• Title/Summary/Keyword: uncertainty analysis

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UNCERTAINTY ANALYSIS OF DATA-BASED MODELS FOR ESTIMATING COLLAPSE MOMENTS OF WALL-THINNED PIPE BENDS AND ELBOWS

  • Kim, Dong-Su;Kim, Ju-Hyun;Na, Man-Gyun;Kim, Jin-Weon
    • Nuclear Engineering and Technology
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    • v.44 no.3
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    • pp.323-330
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    • 2012
  • The development of data-based models requires uncertainty analysis to explain the accuracy of their predictions. In this paper, an uncertainty analysis of the support vector regression (SVR) model, which is a data-based model, was performed because previous research showed that the SVR method accurately estimates the collapse moments of wall-thinned pipe bends and elbows. The uncertainty analysis method used in this study was an analytic uncertainty analysis method, and estimates with a 95% confidence interval were obtained for 370 test data points. From the results, the prediction interval (PI) was very narrow, which means that the predicted values are quite accurate. Therefore, the proposed SVR method can be used effectively to assess and validate the integrity of the wall-thinned pipe bends and elbows.

Uncertainty Analysis of Fire Modeling Input Parameters for Motor Control Center in Switchgear Room of Nuclear Power Plants (원자력발전소 모터제어반 스위치기어실 화재 모델링 입력변수 불확실성 분석)

  • Kang, Dae-Il;Yang, Joon-Eon;Yoo, Seong-Yeon
    • Fire Science and Engineering
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    • v.26 no.2
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    • pp.40-52
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    • 2012
  • This paper presents the uncertainty analysis results of fire modeling input parameters for motor control center in switchgear room of nuclear power plants. FDS (Fire Dynamics simulator) 5.5 was used to simulate the fire scenario and Latin Hyper Cube Monte Carlo simulations were employed to generate random samples for FDS input parameters. The uncertainty analysis results of input parameters are compared with those of the model uncertainty analysis and sensitivity analysis approaches of NUREG-1934. The study results show that the input parameter uncertainty analysis approach may lead to more conservative results than the uncertainty analysis and sensitivity analysis methods of NUREG-1934.

Sensitivity and Uncertainty Analysis of Two-Compartment Model for the Indoor Radon Pollution (실내 라돈오염 해석을 위한 2구역 모델의 민감도 및 불확실성 분석)

  • 유동한;이한수;김상준;양지원
    • Journal of Korean Society for Atmospheric Environment
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    • v.18 no.4
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    • pp.327-334
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    • 2002
  • The work presents sensitivity and uncertainty analysis of 2-compartment model for the evaluation of indoor radon pollution in a house. Effort on the development of such model is directed towards the prediction of the generation and transfer of radon in indoor air released from groundwater. The model is used to estimate a quantitative daily human exposure through inhalation of such radon based on exposure scenarios. However, prediction from the model has uncertainty propagated from uncertainties in model parameters. In order to assess how model predictions are affected by the uncertainties of model inputs, the study performs a quantitative uncertainty analysis in conjunction with the developed model. An importance analysis is performed to rank input parameters with respect to their contribution to model prediction based on the uncertainty analysis. The results obtained from this study would be used to the evaluation of human risk by inhalation associated with the indoor pollution by radon released from groundwater.

Uncertainty Study of Added Resistance Experiment (부가저항 실험의 불확실성 연구)

  • Park, Dong-Min;Lee, Jaehoon;Kim, Yonghwan
    • Journal of the Society of Naval Architects of Korea
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    • v.51 no.5
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    • pp.396-408
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    • 2014
  • In this study, uncertainty analysis based on ITTC(International Towing Tank Conference) Recommended Procedures is carried out in the towing-tank experiment for motion responses and added resistance. The experiment was conducted for KVLCC2 model in head sea condition. The heave, pitch and added resistance were measured in different wave conditions, and the measurement was repeated up to maximum 15 times in each wave condition in order to observe the uncertainty of measured data. The uncertainty analysis was carried out by adopting the ISO-GUM(International Organization for Standardization, Guide to the Expression of Uncertainty in Measurements) method recommended by ITTC. This paper describes the details about the analysis method, uncertainty and the measured uncertainty for each source. The uncertainty analysis results are summarized as a tabular form. To validate the accuracy of the present measurement, the experimental results are compared with the results of numerical computation and other experiment. From the present uncertainty analysis, the main sources of uncertainty are identified, which can be very useful to improve the accuracy for added resistance experiment.

