistical mechanical principles give a probability distribution for the behavior of a system containing many molecules, which enables us to calculate macroscopic thermodynamic properties of the system. The software developed in this work, cheMC, is based on Windows platform providing with easy access. One can efficiently administrate simulations by using an intuitive interface equipped with visualization tool and chart generation. It is expected that molecular simulations supplement the equation of state approach and will play a more important role in the study of thermodynamic properties.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.773-783
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• 2009

The effects of rainfall input uncertainty on predictions of stream flow are studied based extended GLUE (Generalized Likelihood Uncertainty Estimation) approach. The uncertainty in the rainfall data is implemented by systematic/non-systematic rainfall measurement analysis in Weida catchment, Germany. PDM (Probability Distribution Model) rainfall runoff model is selected for hydrological representation of the catchment. Using general correction procedure and DUE(Data Uncertainty Engine), feasible rainfall time series are generated. These series are applied to PDM in MC(Monte Carlo) and GLUE method; Posterior distributions of the model parameters are examined and behavioural model parameters are selected for simplified GLUE prediction of stream flow. All predictions are combined to develop ensemble prediction and 90 percentile of ensemble prediction, which are used to show the effects of uncertainty sources of input data and model parameters. The results show acceptable performances in all flow regime, except underestimation of the peak flows. These results are not definite proof of the effects of rainfall uncertainty on parameter estimation; however, extended GLUE approach in this study is a potential method which can include major uncertainty in the rainfall-runoff modelling.