• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.39-42
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• 1996

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feedforward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.534-539
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• 1997

A CHF table method has been assessed in this study for rod bundle CHF predictions. At the conceptual design stage for a new reactor, a general critical heat flux (CHF) prediction method with a wide applicable range and reasonable accuracy is essential to the thermal-hydraulic design and safety analysis. In many aspects, a CHF table method (i.e., the use of a round tube CHF table with appropriate bundle correction factors) can be a promising way to fulfill this need. So the assessment of the CHF table method has been performed with the bundle CHF data relevant to pressurized water reactors (PWRs). For comparison purposes, W-3R and EPRI-1 were also applied to the same data base. Data analysis has been conducted with the subchannel code COBRA-IV-I. The CHF table method shows the best predictions based on the direct substitution method. Improvements of the bundle correction factors, especially for the spacer grid and cold wall effects, are desirable for better predictions. Though the present assessment is somewhat limited in both fuel geometries and operating conditions, the CHF table method clearly shows potential to be a general CHF predictor.

• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Nuclear Engineering and Technology
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• v.19 no.4
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• pp.233-241
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• 1987

Difficulties are encountered when the behavior of complex systems (i.e., fuel failure probability) that have unreliable deterministic models is predicted. For more realistic prediction of the behavior of complex systems with limited observational data, the present study was undertaken to devise an approach of combining predictions from the deterministic model and actual observational data. Predictions by this method of combining are inferred to be of higher reliability than separate predictions made by either model taken independently. A systematic method of hierarchical pattern discovery based on the method developed in the SPEAR was used for systematic search of weighting factors and pattern boundaries for the present method. A sample calculation was performed for prediction of CANDU fuel failures that had occurred due to power ramp during refuelling process. It was demonstrated by this sample calculation that there exists a region of feature space in which fuel failure probability from the PROFIT model nearly agree with that from observational data.

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.289-302
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• 2017

The key risk analysis technologies for the re-entry of space objects into Earth's atmosphere are divided into four categories: cataloguing and databases of the re-entry of space objects, lifetime and re-entry trajectory predictions, break-up models after re-entry and multiple debris distribution predictions, and ground impact probability models. In this study, we focused on reentry prediction, including orbital lifetime assessments, for space situational awareness systems. Re-entry predictions are very difficult and are affected by various sources of uncertainty. In particular, during uncontrolled re-entry, large spacecraft may break into several pieces of debris, and the surviving fragments can be a significant hazard for persons and properties on the ground. In recent years, specific methods and procedures have been developed to provide clear information for predicting and analyzing the re-entry of space objects and for ground-risk assessments. Representative tools include object reentry survival analysis tool (ORSAT) and debris assessment software (DAS) developed by National Aeronautics and Space Administration (NASA), spacecraft atmospheric re-entry and aerothermal break-up (SCARAB) and debris risk assessment and mitigation analysis (DRAMA) developed by European Space Agency (ESA), and semi-analytic tool for end of life analysis (STELA) developed by Centre National d'Etudes Spatiales (CNES). In this study, various surveys of existing re-entry space objects are reviewed, and an efficient re-entry prediction technique is suggested based on STELA, the life-cycle analysis tool for satellites, and DRAMA, a re-entry analysis tool. To verify the proposed method, the re-entry of the Tiangong-1 Space Lab, which is expected to re-enter Earth's atmosphere shortly, was simulated. Eventually, these results will provide a basis for space situational awareness risk analyses of the re-entry of space objects.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.456-461
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• 1995

The concept of hyperbolicity breaking is applied to predict the flow regime transition from inverted annular flow (IAF) to agitated inverted annular flow (AIAF). The resultant correlation has the similar form to Takenaka's empirical one. To validate the proposed model, it is applied to predict Takenaka's experimental results using R-113 refrigerant with four different tube diameters of 3, 5, 7 and 10 mm. The proposed model gives accurate predictions for the tube diameters of 7 and 10 min. As the tube diameter decreases, the differences between the predictions and the experimental results increase slightly. The flow regime transition from AIAF to dispersed flow (DF) is described by the drift flux model.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.3 s.18
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• pp.165-173
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• 2004

Matching inviscid and boundary layer methods are developed for analysis of hypersonic flow with thick boundary layer. The new equations match all the boundary layer properties with a variation in the inviscid solution near the edge, except for the normal velocity. Computational comparison are peformed for incompressible and compressible flows over a flat plate. Results from the present method are compared with Wavier-Stokes solutions. The present results are in good agreement with Wavier-Stokes solutions. They show that the new technique can provide improved predictions of heating rates and skin friction predictions for preliminary design of vehicles where shear layers and entropy layer swallowing are importantfor for preliminary design.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.368-373
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• 1997

The transition from a statified wavy to a slug flow has been analyzed considering the mechanistic forces acting on the wave crest in a nearly horizontal air-water countercurrent flow. To verify the results of the analysis, a series of experiments have been performed changing the inclination angle of the test section. Comparisons of the theoretical predictions with experimental data show a good agreement and the results show that the present model gives similar results of Taitel and Dukler's in the case of inclined pipes. However, at high superficial liquid velocity, the results of present work agree more closely with data than that of Taitel and Dukler's. Also, predictions of the present model gives a very close agreement with the experimental data for various tube sizes obtained by others.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.559-564
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• 1998

The film condensation models in RELAP5/MOD3.1 and RELAP5/WOD3.2 are assessed with the data experiment performed in the scaled down condensation experimental facility with a single vertical tube inner diameter 46 mm in the range of pressure 0.1∼7.5 Mpa for the PSCS(Passive Secondary Condenser System) Both MOD3.1 and MOD3.2 don't shows any reliable predictions the experimental data The RELAP5/MOD3.1 overpredicts the heat transfer coefficients experiment, whereas the RELAP5/MOD3.2 underpredicts those data it is recommended that the film condonation model in RELAP5/MOD3.2 should be modified to hue a larger heat transfer coefficient than those the present model to give the reliable predictions.