• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.814-822
• /
• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Nuclear Engineering and Technology
• /
• v.55 no.6
• /
• pp.2298-2304
• /
• 2023

Titanium alloys are expected to become one of the candidate materials for nuclear-powered spacecraft due to their excellent overall performance. Nevertheless, atomistic mechanisms of the defect accumulation and evolution of the materials due to long-term exposure to irradiation remain scarcely understood by far. Here we investigate the heavy irradiation damage in a-titanium with a dose as high as 4.0 canonical displacements per atom (cDPA) using atomistic simulations of Frenkel pair accumulation. Results show that the content of surviving defects increases sharply before 0.04 cDPA and then decreases slowly to stabilize, exhibiting a strong correlation with the system energy. Under the current simulation conditions, the defect clustering fraction may be not directly dependent on the irradiation dose. Compared to vacancies, interstitials are more likely to form clusters, which may further cause the formation of 1/3<1210> interstitial-type dislocation loops extended along the (1010) plane. This study provides an important insight into the understanding of the irradiation damage behaviors for titanium.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.167-167
• /
• 2020

Annual sediment deposition in reservoirs behind weirs poses flood risk, while its accurate prediction remains a challenge. Sediment management by hydraulic flushing is an effective method to maintain reservoir storage. In this study, an integrated approach to predict sediment inflow and sediment flushing simulation in reservoirs is presented. The annual sediment inflow prediction was carried out with Artificial Neural Networks (ANN) modelling. RESCON model was applied for quantification of sediment flushing feasibility criteria. The integrated approach was applied on Sangju Weir and also on estuary of Nakdong River (NREB). The mean annual sediment inflow predicted at Sangju Weir and NREB was 400,000 ㎥ and 170,000 ㎥, respectively. The sediment characteristics gathered were used to setup RESCON model and sediment balance ratio (SBR) and long term capacity ratio (LTCR) were used as flushing efficiency indicators. For Sangju Weir, the flushing discharge, Q_f = 140 ㎥/s with a drawdown of 5 m, and flushing duration, T_f = 10 days was necessary for efficient flushing. At NREB site, the parameters for efficient flushing were Q_f = 80 ㎥/s, T_f = 5 days, N = 1, El_f = 2.24 m. The hydraulic flushing was concluded feasible for sediment management at both Sangju Weir and NREB.

• Journal of Information Processing Systems
• /
• v.19 no.5
• /
• pp.688-701
• /
• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• Nuclear Engineering and Technology
• /
• v.55 no.10
• /
• pp.3898-3906
• /
• 2023

The paper notes that the most common way of handling spent nuclear fuel (SNF) of power reactors is its temporary long-term dry storage. At the same time, the operation of the dry spent fuel storage facilities almost never use the modern capabilities of information systems in safety control and collecting information for the next studies under implementation of aging management programs. The author proposes a structure of an information system that can be implemented in a dry spent fuel storage facility with ventilated storage containers. To control the thermal component of spent fuel storage safety, a database structure has been developed, which contains 5 tables. An algorithm for monitoring the thermal state of spent fuel was created for the proposed information system, which is based on the comparison of measured and forecast values of the safety criterion, in which the level of heating the ventilation air temperature was chosen. Predictive values of the safety criterion are obtained on the basis of previously published studies. The proposed algorithm is an implementation of the information function of the system. The proposed information system can be used for effective thermal monitoring and collecting information for the next studies under the implementation of aging management programs for spent fuel storage equipment, permanent control of spent fuel storage safety, staff training, etc.

