• Journal of Korean Society on Water Environment
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• v.30 no.5
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• pp.491-502
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• 2014

As meteorology is the driving force for lake thermodynamics and mixing processes, the effects of climate change on the physical limnology and associated ecosystem are emerging issues. The potential impacts of climate change on the physical features of a reservoir include the heat budget and thermodynamic balance across the air-water interface, formation and stability of the thermal stratification, and the timing of turn over. In addition, the changed physical processes may result in alteration of materials and energy flow because the biogeochemical processes of a stratified waterbody is strongly associated with the thermal stability. In this study, a novel modeling framework that consists of an artificial neural network (ANN), a watershed model (SWAT), a reservoir operation model(HEC-ResSim) and a hydrodynamic and water quality model (CE-QUAL-W2) is developed for projecting the effects of climate change on the reservoir water temperature and thermal stability. The results showed that increasing air temperature will cause higher epilimnion temperatures, earlier and more persistent thermal stratification, and increased thermal stability in the future. The Schmidt stability index used to evaluate the stratification strength showed tendency to increase, implying that the climate change may have considerable impacts on the water quality and ecosystem through changing the vertical mixing characteristics of the reservoir.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.177-186
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• 2019

Wind power tower has been used to augment the performance of VAWT (Vertical Axis Wind Turbine). However, inappropriately designed wind power tower could reduce the performance of VAWT. Hence, an optimization study was conducted on a wind power tower. Six design variables were selected, such as the outer radius and the inner radius of the guide wall, the adoption of the splitter, the inner radius of the splitter, the number of the guide wall and the circumferential angle. For the objective function, the periodic averaged torque obtained at the VAWT was selected. In the optimization, Design of Experiment (DOE), Genetic Algorithm (GA), and Artificial Neural Network (ANN) have been applied in order to avoid a localized optimized result. The ANN has been continuously improved after finishing the optimization process at each generation. The performance of the VAWT was improved more than twice when it operated within the optimized wind power tower compared to that obtained at a standalone.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Journal of the Korean Geotechnical Society
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• v.23 no.8
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• pp.117-127
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• 2007

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of them are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for integrating commercial finite difference method into probabilistic analysis of slope stability is presented. Since the limit-state function cannot be expressed in an explicit form, the ANN-based response surface method is adopted to approximate the limit-state function and the first-, second-order reliability method and the Monte Carlo simulation technique are used to calculate the probability of failure. Probabilistic stability assessments for a hypothetical two-layer slope and the Sugar Creek embankment were performed to verify the application potential to the slope stability problems. The examples show the successful implementation and the possibility of the extension of the proposed procedure to the variety of geotechnical engineering problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3C
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• pp.167-178
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• 2008

Rainfall-induced slope failures were simulated by seepage and stability analyses for actual slopes of weathered soils. After undisturbed sampling and testing on a specimen of unsaturated conditions, a seepage analysis was performed under actual rainfall and it was found that the pore water pressure increased at the boundary of soil and rock layers. The safety factor of slope stability decreased below 1.0 and the failure of actual slope could be simulated. Under design rainfall intensity, the seepage analysis could not include the effects of the antecedent rainfall and the rainfall duration. Due to these limitations, the safety factor of slope stability resulted in above 1.0, since the hydraulic head of soil layers had not be affected significantly. In the analysis of another slope failure, the parameters of unsaturated conditions were evaluated using artificial neural network (ANN). In the analysis of seepage, the boundary of soil and rock was saturated sufficiently and then the safety factor could be calculated below 1.0. It was found that the failure of actual slope can be simulated by ANN-based estimation.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.161-172
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• 2017

In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.55-66
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• 2012

The practical way to minimize the greenhouse gas is to reduce the emission of carbon dioxide. For this reason, CCS(Carbon Capture and Storage) technology, which could reduce carbon dioxide emission, has risen as a realistic alternative in recent years. In addition, the researcher is recently working into ways of applying CCS technologies with deep saline aquifer. In this study, the evaluation model on the feasibility of $CO_2$ sequestration in the deep saline aquifer using ANN(Artificial Neural Network) was developed. In order to develop the efficiency-evaluation model, basic model was created in the deep saline aquifer and sensitivity analysis was performed for the aquifer characteristics by utilizing the commercial simulator of GEM. Based on the sensitivity analysis, the factors and ranges affecting $CO_2$ sequestration in the deep saline aquifer were chosen. The result from ANN training scenario were confirmed $CO_2$ sequestration by solubility trapping and residual trapping mechanism. The result from ANN model evaluation indicated there is the increase of correlation coefficient up to 0.99. It has been confirmed that the developed model can be utilized in feasibility of $CO_2$ sequestration at deep saline aquifer.

• Journal of the Korea Concrete Institute
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• v.20 no.4
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• pp.513-522
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• 2008

The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC (Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.