• Journal of Environmental Science International
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• v.7 no.6
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• pp.773-782
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• 1998

The adsorption experiment of phenol(Ph) from aqueous solution on granular activated carbon was studied in order to design the fixed-bed adsorption column. The experimental data were analyzed by unsteady-state, one-dimensional heterogeneous model. Finite element method(FEM) was applied to analyze the sensitivity of parameter and to predict the fixed-bed adsorption column performance on operation variable changes. The prediction model showed similar effect to mass transfer and intraparticle diffusion coefficient changes suggesting that both parameter present mass transfer rate limits for GAC-phenol system. The Freundlich constants had a greater effect than kinetic parameters for the performance of fixed-bed adsorption column. FEM solution facilitated prediction of concentration history in solution and within adsorbent particle.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1093-1098
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• 2001

The interior sound pressure level of the Korean high speed train is predicted using ray acoustic method. The motor car, motorized car and passenger cabin are investigated under the environment of passing open countryside and inside tunnel Calculated sound levels of KHST are compared with the those of KTX prototype which vehicle shows similar acoustic behavior with KHST for the purpose of assuring the calculated data. In order to reduce the calculated SPL in systematic way, contribution analysis of sound sources and sensitivity analysis of concerning wall's transmission loss on the SPL of the designated receiving points are carried out. Finally, practical design suggestions are proposed.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1324-1334
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• 2005

This study investigated the applicability of the Artificial Neural Network (ANN) technique for prediction of tunnel behavior. For training data collection, a series of finite element analyses were conducted for actual tunnel project site. Using the data, optimimzed ANNs were developed through a sensitivity study on internal parameters. The developed ANNs can make tunneling related predictions such as tunnel crown settlement, shotcrete lining stress, ground surface settlement, and groundwater inflow rate. The results indicated that the developed ANNs can be used as an effective and efficient tool for tunnelling related prediction in practical tunneling situations.

• Journal of Mechanical Science and Technology
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• v.15 no.9
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• pp.1292-1301
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• 2001

The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more resonable empirical models are reeded.

• Journal of Navigation and Port Research
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• v.44 no.2
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• pp.98-109
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• 2020

In recent years, there have been concerted efforts toward predicting ship maneuvering in shallow water since the majority of ship's accidents near harbors commonly occur in shallow and restricted waters. Enhancement of ship maneuverability at the design stage is crucial in ensuring that a ship navigates safely. However, though challenging, establishing the mathematical model of ship maneuvering motion is recognized as crucial toward accurately predicting the assessment of maneuverability. This paper focused on a study on sensitivity analysis of the hydrodynamic coefficients on the maneuverability prediction of KVLCC2 in shallow waters. Hydrodynamic coefficients at different water depths were estimated from the experimental results conducted in the square tank at Changwon National University (CWNU). The simulation of standard maneuvering of KVLLC2 in shallow waters was compared with the results of the Free Running Model Test (FRMT) in shallow waters from other institutes. Additionally the sensitivity analysis of all hydrodynamic coefficients was conducted by deviating each hydrodynamic derivative from the experimental results. The standard maneuvering parameters including turning tests and zig-zag maneuvers were conducted at different water depths and their effects on the standard maneuvering parameters were assessed to understand the importance of different derivatives in ship maneuvering in shallow waters.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1255-1266
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to predict the association between nutritional intake and diabetes mellitus (DM) by developing an artificial neural network (ANN) model for older adults. SUBJECTS/METHODS: Participants aged over 65 years from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey were included. The diagnostic criteria of DM were set as output variables, while various nutritional intakes were set as input variables. An ANN model comprising one input layer with 16 nodes, one hidden layer with 12 nodes, and one output layer with one node was implemented in the MATLAB^® programming language. A sensitivity analysis was conducted to determine the relative importance of the input variables in predicting the output. RESULTS: Our DM-predicting neural network model exhibited relatively high accuracy (81.3%) with 11 nutrient inputs, namely, thiamin, carbohydrates, potassium, energy, cholesterol, sugar, vitamin A, riboflavin, protein, vitamin C, and fat. CONCLUSIONS: In this study, the neural network sensitivity analysis method based on nutrient intake demonstrated a relatively accurate classification and prediction of DM in the older population.

• Atmosphere
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• v.20 no.3
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• pp.287-306
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• 2010

An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF) Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size, assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all, experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.08a
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• pp.313-319
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• 1999

The development of deformation texture in FCC polycystalline metals during rolling was simulated by the finite element analysis using a large-deformation, elaatic-plastic, rate-dependent polycrystalline model of crystal plasticity. Different plastic anisotropy due to different orientation of each crystal makes inhomogeneous deformation. Assuming plane strain compression condition, the simulation with a high rate sensitivity resulted in main component change from Dillamore at low rate sensitivity to Brass component.

• Atmosphere
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• v.27 no.1
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• pp.17-28
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• 2017

The sensitivity of the typhoon track and intensity simulation to physics schemes of the global model are examined for the typhoon Bolaven and Tembin cases by using the Global/Regional Integrated Model System-Global Model Program (GRIMs-GMP) with the physics package version 2.0 of the Korea Institute of Atmospheric Prediction Systems. Microphysics, Cloudiness, and Planetary boundary Layer (PBL) parameterizations are changed and the impact of each scheme change to typhoon simulation is compared with the control simulation and observation. It is found that change of microphysics scheme from WRF Single-Moment 5-class (WSM5) to 1-class (WSM1) affects to the typhoon simulation significantly, showing the intensified typhoon activity and increased precipitation amount, while the effect of the prognostic cloudiness and PBL enhanced mixing scheme is not noticeable. It appears that WSM1 simulates relatively unstable and drier atmospheric structure than WSM5, which is induced by the latent heat change and the associated radiative effect due to not considering ice cloud. And WSM1 results the enhanced typhoon intensity and heavy rainfall simulation. It suggests that the microphysics is important to improve the capability for typhoon simulation of a global model and to increase the predictability of medium range forecast.

• Environmental Engineering Research
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• v.25 no.3
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• pp.418-425
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• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.