• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Structural Engineering and Mechanics
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• v.64 no.3
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• pp.339-346
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• 2017

Slabs prevent harmful effects of fire that may occur in any floor. However, it is necessary to protect the slabs from fire. Insulation materials may be appropriate to protect reinforced concrete (RC) slab from elevated temperature. In the present study, a model has been developed in artificial neural network (ANN) to predict the moment capacity ($M_r$) of RC slabs exposed to fire with insulation material. 672 data were obtained for ANN model through author's prepared program. Input layer in model consisted of seven input parameters; such as effective depth (d), ratio of d'/d, thermal conductivity coefficient ($k_{insulation}$), insulation materials thickness ($L_{insulation}$), reinforcement area ($A_{st}$), fire exposure time ($t_{\exp}$), and concrete compressive strength ($f_c$). The predicted $M_r$ by ANN was consistent with the obtained $M_r$ by author. It is proposed to ease computational complexity in determining $M_r$ using ANN. The effects of using insulation material on the moment capacity in RC slabs were also investigated. Insulating material with low thermal conductivity has been found to be more effective for durability to high temperature.

• Journal of Welding and Joining
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• v.21 no.4
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• pp.31-38
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• 2003

Line heating is an effective and economical process for forming flat metal plates into three-dimensional shapes for plating of ships. Because the nature of the line heating process is a transient thermal process, followed by a thermo elastic plastic stress field, predicting deformed shapes of plate is very difficult and complex problem. In this paper, neural network model o3r solving the inverse problem of metal forming is proposed. The backpropagation neural network systems for determining line-heating positions from object shape of plate are reported in this paper. Two cases of the network are constructed-the first case has 18 lines which have different positions and directions and the second case has 10 parallel heating lines. The input data are vertical displacements of plate and the output data are selected heating lines. The train sets of neural network are obtained by using an analytical solution that predicts plate deformations in line heating process. This method shows the feasibility that the neural network can be used to determine the heating-line positions in line heating process.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.11
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• pp.915-924
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• 2014

In an aero gas-turbine engine, a surface air-oil heat exchanger (SAOHE) is used to cool the oil system for the gearboxes and electric generators. The SAOHE is installed inside the fan casing of the engine in order to dissipate the heat from the oil system into the bypass duct stream. The purpose of this study was to develop an effective numerical method for designing an SAOHE for an aero gas-turbine engine. A two-dimensional model using a porous medium was developed to evaluate the aero-thermal performance of the fins of the heat exchanger, and a one-dimensional flow and thermal network program was developed to save time and cost in the evaluation of the heat exchanger performance. Using this network program, the pressure drop and heat transfer performance of the heat exchanger were predicted, and the results were compared with two-dimensional computational fluid dynamics results and experiment data for validation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1343-1351
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• 2000

Thermally induced errors have been significant factors affecting the machine tool accuracy. In this paper, the spindle thermal error has been focused, where the 5 degree of freedom thermal error components are considered. An effective measurement system has been devised for the 5 DOF thermal errors, consisting of gap sensors and thermocouples around the micro-computer interfaced environment. Several thermal error modeling techniques are also implemented for the thermal error prediction: multiple linear regression, neural network and system identification methods, etc. The performance of the thermal error modeling techniques is evaluated and compared, giving the system identification method as the optimum model having the least deviation. The developed system for the thermal error measurement and modeling was practically applied to a CNC machining center, and the spindle thermal errors were effectively compensated around the micro computer-machine tool interfaced networks. The machine tool accuracy was improved about 4-5 times typically.

• 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.

• International Journal of Fluid Machinery and Systems
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• v.9 no.4
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• pp.370-381
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• 2016

As a single-channel pump is used for wastewater treatment, this particular pump type can prevent performance reduction or damage caused by foreign substances. However, the design methods for single-channel pumps are different and more difficult than those for general pumps. In this study, a design optimization method to improve the hydrodynamic performance of a single-channel pump impeller is implemented. Numerical analysis was carried out by solving three-dimensional steady-state incompressible Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. As a state-of-the-art impeller design method, two design variables related to controlling the internal cross-sectional flow area of a single-channel pump impeller were selected for optimization. Efficiency was used as the objective function and was numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. An optimization process based on a radial basis neural network model was conducted systematically, and the performance of the optimum model was finally evaluated through an experimental test. Consequently, the optimum model showed improved performance compared with the base model, and the unstable flow components previously observed in the base model were suppressed remarkably well.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.148-153
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• 1998

In order to control thermal deformation of machine origin of machine tools due to internal and external heat sources, the real-time compensation system has been developed. First, GMDH models were constructed to estimate thermal deformation of machine origin for a vertical machining center through the measurement of deformation data and temperature data of specific points on the machine tool. Thermocouples and gap sensors are used respectively for measurement. These models are nonlinear equations with high-order polynomials and implemented in a multilayered perceptron type network structure. Secondly, work origin shift method were developed by implementing digital I/O interface board between CNC controller and IBM-PC. The work origin shift method is to shift the work origin by the compensation amounts which is calculated by pre-established GMDH model. From the experimental result, thermal deformation of machine origin was reduced to below $\pm$5${\mu}{\textrm}{m}$.

• Journal of Korean Society on Water Environment
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• v.30 no.5
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• pp.491-502
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• 2014

As meteorology is the driving force for lake thermodynamics and mixing processes, the effects of climate change on the physical limnology and associated ecosystem are emerging issues. The potential impacts of climate change on the physical features of a reservoir include the heat budget and thermodynamic balance across the air-water interface, formation and stability of the thermal stratification, and the timing of turn over. In addition, the changed physical processes may result in alteration of materials and energy flow because the biogeochemical processes of a stratified waterbody is strongly associated with the thermal stability. In this study, a novel modeling framework that consists of an artificial neural network (ANN), a watershed model (SWAT), a reservoir operation model(HEC-ResSim) and a hydrodynamic and water quality model (CE-QUAL-W2) is developed for projecting the effects of climate change on the reservoir water temperature and thermal stability. The results showed that increasing air temperature will cause higher epilimnion temperatures, earlier and more persistent thermal stratification, and increased thermal stability in the future. The Schmidt stability index used to evaluate the stratification strength showed tendency to increase, implying that the climate change may have considerable impacts on the water quality and ecosystem through changing the vertical mixing characteristics of the reservoir.

• Applied Chemistry for Engineering
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• v.8 no.6
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• pp.1034-1040
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• 1997

Temperature distribution of battery module consists of 11 batteries of 90Ah rate is analyzed using commercial software NISA II. Equivalent thermal resistance network is used to reduce the number of element in calculating heat transfer through a medium composed of several different thermal conductivity layers. Orthotropic model is used to put different thermal conductivity values according to Cartesian coordinate. Aluminum cooling fins are inserted in the middle of batteries to reduce battery module temperature. The cooling fin at the end of the module does not necessary in reducing maximum temperature. Combined effect of front and side cooling fin is analyzed to reduce the temperature difference among batteries. The maximum temperature difference among batteries is reduced within $3^{\circ}C$ when 4 aluminum cooling tin of 1mm thickness is inserted in battery module.