• Journal of the Korean Society of Safety
• /
• v.28 no.3
• /
• pp.123-128
• /
• 2013

The Advanced Power Reactor Plus(APR+), which is a GEN III+ reactor based on the APR1400, is being developed in Korea. In order to enhance the safety of the APR+, a passive auxiliary feedwater system(PAFS) has been adopted in the APR+. The PAFS replaces the conventional active auxiliary feedwater system(AFWS) by introducing a natural driving force mechanism while maintaining the system function of cooling the primary side and removing the decay heat. As the PAFS completely replaces the conventional AFWS, it is required to verify the cooling capacity of PAFS for the core damage frequency(CDF) evaluation. For this reason, this paper discusses the cooling performance of the PAFS during transient accidents. The test case and scenarios were picked from the result of the sensitivity analysis in APR+ Probabilistic Safety Assessment(PSA). The analysis was performed by the best estimate thermal-hydraulic code, RELAP5/.MOD3.3. This study shows that the plant maintains the stable state without the core damages under the given test scenarios. The results of PSA considering this analysis' results shows that the CDF values are decreased. The analysis results can be used for more realistic and accurate performance of a PSA.

• Nuclear Engineering and Technology
• /
• v.42 no.6
• /
• pp.690-703
• /
• 2010

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

• Genomics & Informatics
• /
• v.18 no.3
• /
• pp.30.1-30.7
• /
• 2020

The novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) play an important role in understanding the concept of genetic variation. In this paper, the genomic data accessed from National Center for Biotechnology Information (NCBI) through Molecular Evolutionary Genetic Analysis (MEGA) for statistical analysis. Firstly, the Bayesian information criterion (BIC) and Akaike information criterion (AICc) are used to evaluate the best substitution pattern. Secondly, the maximum likelihood method used to estimate of transition/transversions (R) through Kimura-2, Tamura-3, Hasegawa-Kishino-Yano, and Tamura-Nei nucleotide substitutions model. Thirdly and finally nucleotide frequencies computed based on genomic data of NCBI. The results indicate that general times reversible model has the lowest BIC and AICc score 347,394 and 347,287, respectively. The transition/transversions bias for nucleotide substitutions models varies from 0.56 to 0.59 in MEGA output. The average nitrogenous bases frequency of U, C, A, and G are 31.74, 19.48, 28.04, and 20.74, respectively in percentages. Overall the genomic data analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV highlights the close genetic relationship.

• Journal of the Korean Society of Systems Engineering
• /
• v.15 no.1
• /
• pp.51-59
• /
• 2019

After Fukushima Dai-ichi NPP accident, the need for implementation of diverse and flexible coping strategies (FLEX) became evident. However, to ensure the effectiveness of the safety strategy, it is essential to quantify the uncertainties associated with the station blackout (SBO) scenario as well as the operator actions. In this paper, a systems engineering approach for uncertainty analysis (UA) of a SBO scenario in advanced pressurized water reactor is performed. MARS-KS is used as a best estimate thermal-hydraulic code and is loosely-coupled with Dakota software which is employed to develop the uncertainty quantification framework. Furthermore, the systems engineering approach is adopted to identify the requirements, functions and physical architecture, and to develop the verification and validation plan. For the preliminary analysis, 13 uncertainty parameters are propagated through the model to evaluate the stability and convergence of the framework. The developed framework will ultimately be used to quantify the aleatory and epistemic uncertainties associated with an extended SBO accident scenario and assess the coping capability of APR1400 and the effectiveness of the implemented FLEX strategies.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.36 no.12
• /
• pp.1605-1612
• /
• 2012

The distribution of a response usually depends on the distribution of the variables. When a variable shows a distribution with two different modes, the response also shows a distribution with two different modes. In this case, recently developed methods for reliability analysis assume that the distribution functions are continuous with a mode. In actual problems, however, because information is often provided in a discrete form with two or more modes, it is important to estimate the distributions for such information. In this study, we employ the finite mixture model to estimate the response distribution with two different modes, and we select the best candidate distribution through AIC. Mathematical examples are illustrated to verify the proposed method.

• The Korean Journal of Applied Statistics
• /
• v.34 no.6
• /
• pp.957-968
• /
• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• Animal Bioscience
• /
• v.34 no.10
• /
• pp.1623-1631
• /
• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.22 no.3
• /
• pp.75-87
• /
• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of Mechanical Science and Technology
• /
• v.19 no.5
• /
• pp.1182-1193
• /
• 2005

The capability of TRAC-M code to predict downcomer boiling effect during reflood phase in postulated PWR LOCA is evaluated using the results of downcomer effective water head and Cylindrical Core Test Facility (CCTF) experiments, which were performed at JAERI. With a full height downcomer simulator, effective water head experiment was carried out to investigate the applicability of the TRAC-M best estimate LOCA code to evaluate the effective water head with superheated wall temperature in downcomer. In order to clarify the effect of the initial superheat of the downcomer wall on the system and the core cooling behaviors during the reflood phase, two sets of analysis were also performed with a CCTF. Results show that TRAC­M code tends to under-predict downcomer effective water head and core differential pressure. However, the code results show a good agreement with the experimental results in downcomer temperature, heat flux and pressure. Finally, both experiment and calculation showed that the downcomer water head with the superheated downcomer wall is lower than that of the saturated wall temperature.

• Nuclear Engineering and Technology
• /
• v.18 no.1
• /
• pp.38-47
• /
• 1986

A statistical procedure, which uses response surface method and Monte Carlo simulation technique, is proposed for analyzing the thermal margin of light water reactor core. The statistical thermal margin analysis method performs the best.estimate thermal margin evaluation by the probabilistic treatment of uncertainties of input parameters. This methodology is applied to KNU-1 core thermal margin analysis under the steady state nominal operating condition. Also discussed are the comparisons with conventional deterministic method and Improved Thermal Design Procedure of Westinghouse. It is deduced from this study that the response surface method is useful for performing the statistical thermal margin analysis and that thermal margin improvement is assured through this procedure.