• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4134-4145
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• 2022

The collimator is one of the high-heat-flux components used to avoid a series of vacuum and thermal problems. In this paper, the heat load distribution throughout the collimator is first calculated through experimental data, and a transient thermodynamic simulation analysis of the original model is carried out. The error of the pipe outlet temperature between the simulated and experimental values is 1.632%, indicating that the simulation result is reliable. Second, the model is optimized to improve the heat transfer performance of the collimator, including the contact mode between the pipe and the flange, the pipe material and the addition of a twisted tape in the pipe. It is concluded that the convective heat transfer coefficient of the optimized model is increased by 15.381% and the maximum wall temperature is reduced by 16.415%; thus, the heat transfer capacity of the optimized model is effectively improved. Third, to adapt the long-pulse steady-state operation of the experimental advanced superconducting Tokamak (EAST) in the future, steady-state simulations of the original and optimized collimators are carried out. The results show that the maximum temperature of the optimized model is reduced by 37.864% compared with that of the original model. The optimized model was changed as little as possible to obtain a better heat exchange structure on the premise of ensuring the consumption of the same mass flow rate of water so that the collimator can adapt to operational environments with higher heat fluxes and long pulses in the future. These research methods also provide a reference for the future design of components under high-energy and long-pulse operational conditions.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.106-116
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• 2023

In this paper, we designed a coaxial dual camera incorporating two optical systems-one for the visible rays and the other for far-infrared ones-with the aim of capturing images in both wavelength ranges. The far-infrared system, which uses an uncooled detector, has a sensor array of 640×480 pixels. The visible ray system has 1,945×1,097 pixels. The coaxial dual optical system was designed using a hot mirror beam splitter to minimize heat transfer caused by infrared rays in the visible ray optical system. The optimization process revealed that the final version of the dual camera system reached more than 90% of the fusion performance between two separate images from dual systems. Multiple rigorous testing processes confirmed that the coaxial dual camera we designed demonstrates meaningful design efficiency and improved image conformity degree compared to existing dual cameras.

• Journal of Powder Materials
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• v.30 no.2
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• pp.146-155
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• 2023

The Ti-6Al-4V lattice structure is widely used in the aerospace industry owing to its high specific strength, specific stiffness, and energy absorption. The quality, performance, and surface roughness of the additively manufactured parts are significantly dependent on various process parameters. Therefore, it is important to study process parameter optimization for relative density and surface roughness control. Here, the part density and surface roughness are examined according to the hatching space, laser power, and scan rotation during laser-powder bed fusion (LPBF), and the optimal process parameters for LPBF are investigated. It has high density and low surface roughness in the specific process parameter ranges of hatching space (0.06-0.12 mm), laser power (225-325 W), and scan rotation (15°). In addition, to investigate the compressive behavior of the lattice structure, a finite element analysis is performed based on the homogenization method. Finite element analysis using the homogenization method indicates that the number of elements decreases from 437,710 to 27 and the analysis time decreases from 3,360 to 9 s. In addition, to verify the reliability of this method, stress-strain data from the compression test and analysis are compared.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.3
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• pp.226-232
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• 2023

As a potential alternative to flash memory, HfO₂/Al₂O₃ stacks appear to be a viable option as charge capture layers in charge trapping memories. The paper undertakes a review of HfO₂/Al₂O₃ stacks as charge trapping layers, with a focus on comparing the number, thickness, and post-deposition heat treatment and γ-ray and white x-ray treatment of such stacks. Compared to a single HfO₂ layer, the memory window of the 5-layered stack increased by 152.4% after O₂ annealing at ±12 V. The memory window enlarged with the increase in number of layers in the stack and the increase in the Al/Hf content in the stack. Furthermore, our comparison of the treatment of HfO₂/Al₂O₃ stacks with varying annealing temperatures revealed that an increased annealing temperature resulted in a wider storage window. The samples treated with O₂ and subjected to various γ radiation intensities displayed superior resistance. and the memory window increased to 12.6 V at ±16 V for 100 kGy radiation intensity compared to the untreated samples. It has also been established that increasing doses of white x-rays induced a greater number of deep defects. The optimization of stacking layers along with post-deposition treatment condition can play significant role in extending the memory window.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.279-290
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• 2022

A superconducting fault current limiter(SFCL) is a power device that exploits superconducting transition to control currents and enhances the flexibility, stability and reliability of the power system within a few milliseconds. With a high phase transition speed, high critical current densities and little AC loss, high-temperature superconducting (HTS) wires are suitable for a resistive-type SFCL. However, HTS wires due to the lack of optimization research are rather inefficient to directly apply to a fault current limiter in terms of the design and capacity, for the existing method relied the characteristics. Therefore, in order to develop a suitable wire for an SFCL, it is necessary to enhance critical current uniformity, select optimal stabilizer materials and conducted research on the development of uniform stabilizer layering technology. The high temperature superconducting wires manufactured by this study get an average critical current of 804 A/12mm-width at the length of 710m; therefore, conducted research was able to secure economic performance by improving efficiency, reducing costs, and reducing size.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.202-211
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• 2023

The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard's production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2628-2641
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• 2023

Nuclear safety is the lifeline for the development and application of nuclear energy. In severe accidents of pressurized water reactor (PWR), aerosols, as the main carrier of fission products, are suspended in the containment vessel, posing a potential threat of radioactive contamination caused by leakage into the environment. The gas-phase aerosols suspended in the containment will settle onto the wall or sump water through the natural deposition mechanism, thereby reducing atmospheric radioactivity. Aiming at the low accuracy of the aerosol model in the ISAA code, this paper improves the natural deposition model of aerosol in the containment. The aerosol dynamic shape factor was introduced to correct the natural deposition rate of non-spherical aerosols. Moreover, the gravity, Brownian diffusion, thermophoresis and diffusiophoresis deposition models were improved. In addition, ABCOVE, AHMED and LACE experiments were selected to validate and evaluate the improved ISAA code. According to the calculation results, the improved model can more accurately simulate the peak aerosol mass and respond to the influence of the containment pressure and temperature on the natural deposition rate of aerosols. At the same time, it can significantly improve the calculation accuracy of the residual mass of aerosols in the containment. The performance of improved ISAA can meet the requirements for analyzing the natural deposition behavior of aerosol in containment of advanced PWRs in severe accident. In the future, further optimization will be made to address the problems found in the current aerosol model.