• Journal of Korean Society on Water Environment
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• v.22 no.3
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• pp.427-430
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• 2006

Uncertainty is one of the key issues of the water quality management. Uncertainty occurs in the course of all water quality management stages including monitoring, modeling, and regulation enforcement. To reduce uncertainties of water quality monitoring, manualized monitoring methodology should be developed and implemented. In addition, long-term monitoring is essential for acquiring reliable water quality data which enables best water quality management. For the water quality management in the watershed scale, fate of pollutant including its generation, transport and impact should be considered while regarding each stage of water quality management as an unit process. Uncertainties of each stage of water quality management should be treated properly to prevent error propagation transferred to the next stage of management for successful achievement of water quality conservation.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• Nuclear Engineering and Technology
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• v.51 no.4
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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.

• Steel and Composite Structures
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• v.19 no.4
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• pp.811-827
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• 2015

This paper presents a probabilistic investigation of American and European specifications (i.e., AISC and Eurocode 4) for square concrete-filled steel tubular (CFT) stub columns. The study is based on experimental results of 100 axially loaded square CFT stub columns from the literature. By comparing experimental results for ultimate loads with code-predicted column resistances, the uncertainty of resistance models is analyzed and it is found that the modeling uncertainty parameter can be described using random variables of lognormal distribution. Reliability analyses were then performed with/without considering the modeling uncertainty parameter and the safety level of the specifications is evaluated in terms of sufficient and uniform reliability criteria. Results show that: (1) The AISC design code provided slightly conservative results of square CFT stub columns with reliability indices larger than 3.25 and the uniformness of reliability indices is no better because of the quality of the resistance model; (2) The uniformness of reliability indices for the Eurocode 4 was better than that of AISC, but the reliability indices of columns designed following the Eurocode 4 were found to be quite below the target reliability level of Eurocode 4.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.287-287
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• 2023

Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3073-3084
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• 2022

The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively.

• Computational Structural Engineering
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• v.28 no.3
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• pp.13-17
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• 2015

본 기사에서는 모델링불확실성(modeling uncertainty)에 따른 신뢰성 해석결과의 가변성(variability)을 가능성 분포함수(possibility distribution function)를 구성하여 해결하는 방법을 AISC(1998), AIJ(1985), CSA(1994)에서 제안된 3개의 최대 D/t 계산식을 예로 들어 소개하였다. 확신정도가 측정된 신뢰성지수 들을 얻을 수 있으며, 확신정도를 고려한 신뢰성지수의 결정이 가능하게 된다. 다양한 형태의 불확실성에 대하여 그 형태에 맞는 적합한 불확실성 모델링을 사용하는 것도 중요하지만, 확률적 표현에 익숙한 우리의 인지구조를 고려하여 기존의 신뢰성 해석에 어떻게 다양한 불확실성 모델링 방법을 접목시킬 것인지에 대한 연구도 중요할 것이다.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.34-44
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• 1997

This study presents the recursive constraint bounding(RCB) method to reduce the cycle time in internal cylindrical plunge grinding process. This method can cope with process noise as well as modeling bias. The main features of RCB method are its utilization of measurements at the end of each cycle and its use of monotonicity analysis for determining the extremes of bias and noise. This method is investigated in simulation and evaluated by experiment in internal cylindrical plunge grinding operation. The results from simulation and experiment show that it is effective in reducing cycle time in spite of modeling uncertainty in the forms of process noise and modeling bias.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.313-342
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• 2014

A Nuclear Energy Agency (NEA), Organization for Economic Co-operation and Development (OECD) benchmark for Uncertainty Analysis in Modeling (UAM) is defined in order to facilitate the development and validation of available uncertainty analysis and sensitivity analysis methods for best-estimate Light water Reactor (LWR) design and safety calculations. The benchmark has been named the OECD/NEA UAM-LWR benchmark, and has been divided into three phases each of which focuses on a different portion of the uncertainty propagation in LWR multi-physics and multi-scale analysis. Several different reactor cases are modeled at various phases of a reactor calculation. This paper discusses Phase I, known as the "Neutronics Phase", which is devoted mostly to the propagation of nuclear data (cross-section) uncertainty throughout steady-state stand-alone neutronics core calculations. Three reactor systems (for which design, operation and measured data are available) are rigorously studied in this benchmark: Peach Bottom Unit 2 BWR, Three Mile Island Unit 1 PWR, and VVER-1000 Kozloduy-6/Kalinin-3. Additional measured data is analyzed such as the KRITZ LEU criticality experiments and the SNEAK-7A and 7B experiments of the Karlsruhe Fast Critical Facility. Analyzed results include the top five neutron-nuclide reactions, which contribute the most to the prediction uncertainty in keff, as well as the uncertainty in key parameters of neutronics analysis such as microscopic and macroscopic cross-sections, six-group decay constants, assembly discontinuity factors, and axial and radial core power distributions. Conclusions are drawn regarding where further studies should be done to reduce uncertainties in key nuclide reaction uncertainties (i.e.: $^{238}U$ radiative capture and inelastic scattering (n, n') as well as the average number of neutrons released per fission event of $^{239}Pu$).

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.225-228
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• 2007

This paper presents a geostatistical methodology to model local uncertainty in spatial estimation of sediment grain size with high-resolution remote sensing imagery. Within a multi-Gaussian framework, the IKONOS imagery is used as local means both to estimate the grain size values and to model local uncertainty at unsample locations. A conditional cumulative distribution function (ccdf) at any locations is defined by mean and variance values which can be estimated by multi-Gaussian kriging with local means. Two ccdf statistics including condition variance and interquartile range are used here as measures of local uncertainty and are compared through a cross validation analysis. In addition to local uncertainty measures, the probabilities of not exceeding or exceeding any grain size value at any locations are retrieved and mapped from the local ccdf models. A case study of Baramarae beach, Korea is carried out to illustrate the potential of geostatistical uncertainty modeling.