• Korean Journal of Materials Research
• /
• v.33 no.7
• /
• pp.273-278
• /
• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• Membrane Journal
• /
• v.10 no.1
• /
• pp.30-38
• /
• 2000

Prediction method of permeation flux and sorption characteristics in pervaporation through a polydimethylsiloxane(PDMS) memrane was suggested. The amount of sorption and permeation flux of chloroform, toluene, methanol and n-butanol were calculated with this method and compared with this method and compared with experimetal data. The calculated values of permeation flux and the amount of sorption of good solvents, that is, toluene and chloroform were well agreed with the experimental data. The lower the density of PDMS membrane is, the more permeation flux and sorption quantity were increased. However, the experimental data of poor solvents, that is, methanol and n-butanol were no so well agreed with the calculated values. It is shown that the prediction method suggested in this study may be used without experimetnal for the prediction of permeation flux and sorption quantity of the good solvent on PDMS membrane.

• Journal of Mechanical Science and Technology
• /
• v.17 no.8
• /
• pp.1171-1184
• /
• 2003

An experimental study on critical heat flux (CHF) has been performed in an internally heated vertical annulus with non-uniform heating. The CHF data for the chopped cosine heat flux have been compared with those for uniform heat flux obtained from the previous study of the authors, in order to investigate the effect of axial heat flux distribution on CHF. The local CHF with the parameters such as mass flux and critical quality shows an irregular behavior. However, the total critical power with mass flux and the average CHF with critical quality are represented by a unique curve without the irregularity. The effect of the heat flux distribution on CHF is large at low pressure conditions but becomes rapidly smaller as the pressure increases. The relationship between the critical quality and the boiling length is represented by a single curve, independent of the axial heat flux distribution. For non-uniform axial heat flux distribution, the prediction results from Doerffer et al.'s and Bowling's CHF correlations have considerably large errors, compared to the prediction for uniform heat flux distribution.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.1
• /
• pp.93-100
• /
• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

• Proceedings of the KSME Conference
• /
• 2001.11b
• /
• pp.288-293
• /
• 2001

A critical heat flux (CHF) prediction method using an artificial neural network (ANN) was evaluated for application to the high-heat-flux (HHF) subcooled flow boiling. The developed ANN predictions were compared with the experimental database consisting of a total of 3069 CHF data points. Also, the prediction performance by the ANN was compared with those by mechanistic models and a look up table technique. The parameter ranges of the experimental data are: $0.33{\leq}D{\leq}37.5mm$, $0.002{\leq}L{\leq}4m$, $0.37{\leq}G{\leq}134Mg/m^2s$, $0.1{\leq}P{\leq}20MPa$, $50\leq{\Delta}h_{sub,in}\leq1660kJ/kg$, and $1.1{\leq}q_{CHF}\leq276MW/m^2$. $276MW/m^2$. It was found that 91.5% of the total data points were predicted within $a{\pm}20%$ error band, which showed the best prediction performance among the existing CHF prediction methods considered.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.35 no.9
• /
• pp.826-835
• /
• 2007

Solar heat flux data is needed for thermal load prediction of launch vehicle. In order to predict the solar flux, several solar flux models have been compared and a new model is developed. Most of the models can predict well the direct solar flux, but show some errors in the scattered solar flux. The newly developed model considered isotropic and anisotropic scattered solar fluxes, and the predicted solar flux agreed well with the measured. Because the present model can be used at any longitude, latitude, day and altitude, the model would be an useful tool to predict the thermal load of the launch vehicle and the vehicles which have to consider the solar heat.

• Nuclear Engineering and Technology
• /
• v.54 no.3
• /
• pp.1126-1135
• /
• 2022

The MARS code has been assessed for the prediction of onset of flow instability (OFI) in a vertical channel. For assessment, we built an experiment database that consists of experiments under various geometry and thermal-hydraulic condition. It covers pressure from 0.12 to 1.73 MPa; heat flux from 0.67 to 3.48 MW/m²; inlet sub-cooling from 39 to 166 ℃; hydraulic diameters between 2.37 and 6.45 mm of rectangular channels and pipes. It was shown that the MARS code can predict the OFI mass flux for pipes reasonably well. However, it could not predict the OFI in a rectangular channel well with a mean absolute percentage error of 8.77%. In the cases of rectangular channels, the error tends to depend on the hydraulic diameter. Because the OFI is directly related to the subcooled boiling in a flow channel, we suggest a modified subcooled boiling model for better prediction of OFI in a rectangular channel; the net vapor generation (NVG) model and the modified wall evaporation model were modified so that the effect of hydraulic diameter and heat flux can be accurately considered. The assessment of the modified model shows the prediction of OFI mass flux for rectangular channels is greatly improved.

• Architectural research
• /
• v.2 no.1
• /
• pp.41-46
• /
• 2000

The sawtooth skylight is an excellent daylighting concept for the uniform interior illuminance over large working areas. In computer simulation, it is difficult for an architect to get accurate daylight illuminances for the spaces where sawtooth apertures are applied. In this study, daylight prediction algorithms for sawtooth apertures are developed. The flux transfer method is applied for this study to predict daylight illuminances. The simplified equations from this study can be used effectively for preliminary prediction of daylight in sawtooth spaces.

• 한국전산유체공학회:학술대회논문집
• /
• 2004.10a
• /
• pp.221-225
• /
• 2004

Evaluation of turbulence models is performed for a better prediction of thermal stratification in an upper plenum of a liquid metal reactor by applying them to the experiment conducted at JNC. The turbulence models tested in the present study are the two-layer model, the $\kappa-\omega$ model, the v2-f model and the low-Reynolds number differential stress-flux model. When the algebraic flux model or differential flux model are used for treating the turbulent heat flux, there exist little differences between turbulence models in predicting the temporal variation of temperature. However, the v2-f model and the low-Reynolds number differential stress-flux model better predict the steep gradient o( temperature at the interface of thermal stratification, and only the v2-f model predicts properly the oscillation of temperature. The LES Is needed for a better prediction of the amplitude and frequency of the temperature fluctuation.

• Journal of Ocean Engineering and Technology
• /
• v.22 no.1
• /
• pp.13-21
• /
• 2008

In this study a method to predict CHF(Critical heat flux) in vertical round tubes with axially non-uniform cosine heat flux distribution for water was examined. For this purpose a local condition hypothesis based CHF prediction correlation for uniform heat flux in vertical round tubes for water was developed from 9,366 CHF data points. The local correlation consisted of 4 local condition variables: the system pressure(P), tube diameter(D), mass flux of water(G), and 'true mass quality' of vapor($X_t$). The CHF data points used were collected from 13 different published sources having the following operation ranges: 1.01 ${\leq}$ P (pressure) ${\leq}$ 206.79 bar, 9.92${\leq}$ G (mass flux) ${\leq}$ 18,619.39 $kg/m^2s$, 0.00102 ${\leq}$ D(diameter) ${\leq}$ 0.04468 m, 0.0254${\leq}$ L (length) ${\leq}$ 4.966 m, 0.11 ${\leq}$ qc (CHF) ${\leq}$ 21.41 $MVW/m^2$, and -0.87 ${\leq}X_c$ (exit qualities) ${\leq}$ 1.58. The result of this work showed that a uniform CHF correlation can be easily extended to predict CHF in axially non-uniform heat flux heater. In addition, the location of the CHF in axially non-uniform tube can also be determined. The local uniform correlation predicted CHF in tubes with axially cosine heat flux profile within the root mean square error of 12.42% and average error of 1.06% for 297 CHF data points collected from 5 different published sources.