• Geomechanics and Engineering
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• v.27 no.5
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• pp.433-445
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• 2021

Artificial ground freezing (AGF) is a commonly used geotechnical support technique that can be applied in any soil type and has low environmental impact. Experimental and numerical investigations have been conducted to optimize AGF for application in diverse scenarios. Precise simulation of groundwater flow is crucial to improving the reliability these investigations' results. Previous experimental research has mostly considered horizontal seepage flow, which does not allow accurate calculation of the groundwater flow velocity due to spatial variation of the piezometric head. This study adopted vertical seepage flow-which can maintain a constant cross-sectional area-to eliminate the limitations of using horizontal seepage flow. The closure time is a measure of the time taken for an impermeable layer to begin to form, this being the time for a frozen soil-ice wall to start forming adjacent to the freeze pipes; this is of great importance to applied AGF. This study reports verification of the reliability of our experimental apparatus and measurement system using only water, because temperature data could be measured while freezing was observed visually. Subsequent experimental AFG tests with saturated sandy soil were also performed. From the experimental results, a method of estimating closure time is proposed using the inflection point in the thermal conductivity difference between pore water and pore ice. It is expected that this estimation method will be highly applicable in the field. A further parametric study assessed factors influencing the closure time using a two-dimensional coupled thermo-hydraulic numerical analysis model that can simulate the AGF of saturated sandy soil considering groundwater flow. It shows that the closure time is affected by factors such as hydraulic gradient, unfrozen permeability, particle thermal conductivity, and freezing temperature. Among these factors, changes in the unfrozen permeability and particle thermal conductivity have less effect on the formation of frozen soil-ice walls when the freezing temperature is sufficiently low.

• Polymer(Korea)
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• v.24 no.2
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• pp.268-275
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• 2000

We investigated the effect of polymer sublayer on volumetric resistivity and tensile strength of carbon fiber (CF)/polyethylene composite films fabricated under high intensity electric fields. The dependence of volumetric resistivity and tensile strength of the films on the polymer sublayer thickness or mass part exhibited complex behavior according to CF content and CF layer density in the films. As the thickness of polymer sublayer increases, two groups of processes at thermo-mechanical forming stage would take effects in the properties of the films. The first group comprises the increase of polymer layer thickness having reduced CF content compared with central or upper part of the film and insufficient wetting of CF resulting in the loosened structure near upper film side. The second group, on the other hand, is the improvement of mobility of molten sublayer leading to better distribution of CF throughout the film thickness and the formation of more compact structure. The different degree of contribution of these two competing processes at varied CF content and CF layer density could explain complex dependence of the film properties on the polymer sublayer. These results are important to optimize the electrical and mechanical properties of highly conductive polymer films, which can be used as electromagnetic interference shielding materials.

• Korean Journal of Microbiology
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• v.45 no.1
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• pp.32-40
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• 2009

To understand contribution of thermophilic microorganisms in decomposition of litter deposits on shore of lakes, we surveyed a lakeshore litter deposit for bacteria growing at $60^{\circ}C$. Ten thermophilic isolates were selected for in-depth characterization, based on their high capacity to degrade high molecular weight organic compounds. Based on phylogenetic analysis on their 16S rRNA gene sequences, all isolates were identified as Geobacillus. The optimal growth temperature and pH of the strains ranged $55{\sim}60^{\circ}C$ and 6.0${\sim}$8.0, respectively. Salinity was inhibitory to the growth of the isolates, showing marked decrease of growth rates at 3% salinity. Based on activities of hydrolytic enzymes and profiles of carbohydrate utilization (determined by API 50 CHB kit), three G. stearothermophilus strains showed patterns clearly distinctive from other isolates. Two G. kaustophilus strains also demonstrated distinctiveness in their metabolic pattern and ecological parameters. However, ecological and metabolic profiles of the other five isolates were more variable and showed some degree of digression from their phylogenetic classification. Therefore, it could be concluded that endospore-forming thermophilic bacteria in lakeshore litter deposits contribute to degradation of organic materials with diverse ecological niches while having successions similar to microbial flora in compost. We propose that the thermophilic isolates and/or their thermo-tolerant enzymes can be applied to industrial processes as appropriate mixtures.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.1
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• pp.122-129
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• 1997

Thermo-elastic-plastic analyses used in solving plate forming process are often computationally expensive. To obtain an optimal process of line heating typically requires numerous iterations between the simulation and a finite element analysis. This process often becomes prohibitive due to the amount of computer time required for numerical simulation of line heating process. Therefore, a new techniques that could significantly reduce the computer time required to solve a complex analysis problem would be beneficial. In this paper, we considered factors that influence the bending effect by line heating and developed inference engine by using the concept of artificial neural network. To verify the validity of the neural network, we used results obtained from numerical analysis. We trained the neural network with the data made from numerical analysis and experiments varying the structure of neural network, in other words varying the number of hidden layers and the number of neurons in each hidden layers. From that we concluded that if the number of neurons in each hidden layers is large enough neural network having two hidden layers can be trained easily and errors between exact value and results obtained from trained network are not so large. Consequently, if there are enough number of training pairs, artificial neural network can infer similar results. Based on the numerical results, we applied the artificial neural network technique to deal with mechanical behavior of line heating at simulation stage effectively.