• 국제강구조저널
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• 제18권5호
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• pp.1607-1616
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• 2018

Quantitatively modeling and propagating all sources of uncertainty stand at the core of seismic fragility assessment of structures. This paper investigates the effects of various sources of uncertainty on seismic responses and seismic fragility estimates of single-layer reticulated domes. Sensitivity analyses are performed to examine the sensitivity of typical seismic responses to uncertainties in structural modeling parameters, and the results suggest that the variability in structural damping, yielding strength, steel ultimate strain, dead load and snow load has significant effects on the seismic responses, and these five parameters should be taken as random variables in the seismic fragility assessment. Based on this, fragility estimates and fragility curves incorporating different levels of uncertainty are obtained on the basis of the results of incremental dynamic analyses on the corresponding set of 40 sample models generated by Latin Hypercube Sampling method. The comparisons of these fragility curves illustrate that, the inclusion of only ground motion uncertainty is inappropriate and inadequate, and the appropriate way is incorporating the variability in the five identified structural modeling parameters as well into the seismic fragility assessment of single-layer reticulated domes.

• Nuclear Engineering and Technology
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• 제35권1호
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• pp.64-79
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• 2003

In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems.

• 제어로봇시스템학회논문지
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• 제20권3호
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• pp.270-276
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• 2014

This survey paper reviews robust control problems in both frequency domain and time domain. Robust control is focused on model uncertainties such as modeling error, system parameter variations, and disturbances. Robust control design problems are discussed according to parameter uncertainty, polytopic uncertainty, and norm-bounded uncertainty. Nowadays, robust control theory is combined with various control theory such as model predictive control, adaptive control, intelligent control, and time delay control.

• 한국화재소방학회논문지
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• 제26권2호
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• pp.40-52
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• 2012

본 논문에서는 원자력발전소의 모터제어반 스위치기어실 화재 모델링에 대한 입력변수 불확실성 분석을 수행하였다. 화재모델링은 FDS 5.5를 사용하였고 FDS 입력변수 램던 샘플링은 라틴하이퍼쿠브 몬테칼로 방법을 이용하였다. 본 연구에서 수행한 입력변수 불확실성 분석 결과를 비교하기 위해 NUREG-1934의 화재모델링 결정론적 불확실성 분석과 민감도 분석 방법을 이용한 분석도 수행하였다. 분석결과, 본 연구의 모터제어반 스위치 기어룸 화재 모델링에 대한 입력변수 불확실성 분석방법이 NUREG-1934의 방법보다 보수적인 결과를 얻을 수 있음을 확인하였다.

• Nuclear Engineering and Technology
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• 제51권4호
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• pp.968-976
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• 2019

This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

• 한국물환경학회지
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• 제36권5호
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• 한국농공학회논문집
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• 제60권1호
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• pp.47-57
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• 2018

This study implemented the rainfall-runoff analysis of the Mekong River basin using the SWAT (Soil and Water Assessment Tool). The runoff analysis was simulated for 2000~2007, and 11 parameters were calibrated using the SUFI-2 (Sequential Uncertainty Fitting-version 2) algorithm of SWAT-CUP (Calibration and Uncertainty Program). As a result of analyzing optimal parameters and sensitivity analysis for 6 cases, the parameter ALPHA_BF was found to be the most sensitive. The reproducibility of the rainfall-runoff results decreased with increasing number of stations used for parameter calibration. The rainfall-runoff simulation results of Case 6 showed that the RMSE of Nong Khai and Kratie stations were 0.97 and 0.9, respectively, and the runoff patterns were relatively accurately simulated. The runoff patterns of Mukdahan and Khong Chaim stations were underestimated during the flood season from 2004 to 2005 but it was acceptable in terms of the overall runoff pattern. These results suggest that the combination of SWAT and SWAT-CUP models is applicable to very large watersheds such as the Mekong for rainfall-runoff simulation, but further studies are needed to reduce the range of modeling uncertainty.

• 한국물환경학회지
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• 제29권5호
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• pp.681-690
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• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.

• 산업경영시스템학회지
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• 제40권4호
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• Structural Engineering and Mechanics
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• 제84권2호
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• pp.207-224
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• 2022

The mechanical behavior of prestressed concrete haunched beams (PSHBs) was investigated in depth using a finite element modeling technique in this study. The efficiency of finite element modeling was investigated in the first stage by taking into account a previous study from the literature. The first stage's findings suggested that finite element modeling might be preferable for modeling PSHBs. In the second stage of the research, a comprehensive parametric study was carried out to determine the effect of each parameter on PSHB load capacity, including haunch angle, prestress level, compressive strength, tensile reinforcement ratio, and shear span to depth ratio. PSHBs and prestressed concrete rectangular beams (PSRBs) were also compared in terms of capacity. Stochastic analysis was used in the third stage to define the uncertainty in PSHB capacity by taking into account uncertainty in geometric and material parameters. Standard deviation, coefficient of variation, and the most appropriate probability density function (PDF) were proposed as a result of the analysis to define the randomness of capacity of PSHBs. In the study's final section, a new equation was proposed for using symbolic regression to predict the load capacity of PSHBs and PSRBs. The equation's statistical results show that it can be used to calculate the capacity of PSHBs and PSRBs.