Jo May 30, 2025
The time-dependent Ginzburg-Landau (TDGL) equations are a useful computational tool for characterizing the spatiotemporal variance of an order parameter that represents the phase transition in mesoscopic type-II superconductors. Especially, they are used to simulate the dynamics of magnetic vortex for designing superconducting materials with high critical current density.
Therefore, simulation of type-II superconductors using TDGL equations is essential for understanding magnetic vortex dynamics and studying superconducting critical characters such as critical current density and critical magnetic field. Numerical simulations for it are conducted by the finite difference method (FDM), the finite element method (FEM), the lattice Boltzmann method (LBM), etc.
Ryu Yu Gwang, a researcher at the Faculty of Physical Engineering, has investigated the influences of surface defects on the motion of magnetic vortices in a mesoscopic type-II superconductor with randomly distributed pinning centers through the simulations of TDGL equations by using COMSOL Multiphysics.
Two kinds of surface defects are located in the boundary: one is pinning centers and the other is geometric defects along with pinning centers.
In the simulation, he analyzed the magnetization curves, the vorticity and the density of superconducting electrons for both different contents of pinning centers and various geometric defects.
For the pinning centers as surface defects, the maximum magnetization values as a function of the contents exponentially decrease, and the field where the first vortex penetrates and the field where the complete transition from superconducting to a normal state in the system occurs are reduced.
For the geometric defects as surface defects, the density of superconducting electrons and the magnetization curves depend on their size and form. In addition, a threshold in the size of geometric defects in which the motion of vortices and vorticity change, exists.
You can find the details in his paper “The Influence of Surface Defects on Motion of Magnetic Vortices in Mesoscopic Type-II Superconductor with Randomly Distributed Pinning Centers” in “Journal of Superconductivity and Novel Magnetism” (SCI).
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Jo May 29, 2025
Stroke remains a tremendous public health burden with approximately 795 000 people affected every year. Stroke is the leading cause of major long-term disability of adults and the third leading cause of death in developed countries. Urinary tract infection is a frequent problem after stroke.
Although prior scoring systems for UTI after stroke have been developed, Pak In Hui, a researcher at the Faculty of Biology and Medicine Engineering, has developed a simple scoring system of our own for all types of stroke.
The study was designed on retrospective data. The population included 1 496 patients with stroke who were admitted at the Neurology Department of Hospital of Pyongyang University of Medical Sciences between January, 2010 and August, 2019. The patients were diagnosed by means of CT and MRI.
You can find the details in her paper “The Development of Simple Scoring System to Predict Urinary Tract Infection (UTI) in Patients with Stroke” in “International Journal of Endocrinology” (SCI).
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Jo May 28, 2025
It is well known that accurately modeling the radiative heat transfer process at high temperature of several thousand to tens of thousands of K is a main factor in evaluating the efficiency of rocket propellants, pulverized coal combustion and thermal plasma. In particular, as radiative transfer equation (RTE) mathematically explaining heat radiation in the absorbing, emitting and scattering media has a calculus characteristic, its solution exists only in extremely limited geometries and conditions.
To analyze the radiative heat transfer in high temperature systems such as plasma, it is essential to determine the temperature distribution of the plasma, which requires the distribution of radiant intensity to be determined. Therefore, computational models for analyzing the temperature distribution of system and the radiant intensity distribution are required, and it is important to establish a methodology for combining these two computational models and to apply them to practice to improve accuracy.
Pak In Ae, a researcher at the Faculty of Physical Engineering, has proposed a new Discrete Ordinate-Lattice Boltzmann Method (DO-LBM) by combining DOM and LBM to analyze radiative heat transfer in a two-dimensional irregular enclosure that involves absorbing, emitting and scattering media.
Through the comparison with other methods, she has confirmed that the DO-LBM is more simple and accurate and can reduce computational cost of simulating radiative heat transfer in a complex boundary structure.
For more information, please refer to her paper “Discrete-Ordinate-Lattice-Boltzmann Method for analyzing radiative heat transfer: Application to two-dimensional irregular enclosure” in “Mathematics and Computers in Simulation” (SCI).
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Jo May 27, 2025
PM 2.5 has been identified as a major pollutant which is harmful to human health and causes destruction of ecosystems, and many investigations and studies have illustrated that air pollutants containing PM 2.5 cause severe diseases such as respiratory and cardiovascular diseases.
Since the correlation between different air pollutants and their own inherent characteristics is complicated, there have been many attempts to improve the forecasting accuracy by using deep neural network (DNN) for air quality forecasting. The results of these studies demonstrate that deep learning combined with spatiotemporal correlation analysis is of great significance in improving the performance of a model.
Pak Un Jin, a researcher at the Faculty of Automation Engineering, has proposed a new PM predictor to predict the daily average PM 2.5 concentration of the next day in Beijing City with regard to the seasonal pattern of air pollution.
He has demonstrated that the performance of the proposed PM predictor is excellent in comparison with MLP and LSTM models, and found clear evidences that the PM predictor is appropriate for overall forecasting and LSTM is more suitable than other models for seasonal forecasting.
If more information is needed, you can refer to his paper “Novel particulate matter (PM2.5) forecasting method based on deep learning with suitable spatiotemporal correlation analysis” in “Journal of Atmospheric and Solar-Terrestrial Physics” (SCI).
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Jo May 26, 2025
Plastic injection molding is one of the most important technologies for manufacturing plastic products. The quality of products made by the plastic injection molding depends on materials, molds, injection molding machines and process parameters. Materials, molds, and injection molding machines are selected at the first stage of product development. Therefore, it is one of the most important issues to determine the optimal injection process parameters to improve the quality of products.
Various methods have been applied to optimize the process parameters. Trial and error method demands massive experiments and a huge amount of labor, time and cost. To overcome these drawbacks and determine optimal process parameters, various optimization techniques such as Taguchi method, genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) methods have been widely used in various works. Among them, Taguchi method has been applied most widely to solving many practical engineering optimization problems because of its simplicity and effectiveness.
Kim Ju Song, a researcher at the Faculty of Materials Science and Technology, has proposed a method to determine optimal process parameter values using Taguchi method and TOPSIS in plastic injection molding, and applied it to the determination of optimal process parameters such as melt temperature, packing pressure, cooling time and injection pressure in order to optimize the mechanical properties such as tensile strength, elasticity module, flexural modulus and impact strength with ABS compound as plastic materials and AISI 1020 as mold materials.
The proposed method can determine optimal values and effect ranking of the process parameters for simultaneously improving the multiple mechanical properties of plastic injection moldings.
For more information, please refer to his paper “Determining method of optimal process parameters using Taguchi method and TOPSIS in plastic injection molding” in “Journal of Reinforced Plastics and Composites” (SCI).
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