• 한국연소학회:학술대회논문집
• /
• 한국연소학회 2006년도 제32회 KOSCO SYMPOSIUM 논문집
• /
• pp.39-45
• /
• 2006

Analysis of the internal state of the blast furnace is needed to predict and control the operating condition. Especially, it is important to develop modeling of blast furnace for predicting cohesive zone because shape of cohesive zone influences overall operating condition of blast furnace such as gas flow, chemical reactions and temperature. because many previous blast furnace models assumed cohesive zone to be fixed, they can't evaluate change of cohesive zone shape by operation condition such as PCR, blast condition, and production rate. In this study, an axi-symmetric 2-dimensional steady state model is proposed to simulate blast furnace process. In this model, cohesive zone is changed by solid temperature range, FVM is used for numerical simulation. To find location of cohesive zone whole calculation procedure is iterated Until cohesive zone is converged. Through this approach, shape of cohesive zone, velocity, composition and temperature within the furnace are predicted by model.

• 한국안전학회지
• /
• 제19권3호
• /
• pp.89-94
• /
• 2004

Most reinforcements in concrete are constructed by steel. Corrosion of reinforcement is the main cause of damage and early failure of reinforced concrete structures. The corrosion is mainly professed by the chloride ingress. In general, chloride in concrete can be discriminated by two components, total chloride and fire chloride. This paper provides a testing method on the coefficient of chloride diffusion in concrete and the relationship between total chloride and free chloride in concrete for the composition of predicting model on diffusion rate of chloride. In order to complete this predicting model, this study will use chloride penetration characteristic, diffusion coefficient and experiment of color change on silver nitrate solution. This predicting model is going to help that grasp special quality on salt content inclusion of concrete structure that is exposed in chloride environment. Accurate predicting model can be effectively used not only in selecting of repair time but also in preventing from various deteriorations.

• 상하수도학회지
• /
• 제26권6호
• /
• pp.871-881
• /
• 2012

This study suggested a model that can predict a degradation condition over time of two plastic pipes, PE and PVC, which are currently used in the country. This study was analyzed physical characteristics change of plastic pipes by comparison with initial physical characteristics (on the case of new pipes). Since this is dependent on accidents that already occurred, there are limitations that it only decides a priority on improvement based on relative corrosion status rather than precautionary aspects. The comparison results between physical degradation by the deducted performance rating and a conventional numerical scoring method showed that correlation coefficient was 0.67 for PE pipes and 0.86 for PVC pipes, indicating a high correlation. According to this result, it has been decided that the performance rating suggested herein can be applied naturally to the criterion of an improvement decision, which was based on Scoring System. From results of the research, it is expected that a reliable result can be provided to an improvement decision process related to degradation of plastic pipes by comprehensively comparing and evaluating a condition of pipe materials(direct factors) and an environmental impact(indirect factors).

• International Journal of Fluid Machinery and Systems
• /
• 제5권4호
• /
• pp.143-151
• /
• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2005년도 하계학술대회 논문집 Vol.6
• /
• pp.115-117
• /
• 2005

In this paper, we formed the shallow junction by preamorphization and low energy ion implantation. And a simulator is designed for predicting the annealing process results. Especially, if considered the applicable to single step annealing process(RTA, FA) and dual step annealing process(RTA+FA, FA+RTA). In this simulation, the ion implantation model and the boron diffusion model are used. The Monte Carlo model is used for the ion implantation. Boron diffusion model is based on pair diffusion at nonequilibrium condition. And we considered that the BI-pairs lead the diffusion and the boron activation and clustering reaction. Using the boundary condition and initial condition, the diffusion equation is solved successfully. The simulator is made ofC language and reappear the experimental data successfully.