Uncertainty Analysis for the Resistance and Self-Propulsion Test of Ship Model (저항, 자항시험에 있어서의 불확실성 해석)

  • 박동우;김민규;강선형
    • Journal of the Society of Naval Architects of Korea
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    • v.40 no.5
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    • pp.1-9
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    • 2003
  • To predict the powering performance of full scale ships from the towing tank tests, resistance, propeller open water and self-propulsion tests are conducted. Model tests inevitably include the experimental error defined as the sum of two types of uncertainties, bias and precision errors. The induced errors in each element of model test are propagated through various routes and correlated with one another. The correlation coefficients are very important in the uncertainty analysis. The coefficient gives a direction(increase or decrease) for a value of error in individual elements. If the coefficient is not used accurately, the error bounds of the individual elements are overestimated or underestimated. In this study, the new methodology is applied to the uncertainty analysis of HMRI's towing tank tests, thus error bounds of each element is suggested and verified by several repetitive experiments.

Parameter Optimization and Uncertainty Analysis of the Rainfall-Runoff Model (강우-유출모형 매개변수의 최적화 및 불확실성 분석)

  • Moon, Young-Il;Kwon, Hyun-Han
    • 한국방재학회:학술대회논문집
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    • 2008.02a
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    • pp.723-726
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    • 2008
  • It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

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AN ASSESSMENT OF UNCERTAINTY ON A LOFT L2-5 LBLOCA PCT BASED ON THE ACE-RSM APPROACH: COMPLEMENTARY WORK FOR THE OECD BEMUSE PHASE-III PROGRAM

  • Ahn, Kwang-Il;Chung, Bub-Dong;Lee, John C.
    • Nuclear Engineering and Technology
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    • v.42 no.2
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    • pp.163-174
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    • 2010
  • As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

Uncertainty Analysis for the Propeller Open Water Test (프로펠러 단독시험에 있어서 불확실성 해석)

  • G.I. Choi;H.H. Chun;J.S. Kim;C.M. Lee
    • Journal of the Society of Naval Architects of Korea
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    • v.31 no.1
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    • pp.71-83
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    • 1994
  • Recently, an interest in the uncertainty analysis on measurement and prediction has been growing. An uncertainty analysis method is applied to the P.O.W test where error sources, estimated errors, their propagation route and their sensitivities to the uncertainty items are clearly illustrated. The uncertainty range for the results obtained from the HMRI Propeller Open Water test is within ${\pm}1%$ which is assumed to be lower than an usual measurement error range of ${\pm}1%$. It has been noticed that the uncertainty analysis can be used quite usefully for detecting dominant error-sources and hence improving the experimental measurement accuracy.

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Uncertainty Analysis of Concrete Structures Using Modified Latin Hypercube Sampling Method

  • Yang, In-Hwan
    • International Journal of Concrete Structures and Materials
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    • v.18 no.2E
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    • pp.89-95
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    • 2006
  • This paper proposes a modified method of Latin Hypercube sampling to reduce the variance of statistical parameters in uncertainty analysis of concrete structures. The proposed method is a modification of Latin Hypercube sampling method. This analysis method uses specifically modified tables of random permutations of ranked numbers. In addition, the Spearman coefficient is used to make modified tables. Numerical analysis is carried out to predict the uncertainty of axial shortening in prestressed concrete bridge. Statistical parameters obtained from modified Latin Hypercube sampling method and conventional Latin Hypercube sampling method are compared and evaluated by a numeric analysis. The results show that the proposed method results in a decrease in the variance of statistical parameters. This indicates the method is efficient and effective in the uncertainty analysis of complex structural system such as prestressed concrete bridges.

A Bayesian uncertainty analysis for nonignorable nonresponse in two-way contingency table

  • Woo, Namkyo;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1547-1555
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    • 2015
  • We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.