• Nuclear Engineering and Technology
• /
• v.55 no.12
• /
• pp.4628-4636
• /
• 2023

Cables are indispensable in nuclear power plants for transmitting data measured by various types of detectors, such as self-powered neutron detectors (SPNDs). These cables will generate disturbing signals that must be accurately distinguished and eliminated. Given that the cable current is not very significant, previous research has focused on SPND, with little attention paid to cable evaluation and validation. This paper specifically focuses on the quantitative analysis of cables and proposes a theoretical model to predict cable noise. In this model, the reaction characteristics between irradiated neutrons and cables were discussed thoroughly. Based on the Monte Carlo method, a comprehensive simulation approach of neutron sensitivity was introduced and long-term irradiation experiments in a heavy water reactor (HWR) were designed to verify this model. The theoretical results of this method agree quite well with the experimental measurements, proving that the model is reliable and exhibits excellent accuracy. The experimental data also show that the cable current accounts for approximately 0.2% of the total current at the initial moment, but as the detector gradually depletes, it will contribute more than 2%, making it a non-negligible proportion of the total signal current.

• Nuclear Engineering and Technology
• /
• v.56 no.6
• /
• pp.2266-2273
• /
• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3942-3949
• /
• 2024

This study focuses on investigating the chemical degradation characteristics of cementitious barriers used in low-and intermediate-level radioactive waste repository by reactive transport modeling. The impact of the blending with supplementary cementitious materials (SCMs) in the barriers on the chemical degradation was evaluated to find the optimum barrier design. A number of different barrier designs were examined by replacing ordinary Portland cement (OPC) by SCMs (i.e., fly ash, silica fume, and blast-furnace slag). The simulation results showed that silica fume blended barrier has better durability against chemical degradation by rainwater compared to fly ash or blast-furnace slag blended barriers. In addition, the chemical durability of silica fume-based barrier increased with increasing replacement level up to about 20 %. It seems that the amount of formed calcium silicate hydrate (CSH) in the initial cement-based barrier highly affects the overall chemical durability. The newly developed reactive transport model demonstrated its capability for understanding the barrier performance and investigating the optimal design of the barrier system.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2016.02a
• /
• pp.147.2-148
• /
• 2016

Light management technology is very important for thin film solar cells, which can reduce optical reflection from the surface of thin film solar cells or enhance optical path, increasing the absorption of the incident solar light. Using proper light trapping structures in hydrogenated amorphous silicon (a-Si:H) solar cells, the thickness of absorber layers can be reduced. Instead, the internal electric field in the absorber can be strengthened, which helps to collect photon generated carriers very effectively and to reduce light-induced loss under long-term light exposure. In this work, we introduced a chemical etching technology to make honey-comb textures on glass substrates and analyzed the optical properties for the textured surface such as transmission, reflection and scattering effects. Using ray optics and finite difference time domain method (FDTD) we represented the behaviors of light waves near the etched surfaces of the glass substrates and discussed to obtain haze parameters for the different honey-comb structures. The simulation results showed that high haze values were maintained up to the long wavelength range over 700 nm, and with the proper design of the honey-comb structure, reflection or transmission of the glass substrates can be enhanced, which will be very useful for the multi-junction (tandem or triple junction) thin film a-Si:H solar cells.

• Journal of Astronomy and Space Sciences
• /
• v.12 no.1
• /
• pp.78-89
• /
• 1995

We solved n-body problem about 9 planets, moon, and 4 minor planets with relativistic effect related to the basic equation of motion of the solar system. Perturbations including flgure potential of the earth and the moon and solid earth tidal effect were considered on this relativistic equation of motion. The orientations employed precession and nutation for the earth, and lunar libration model with Eckert's lunar libration model based on J2000.0 were used for the moon. Finally, we developed heliocentric ecliptic position and velocity of each planet using this software package named the SSEG (Solar System Ephemerides Generator) by long-term (more than 100 years) simulation on CRAY-2S super computer, through testing each subroutine on personal computer and short-time(within 800 dyas) running on SUN3/280 workstation. Epoch of input data JD2440400.5 were adopted in order to compare our results to the data archived from JPL's DE 200 by Standish and Newhall. Above equation of motion was integrated numerically having 1-day step-size interval through 40,000 days (about 110 years long) as total computing interval. We obtained high-precision ephemerides of the planets with maximum error, less $than\pm2\times10^{-8}AU(\approx\pm3km)$ compared with DE200 data (except for mars and moon).