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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Jo May 25, 2025
Transversely excited atmospheric pressure (TEA) CO2 lasers have been widely developed and used due to their high peak power and short pulse width, and research into them is being further intensified. TEA CO2 lasers have been used in the photochemical fields such as light detection and ranging of atmospheric pollutants (LIDAR) and laser isotope separation. TEA CO2 lasers have also been used for laser particle acceleration, synthesis of nanoparticles, and rapid large paint stripping required in the aerospace industry.
In order to study the dynamics of TEA CO2 laser, multi-temperature models such as four-, five- and six-temperature models have been developed and different simulation methods have been introduced. Here, the six-temperature model is accepted as the most suitable model to describe the dynamics of TEA CO2 laser, taking into account all possible vibrational-rotational transitions of gas mixture. The six-temperature model considers the given gas mixture ratio, total pressure, laser cavity geometry, pumping mechanism and discharge gap geometry, etc. to determine simulation parameters including excitation rate and relaxation time, and to simulate TEA CO2 laser dynamics.
Although some preceding experimental results have reported that addition of a small amount of hydrogen to the gas mixture of TEA CO2 laser leads to higher power and efficiency, unfortunately, little has been investigated about the theoretical modeling and dynamics of hydrogen-doped TEA CO2 laser.
Pak Kwang Il, a researcher at the Faculty of Physical Engineering, has developed an improved six-temperature model, taking into account the effect of hydrogen on the vibrational levels of gas mixture. Through the simulation of the dynamic processes of the hydrogen-doped TEA CO2 lasers, he determined the optimized gas mixture ratio to increase the output power and the pulse energy.
You can find the details in his paper “Improved six-temperature model and simulation for dynamics of high-power TEA CO2 lasers considering effect of hydrogen” in “Optics and Laser Technology” (SCI).
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Jo May 23, 2025
Pulsed electric field (PEF) processing is a nonthermal treatment that uses high voltage electric pulses (HVEP) for many operations in food and bioengineering, including improvement of protein digestibility, potato processing, microalgae processing, exposure of biological cells, fruit processing, and meat preservation. The processing medium, placed between or passing through two electrodes, are affected by the HVEP of a few microseconds. There have been proposed several designs of PEF treatment chamber such as parallel, co-axial and co-linear configuration. Of all the configurations, the co-linear design is regarded as the most effective. Although this technology has already been introduced to industrial application such as food preservation, the detailed mechanisms of HVEP action on different processing media still remain imperfect.
To clarify the beneficial effect of HVEP and find the proper design of treatment chamber for different treatment objects, more reliable and optimized modeling on the electric field of PEF treatment chamber should be conducted because its measurement is difficult or impossible to perform. Many new data on the electric field of PEF treatment chamber have been presented but the simulation results are not identical with each other. The origin of different results in the literature is likely related to the difference in the simulation conditions, which might cause ambiguity in clarifying the mechanism of PEF treatment and designing the device.
Ham Kum Hae, a post-graduate student at the Faculty of Physical Engineering, has constructed a model to simulate the electric field in a PEF treatment chamber and verify the advantage of a new type of treatment chamber by lattice Boltzmann method.
First, she developed a lattice Boltzmann model (LBM) to describe the electric field distribution in co-linear PEF processing. Based on the assumption that PEF does not cause a time varying magnetic field, she carried out a simulation by using the charge conservation equation. For a two-dimensional LBM, she specified a macroscopic boundary condition for electric potential at high voltage and ground electrodes, and a bounce-back boundary condition for electric potential at the insulator. Next, she suggested a new type of treatment chamber with “holo-elliptical” geometry where the uniformity of electric field was remarkably improved.
For more details, you can refer to her paper “Lattice Boltzmann Simulation of Electric Field in Co-Linear Pulsed Electric Field (PEF) Treatment Chamber” in “Bulletin of the Lebedev Physics Institute” (SCI).
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Jo May 22, 2025
Magnetic resonance imaging (MRI) finds wide application in various studies and clinical practice related to the quantitative and intuitive assessment of cerebral nerve system because of its good contrast and high resolution for brain structures. Most of them require an image processing step called brain extraction by which only the brain part is segmented from cranial magnetic resonance (MR) images.
Though a number of brain extraction algorithms have been presented, brain extraction tool (BET) is still regarded as a favorable tool in the neuroimaging community, and most of the brain extraction algorithms proposed up to date have used the BET as an important competing method to compare their performance.
BET based on the deformable surface initializes the surface as a spherical mesh, and then evolves the surface toward the brain border with small movements applying iteratively a set of forces depending on local parameters to the vertices on the surface. Adopting the local parameters, in general, doesn’t guarantee the balanced evolution all over the surface to generate self-intersections because some vertices may move more quickly while others move slowly depending on the local conditions. This is why BET should have small movements for evolution. Because BET adopts local parameters and small movements, the evolution of deformable surface may not only require more iteration but also tend to easily fall into local optimum resulting in falsely negative regions. Though the computing efficiency of BET is acceptable for clinical applications at present, the computation time is still an important issue when taking the increasing resolution of MRI or large-scale studies into account.
Son Chang Il, a researcher at the Faculty of Biology and Medicine Engineering, has proposed a modified BET (BETWP) consisting of two steps of surface evolution for fast and accurate brain extraction.
He introduced a new fast model using a global parameter, the global mean inter-vertex distance of evolution surface. This fast model is adopted in the preprocessing step and then the original BET model completes the evolution of surface in the second step.
The experiments for evaluating the computation efficiency and segmentation quality have shown that the proposed scheme has a couple of advantages over BET. First, it can improve the evolution speed at least twice for brain extraction without any failure due to self-intersection. Second, it can significantly improve the segmentation quality on JC, TE and NE including the false negative ratio for both MRI modalities (T1-weighted image and DW image).
For more information, please refer to his paper “Fast BET Based on Pre-Processing Evolution Using Global Mean Inter-Vertex Distance of Deformable Surface for MRI Brain Extraction” in “International Journal of Image and Graphics” (SCI).
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