• Structural Engineering and Mechanics
• /
• 제91권1호
• /
• pp.39-47
• /
• 2024

Bridge deterioration shows the change of bridge condition during its operation, and predicting bridge deterioration is important for implementing predictive protection and planning future maintenance. However, in practical application, the raw inspection data of bridges are not continuous, which has a greater impact on the accuracy of the prediction results. Therefore, two kinds of bridge deterioration models are established in this paper: one is based on the traditional regression theory, combined with the distribution fitting theory to preprocess the data, which solves the problem of irregular distribution and incomplete quantity of raw data. Secondly, based on the theory of Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the network is trained using the raw inspection data, which can realize the prediction of the future deterioration of bridges through the historical data. And the inspection data of 60 prestressed concrete box girder bridges in Xiamen, China are used as an example for validation and comparative analysis, and the results show that both deterioration models can predict the deterioration of prestressed concrete box girder bridges. The regression model shows that the bridge deteriorates gradually, while the LSTM-RNN model shows that the bridge keeps great condition during the first 5 years and degrades rapidly from 5 years to 15 years. Based on the current inspection database, the LSTM-RNN model performs better than the regression model because it has smaller prediction error. With the continuous improvement of the database, the results of this study can be extended to other bridge types or other degradation factors can be introduced to improve the accuracy and usefulness of the deterioration model.

• 한국환경과학회지
• /
• 제23권12호
• /
• pp.2121-2127
• /
• 2014

Analyzing the observational data of volcanic activities around the northern part of Korean peninsula, the odds of volcano eruption increases continuously. For example, the cumulative seismic moment and frequence observed near Mt. Baekdu show a sudden increased values. In this study, predicting the diffusion of volcanic ash for two cases were carried out by using interactive realtime simulator, which was developed during last 2 years as a research and development project. The first case is Sakurajima volcano (VEI=3) erupted in August 2013. The second case is assumed as the volcanic eruption at Mt. Baekdu (VEI=7) under landing circumstance of typhoon Maemi (August 2003) in Korean peninsula. The synoptic condition and ash diffusion for the two cases were simulated by WRF(Weather Research and Forecast) model and Lagrangian dispersion model, respectively. Comparing the simulated result of the first case (i.e., Sakurajima volcano) with satellite image, the diffusion pattern show acceptable result. The interactive realtime simulator can be available to support decision making under volcanic disaster around East Asia by predicting several days of ash dispersion within several minutes with ordinary desktop personal computer.

• 대한기계학회논문집B
• /
• 제26권4호
• /
• pp.587-593
• /
• 2002

The present research proposes a pressure based approach along with Langmuir slip condition for predicting microscale fluid flows. Using this method, gaseous slip flows in 2 -dimensional microchannels are numerically investigated. Compared to the DSMC simulation, statistical errors could be avoided and computing time is much less than that of the aforementioned molecular approach. Maxwell slip boundary condition is also studied in this research. These two slip conditions give similar results except for the pressure nonlinearity at high Knudsen number regime. However, Langmuir slip condition seems to be more promising because this does not need to calculate the streamwise velocity gradient accurately and to calibrate the empirical accommodation coefficient. The simulation results show that the proposed method using Langmuir slip condition is an effective tool for predicting compressibility and rarefaction in microscale slip flows.

• Korean Journal of Hydrosciences
• /
• 제4권
• /
• pp.51-63
• /
• 1993

This study is to verify the applicability of statistical models in predicting flood frequency at the stage gaging stations of which the flow is under natural condition in the Han River basin. The results of the study show that the statistical flood frequency models were proven to be fairly reasonable to apply in practice, and also were compared with sampling variance to calibrate the statistical efficiency of the estimators of the T year floods Q(T) by two different flood frequency models. As a result, it was showed that for return periods greater than about T = 10 years the annual exceedance series estimators of Q(T) has smaller sampling variance than the annual maximum series estimators. It was showed that for the range of return periods the partial duration series estimators of !(T) has smaller sampling variance than the annual maximum series estimate only if the POT model contains at least 2N(N : record length) items or more in order to estimate Q(T) more efficiently than the ANNMAX model.

• Journal of Advanced Marine Engineering and Technology
• /
• 제34권7호
• /
• pp.971-980
• /
• 2010

In this paper, an artificial neural network (ANN) was employed to build a predicting model for refrigerator structure. The predicting model includes three input variables of the plaque depth (D), width (W) and interval distance(S) on the inner wall. Finite element method was utilized to obtain the data, which would be necessary for the ANN training process. Finally, a genetic algorithm (GA) was applied to find the optimal parameters that leaded to the minimum inner case deformation under operating condition. The optimal combination found is the depth(D) of 2.63mm, the width(W) of 19.24mm and the interval distance(S) of 49.38mm which leaded to the smallest deformation of 1.88mm for the given refrigerator model.