• Journal of Korea Water Resources Association
• /
• v.50 no.11
• /
• pp.735-743
• /
• 2017

With growing concerns about ever-increasing anthropogenic greenhouse gas emissions, it is crucial to enhance preparedness for unprecedented extreme weathers that can bring catastrophic consequences. In this study, we proposed a stochastic framework that considers uncertainty in weather forecasts for flood analyses. First, we calibrated a simple rainfall-runoff model against observed hourly hydrographs. Then, using probability density functions of rainfall depths conditioned by 6-hourly weather forecasts, we generated many stochastic rainfall depths for upcoming 48 hours. We disaggregated the stochastic 6-hour rainfalls into an hourly scale, and input them into the runoff model to quantify a probabilistic range of runoff during upcoming 48 hours. Under this framework, we assessed two rainfall events occurred in Bocheong River Basin, South Korea in 2017. It is indicated actual flood events could be greater than expectations from weather forecasts in some cases; however, the probabilistic runoff range could be intuitive information for managing flood risks before events. This study suggests combining deterministic and stochastic methods for forecast-based flood analyses to consider uncertainty in weather forecasts.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.2
• /
• pp.351-358
• /
• 1994

A probabilistic model and analysis methods to determine the means and variances of the velocity and acceleration in stochastically-defined planar pin jointed kinematic chains are presented. The presented model considers the effect of tolerances on link length and radial clearance and uncertainty of pin location as a net effect on the link's effective length. The determination of the mean values and variances of the output variables requires the calculation of sensitivities of secondary variables with respect to the random variables. It is shown that this computation is straightforward and can be accomplished by a conventional kinematic analysis package with minor modification. Thus, the concepts of tolerance and clearance have been captured by the model and analysis. The only input data are the nominal linkage model and statistical information. The "effective link length" model is shown to be applicable to both analytical solution and Monte Carlo simulation. The results from both methods are compared. This paper Ksolves the higher-order kinematic problems for the probabilistic design analysis of stochastically-defined mechanisms.echanisms.

• Smart Structures and Systems
• /
• v.17 no.4
• /
• pp.611-629
• /
• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.437-437
• /
• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• Nuclear Engineering and Technology
• /
• v.55 no.12
• /
• pp.4607-4616
• /
• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.102-106
• /
• 2008

This study is to evaluated the impact of varying spatial resolutions of DEM (2 m, 10 m, and 30 m), land use (QuickBird, 1/25,000 and Landsat), and soil data (1/25,000 and 1/50,000) on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, sediment, T-N, and T-P transport in a small agricultural watershed ($1.21\;km^2$). SWAT model was adopted and the model was calibrated for a $255.4\;km^2$ watershed using 30 m DEM, Landsat land use, and 1/25,000 soil data. The model was run with the combination of three DEM, land use, and soil map respectively. The SWAT model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality (SS, T-N, T-P) records from 1999 to 2000, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and the root mean square error were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow.

• Journal of the Korean Solar Energy Society
• /
• v.34 no.4
• /
• pp.23-29
• /
• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• Journal of Fisheries and Marine Sciences Education
• /
• v.29 no.2
• /
• pp.513-525
• /
• 2017

In the estimation of the exploitable carrying capacity (ECC) in the Korean water of the East China Sea, two approaches, which are the ecosystem modeling method (EMM) and the holistic production method (HPM), were applied. The EMM is accomplished by Ecopath with Ecosim model using a number of ecological data and fishery catch for each species group, which was categorized by a self-organizing mapping (SOM) based on eight biological characteristics of species. In this method, the converged value during the Ecosim simulation by setting the instantaneous rate of fishing mortality (F) as zero was estimated as the ECC of each group. The HPM is to use surplus production models for estimateing ECC. The ECC estimates were 4.6 and 5.1 million mt (mmt) from EMM and HPM, respectiverly. The estimate from the EMM has a considerable uncertainty due to the lack of confidence in input ecological parameters, especially production/biomass ratio (P/B) and consumption/biomass ratio (Q/B). However, ECC from the HPM was estimated on the basis of relatively fewer assumptions and long time-series fishery data as input, so the estimate from the HPM is regarded as more reasonable estimate of ECC, although the ECC estimate could be considerd as a preliminary one. The quality of input data should be improved for the future study of the ECC to obtain more reliable estimate.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.641-649
• /
• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Proceedings of the Korean Nuclear Society Conference
• /
• 1996.05b
• /
• pp.513-518
• /
• 1996

This report presents a demonstration of application of realistic evaluation methodology to a posturated cold leg large break LOCA in a Westinghouse three-loop pressurized water reactor with 17$\times$17 fuel. The new method of this analysis can be divided into three distinct step: 1) Best Estimate Code Validation and Uncertainty Quantification 2) Realistic LOCA Calculation 3) Limiting Value LOCA Calculation and Uncertainty Combination RELAP5/MOD3/K ［1］, which was improved from RELAP5/MOD3.1, and CONTEMPT4/MOD5 code were used as a best estimate thermal-hydraulic model for realistic LOCA calculation. The code uncertainties which will be determined in step 1) were quantified already in previous study ［2］, and thus the step 2) and 3) for plant application were presented in this paper. The application uncertainty parameters are divided into two categories, i.e. plant system parameters and fuel statistical parameters. Single parameter sensitivity calculations were performed to select system parameters which would be set at their limiting value in Limiting Value Approach (LVA) calculation. Single run of LVA calculation generated 27 PCT data according to the various combinations of fuel parameters and these data provided input to response surface generation. The probability distribution function was generated from Monte Carlo sampling of a response surface and the upper 95$^{th}$ percentile PCT was determined. Break spectrum analysis was also made to determine the critical break size. The results show that sufficient LOCA margin can be obtained for the demonstration NPP.