• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.123-131
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• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.

• Journal of the Korean Ceramic Society
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• v.44 no.3 s.298
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• pp.162-168
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• 2007

Silica wet gels were prepared ken water glass ($29\;wt%\;SiO_{2}$) by using Amberlite as a ion exchange resin. After washing in distilled water, the wet gels were further aged in a solution of TEOS/EtOH to strengthen of 3-dimensional network structure. As increase TEOS content in aging solution, BET surface area and porosity of the ambient dried silica aerogels were significantly decreased, and average pore diameter was also decreased 30 nm to -10 nm. Also, higher density and compressive strength were obtained in case of higher TEOS content. This is due to precipitation of $SiO_{2}$ nano particles by TEOS. Hence, TEOS addition plays an important role of both strengthening and stiffness of silica wet gel network. By adding over 30 vol% TEOS, a crack-free monolithic silica aerogel tiles were obtained and its density, compressive strength, and thermal conductivity were shown $0.232g/cm^{3}$, 7.3 MPa, and 0.029 W/mk, respectivly.

• Journal of Hydrogen and New Energy
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• v.26 no.3
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• pp.294-299
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• 2015

Free piston linear engine (FPLE) is a promising concept being explored in the mid-20th century. On the other hand, Arficial neural networks (ANNs) are non-linear computer algorithms and can model the behavior of complicated non-linear processes. Some researchers already studied this method to predict internal combustion engine characteristics. However, no investigation to predict the performance of a FPLE using ANN approach appears to have been published in the literature to date. In this study, the ability of an artificial neural network model, using a back propagation learning algorithm has been used to predict the in-cylinder pressure, frequency, maximum stroke length of a free piston linear engine. It is advised that, well-trained neural network models can provide fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.

• Convergence Security Journal
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• v.6 no.2
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• pp.71-80
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• 2006

Sensor network is made from very small and restrictive-power nodes, and they collect some information of environment like as thermal and tremor, etc. And they transfer the information to each other. Generally, supporting the Security service of sensor network is a difficult work, because the nodes have very small cpu-power and low electronic-power. So, More effective management scheme will needed for the maintenance of stability. In this paper, we propose the location based management scheme with circular coordinates. We were make the with the relative location information from one node to other. The new scheme show more simple and effective result then the other method for key management.

• Korea-Australia Rheology Journal
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• v.14 no.2
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• pp.49-55
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• 2002

To understand the large amplitude oscillatory shear (LAOS) behavior of complex fluids, we have investigated the flow behavior of a network model in the LAOS environment. We applied the LAOS flow to the model proposed by Vaccaro and Marrucci (2000), which was originally developed to describe the system of associating telechelic polymers. The model was found to predict at least three different types of LAOS behavior; strain thinning (G' and G" decreasing), strong strain overshoot (G' and G" increasing followed by decreasing), and weak strain overshoot (G' decreasing, G" increasing followed by decreasing). The overshoot behavior in the strain sweep test, which il often observed in some complex fluid systems with little explanation, could be explained in terms of the model parameters, or in terms of the overall balance between the creation and loss rates of the network junctions, which are continually created and destroyed due to thermal and flow energy. This model does not predict strain hardening behavior because of the finitely extensible nonlinear elastic (FENE) type nonlinear effect of loss rate. However, the model predicts the LAOS behavior of most of the complex fluids observed in the experiments.he experiments.

• Bulletin of the Korean Chemical Society
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• v.23 no.4
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• pp.575-579
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• 2002

Two-dimensionally cross-linked ultrathin films of poly(maleic acid-alt-methyl vinyl ether) (MA-MVE) and poly(allylamine) (PAA) were produced by using sodium dioctadecyl sulfate (2C18S) as the monolayer template for Langmuir-Blodgett (LB) depositio n. The template molecules were subsequently removed by thermal treatment followed by extraction. The polyion-complexed monolayers of three components, i.e., template 2C18S, co-spread PAA, and subphase MA-MVE, were formed at the air-water interface. Their monolayer properties were studied by the surface pressure-area isotherm. The monolayers were transferred on solid substrates as Y type. The polyion-complexed LB films and the resulting network films were characterized by FT-IR spectroscopy, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The cross-linking to form a polymer network was achieved by amide or imide formation through heat treatment under a vacuum. SEM observation of the film on a porous fluorocarbon membrane filter (pore diameter 0.1 ㎛) showed covering of the pores by four layers in the polyion complex state. Extraction by chloroform followed by heat treatment produced hole defects in the film.

• Current Optics and Photonics
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• v.6 no.5
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• pp.479-488
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• 2022

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• Journal of Energy Engineering
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• v.20 no.1
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• pp.1-7
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• 2011

The performance and life-cycle costs of electric vehicle(EV) and hybrid electric vehicle(HEV) depend inherently on battery packs. Temperature uniformity in a pack is an important factor for obtaining optimum performance for an EV or HEV battery pack, because uneven temperature distribution in a pack leads to electrically unbalanced battery cells and reduced pack performance. In this work, a three-dimensional modeling was carried out to investigate the effects of operating conditions on the thermal behavior of a lithium-ion battery pack for an EV or HEV application. Thermal conductivities of various compartments of the battery were estimated based on the equivalent network of parallel/series thermal resistances of battery components. Heat generation rate in a cell was calculated using the modeling results of the potential and current density distributions of a battery cell.