• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.39-45
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• 2011

This study presents the thermal behaviors of glass beads-rubber mixtures depending on the volumetric fraction of each constituent and relative size between them. The transient plane source method is used to measure the effective thermal conductivity of mixtures. The discrete element method (DEM) and the thermal network model are integrated to investigate the particle-scale mechanism of heat transfer in granular packings. Results show that 1) the effective thermal conductivity decreases as the rubber fraction increases, and 2) the relative size between two solid particles dominates the spatial configuration of inter-particle contact condition that in tum determines the majority of heat propagation path through particle contacts. For the mixtures whose volumetric fraction of rubber is identical, the less conductive materials (e.g., rubber particles) with a large size facilitate heat transfer in granular materials. The experimental results and particle-scale observation highlight that the thermal conduction behavior is dominated not only by the volumetric fraction but also the spatial configuration of each constituent.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2343-2351
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• 2024

Accurately predicting the thermal hydraulic parameters of a transient reactor core under different working conditions is the first step toward reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which have often been modeled as time series prediction problems. This study aims to achieve accurate and continuous prediction of core thermal hydraulic parameters under instantaneous conditions, as well as test the feasibility of a newly constructed gated recurrent unit (GRU) model based on the soft attention mechanism for core parameter predictions. Herein, the China Experimental Fast Reactor (CEFR) is used as the research object, and CEFR 1/2 core was taken as subject to carry out continuous predictive analysis of thermal parameters under transient conditions., while the subchannel analysis code named SUBCHANFLOW is used to generate the time series of core thermal-hydraulic parameters. The GRU model is used to predict the mass flow and temperature time series of the core. The results show that compared to the adaptive radial basis function neural network, the GRU network model produces better prediction results. The average relative error for temperature is less than 0.5 % when the step size is 3, and the prediction effect is better within 15 s. The average relative error of mass flow rate is less than 5 % when the step size is 10, and the prediction effect is better in the subsequent 12 s. The GRU model not only shows a higher prediction accuracy, but also captures the trends of the dynamic time series, which is useful for maintaining reactor safety and preventing nuclear power plant accidents. Furthermore, it can provide long-term continuous predictions under transient reactor conditions, which is useful for engineering applications and improving reactor safety.

• Journal of the Korean Geosynthetics Society
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• v.6 no.1
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• pp.39-43
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• 2007

Geocomposites of needle punched and spunbonded nonwovens having the reinforcement and drainage functions were manufactured by use of thermal bonding method. The physical properties (e.g. tensile, tear and bursting strength, permittivity) of these multi-layered nonwovens were dependent on the processing parameters of temperatures, pressures, bonding periods etc. - in manufacturing by use of thermal bonding method. Therefore, it is very meaningful to optimize the processing parameters and physical properties of the geocomposites by thermal bonding method. In this study, an algorithm has been developed to optimize the process of the geocomposites using an artificial neural network (ANN). Geocomposites were employed to examine the effects of manufacturing methods on the analysis results and the neural network simulations have been applied to predict the changes of the nonwovens performances by varying the processing parameters.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.91-94
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• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• ETRI Journal
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• v.42 no.1
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• pp.118-128
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• 2020

A semitransparent photovoltaic-thermal (PV/T) air collector can produce electricity and heat simultaneously. To maximize the thermal and overall efficiency of the semitransparent PV/T air collector, its availability should be maximum; this can be determined through a Markov analysis. In this paper, a Markov model is developed to select an optimized number of semitransparent PV modules in service with five states and two states by considering two parameters, namely failure rate (λ) and repair rate (μ). Three artificial neural network (ANN) models are developed to obtain the minimum cost, minimum temperature, and maximum thermal efficiency of the semitransparent PV/T air collector by setting its type appropriately and optimizing the number of photovoltaic modules and cost. An attempt is also made to achieve maximum thermal and overall efficiency for the semitransparent PV/T air collector by using ANN after obtaining its minimum temperature and available solar radiation.

• Journal of computational fluids engineering
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• v.8 no.4
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• pp.41-49
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• 2003

This study presents a numerical model to analyze dynamic thermal behavior of a hot chuck designed for flip-chip bonders. The hot chuck of concern is a heater which has been specifically developed for accomplishing high-speed and ultra-precision soldering. The characteristic features are radiative heat source and the heating tool made of a material of high thermal diffusivity. A physical modeling has been conducted for the network of heat transport. A simplified finite volume model is deviced to simulate time-dependent thermal behavior of the heating tool on which soldering is achieved. The reliability of the proposed numerical model is verified experimentally. A series of numerical tests illustrate the usefulness of the numerical model in design analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.555-566
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• 2012

This paper presents a thermal analysis of an interior PM synchronous machine with concentrated winding for electric vehicle. The conventional thermal equivalent network model has been used for a long time for calculation of the temperature rises in electrical machines. In spite of being popular, this method can not be applied correctly for elements with complicated cooling structure like liquid cooled housing. To overcome this drawbacks, in this paper, a hybrid thermal model using the result of CFD analysis partly. Using this method, to improve a thermal performance of PMSM with concentrated winding, the effects of two design parameters are analysed. Finally, the accuracy of this model has been verified by experiments for the developed 21kW motor.

• Journal of Mechanical Science and Technology
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• v.19 no.10
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• pp.1932-1938
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• 2005

Graphite foams consist of a network of interconnected graphite ligaments and are beginning to be applied to thermal management of electronics. The thermal conductivity of the bulk graphite foam is similar to aluminum, but graphite foam has one-fifth the density of aluminum. This combination of high thermal conductivity and low density results in a specific thermal conductivity about five times higher than that of aluminum, allowing heat to rapidly propagate into the foam. This heat is spread out over the very large surface area within the foam, enabling large amounts of energy to be transferred with relatively low temperature difference. For the purpose of graphite foam thermosyphon design in electronics cooling, various effects such as graphite foam geometry, sub-cooling, working fluid effect, and liquid level were investigated in this study. The best thermal performance was achieved with the large graphite foam, working fluid with the lowest boiling point, a liquid level with the exact height of the graphite foam, and at the lowest sub-cooling temperature.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.84-86
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• 2001

This paper deals with the characteristic and thermal analysis of brushless motors for Rack assist type Electric Power Steering(REPS). The performance of permanent magnet is under the influence of temperature. To predict the motor performance, the thermal analysis is necessary. The equivalent thermal network is composed of the thermal resistance and the temperature of major parts is calculated according to the operating condition.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2106-2109
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• 2008

Nanofluids have been studied as possible alternatives for heat transfer fluids to improve the efficiency of heat exchangers. There are deviations of measured effective thermal conductivities between research-groups, and the mechanisms of the effective thermal conductivity enhancement of nanofluids are not confirmed yet. In this study, the effects of particle shape on the effective thermal conductivity enhancement are discussed and presented as a possible explanation of the deviations. The particle motion effect is found to be negligible for nanofluids of high aspect ratio cylindrical particles, which is believed to be important for nanofluids of spherical particles, while the percolation network formation and contact resistance play dominant roles in determining the effective thermal conductivity.