• Macromolecular Research
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• 제11권5호
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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.

• 한국물환경학회지
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• 제36권5호
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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.

• E2M - 전기 전자와 첨단 소재
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• 제10권10호
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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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• 제23권6호
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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.

• 한국전기전자재료학회논문지
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• 제11권6호
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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.

• E2M - 전기 전자와 첨단 소재
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• 제10권3호
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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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• 제29권2호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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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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• 제49권3호
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• pp.361-366
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• 2011

몬테칼로 시뮬레이션 방법을 사용하여 유기화합물의 열역학적 물성을 예측하는 새로운 분자 시뮬레이션 소프트웨어를 개발하였다. 분자 구조, 분자간 포텐셜 에너지 함수와 엄밀한 통계역학적 원리로부터 많은 분자들을 포함한 계의 거동에 대한 확률 분포를 구하고 거시적인 계의 열역학적 물성을 계산한다. 본 연구에서 개발된 소프트웨어 cheMC는 윈도우즈 플랫폼에 기반하여 사용자 접근성이 좋고, 가시화 도구 및 차트 생성 기능 등 직관적인 인터페이스로 시뮬레이션 관리가 쉽다. 분자 시뮬레이션은 기존의 상태 방정식을 사용한 열역학 물성 연구를 보완하고, 향후 그 역할이 점점 더 커질 것으로 기대된다.

• 한국수자원학회논문집
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• 제42권10호
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• pp.773-783
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• 2009

강우유출모형의 입력 자료로 사용되는 강우 관측 자료의 불확실성이 유량예측에 미치는 영향을 분석하기 위하여 모형변수 검정의 불확실성 연구에서 사용하는 GLUE (Generalized Likelihood Uncertainty Estimation)방법을 입력 자료 부분으로 확장하여 적용 하였다. 독일의 Weida 유역의 강우 관측 자료를 바탕으로 구조적 및 비구조적인 불확실성 부분을 각각 구조적인 오차 수정 과정과 DUE (Data Uncertainty Engine)을 통하여 강우자료를 구성하였다. 이를 유역의 수문학적 작용을 고려하기 위해 선정한 집중형 강우유출모형, PDM (Probability Distribution Model)에 MC (Monte Carlo)와 GLUE 방법을 활용하여 적용하였다. MC검정변수들의 검정 후 반응 표면(Posterior response surface)을 검토하고 GLUE 의 반응검정 모형변수(Behavioural model parameter set)를 선택, 간략한 GLUE 유량곡선들을 계산하였다. 계산된 GLUE 유량곡선들을 모두 합하여 앙상블 유량을 산정하고, 이 유량의 90 분위를 강우량자료 및 모형변수 검정의 불확실성을 고려한 신뢰구간으로 제시하였다. PDM 모형의 결과는 유량곡선의 전구간에서 안정적인 모의 능력을 보여주고 있으나, 첨두유량 부분이 적게 산정되는 문제점을 보이고 있다. 본 연구에서 상대적으로 적은 수의 강우 시나리오 및 반응검정 모형변수의 적용이라는 한계에도 불구하고, GLUE 방법을 강우관측자료의 불확실성 부분으로 확장하여 강우자료 및 변수 검정의 불확실성을 고려한 모의된 유량예측의 신뢰구간의 적용가능성을 보여주고 있다.