• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.105-110
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• 2017

Underfloor air distribution (UFAD) is an air distribution strategy for providing ventilation and space conditioning in buildings. UFAD systems use the underfloor plenum beneath a raised access floor to provide conditioned air through floor diffusers that create a vertical thermal stratification during cooling operations. Thermal stratification has significant effects on energy, indoor air quality, and thermal comfort performance. The purpose of this study was to characterize the influence of a linear bar grille diffuser on thermal stratification in both interior and perimeter zones by developing Gamma-Phi based prediction models. Forty-eight simulations were carried out using a Computational Fluid Dynamics (CFD) technique. The number of diffusers, the air flow supply, internal heat gains, and solar radiations varied among the different cases. Models to predict temperature stratification for the tested linear bar grille diffuser have been developed, which can be directly implemented into dynamic whole-building simulation software such as EnergyPlus.

• Nuclear Engineering and Technology
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• v.45 no.4
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• pp.439-458
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• 2013

REX-10 is a fully-passive small modular reactor in which the coolant flow is driven by natural circulation, the RCS is pressurized by a steam-gas pressurizer, and the decay heat is removed by the PRHRS. To confirm design decisions and analyze the transient responses of an integral PWR such as REX-10, a thermal-hydraulic system code named TAPINS (Thermal-hydraulic Analysis Program for INtegral reactor System) is developed in this study. Based on a one-dimensional four-equation drift-flux model, TAPINS incorporates mathematical models for the core, the helical-coil steam generator, and the steam-gas pressurizer. The system of difference equations derived from the semi-implicit finite-difference scheme is numerically solved by the Newton Block Gauss Seidel (NBGS) method. TAPINS is characterized by applicability to transients with non-equilibrium effects, better prediction of the transient behavior of a pressurizer containing non-condensable gas, and code assessment by using the experimental data from the autonomous integral effect tests in the RTF (REX-10 Test Facility). Details on the hydrodynamic models as well as a part of validation results that reveal the features of TAPINS are presented in this paper.

• Journal of computational fluids engineering
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• v.20 no.1
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• pp.84-91
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• 2015

SMART adopts, very unique facility, an FMHA to enhance the thermal and flow mixing capability in abnormal conditions of some steam generators or reactor coolant pumps. The FMHA is important for enhancing thermal mixing of the core inlet flow during a transient and even during accidents, and thus it is essential that the thermal mixing characteristics of flow of the FMHA be understood. Investigations for the mixing characteristics of the FMHA had been performed by using experimental and CFD methods in KAERI. In this study, the temperature distribution at the core inlet region is investigated for several abnormal conditions of steam generators using the commercial code, FLUENT 12. Simulations are carried out with two kinds of FMHA shapes, different mesh resolutions, turbulence models, and steam generator conditions. The CFD results show that the temperature deviation at the core inlet reduces greatly for all turbulence models and steam generator conditions tested here, and the effect of mesh refinement on the temperature distribution at the core inlet is negligible. Even though the uniformity of FMHA outlet hole flow increases the thermal mixing, the temperature deviation at the core inlet is within an acceptable range. We numerically confirmed that the FMHA applied in SMART has an excellent mixing capability and all simulation cases tested here satisfies the design requirement for FMHA thermal mixing capability.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.1
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• pp.56-65
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• 2024

Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.

• Current Optics and Photonics
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• v.6 no.1
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• pp.69-78
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• 2022

A high-resolution camera is a precise optical system. Its vibrations during transportation and launch, together with changes in temperature and gravity field in orbit, lead to different degrees of defocus of the camera. Thermal refocusing is one of the solutions to the problems related to in-orbit defocusing, but there are few relevant thermal refocusing mathematical models for systematic analysis and research. Therefore, to further research thermal refocusing systems by using the development of a high-resolution micro-nano satellite (CX6-02) super-resolution camera as an example, we established a thermal refocusing mathematical model based on the thermal elasticity theory on the basis of the secondary mirror position. The detailed design of the thermal refocusing system was carried out under the guidance of the mathematical model. Through optical-mechanical-thermal integration analysis and Zernike polynomial calculation, we found that the data error obtained was about 1%, and deformation in the secondary mirror surface conformed to the optical index, indicating the accuracy and reliability of the thermal refocusing mathematical model. In the final ground test, the thermal vacuum experimental verification data and in-orbit imaging results showed that the thermal refocusing system is consistent with the experimental data, and the performance is stable, which provides theoretical and technical support for the future development of a thermal refocusing space camera.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.187-192
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• 1993

Thermal deformation causes large amount of machine tool errors. In order to compensate for thermal and geometric errors of the machine tool an off-line geometric adaptive control (GAC) scheme was developed. THe GAC method was realized by using a measuring plate made of precision spheres. Error vectors and volumetric errors were measured by the measuring plate. Error compensation models were obtained from error vectors and a kinematic chain of machine tools. Reliability of the GAC system of thermal and geometric errors were confrimed by large amount of experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.147-150
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• 1996

This paper describes controller design and implementation method for controlling the temperature distribution in a thermal stratified wind tunnel(TSWT) by using a neural network algorithm. It is impossible to derive a mathematical model of the relation between heat inputs and temperature outputs in the test section of the TSWT governed by a nonlinear turbulent flow. Thus inverse neural network models with a multi layer perceptron structure are used in a feedforward control loop and feedback control loop to generate an arbitrary temperature distribution in the test section of the TSWT.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.39-52
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• 2009

Theory-based models and high performance simulations are briefly reviewed starting with atomistic methods, such as Electronic Structure calculations, Molecular Dynamics, and Monte Carlo, continuing with meso-scale methods, such as Dislocation Dynamics and Phase Field, and ending with continuum methods that include Finite Element and Finite Volume. Special attention is paid to relating thermo-mechanical and chemical properties of the fuel to reactor parameters. By inserting atomistic models of point defects into continuum thermo-chemical calculations, a model of oxygen diffusivity in $UO_{2+x}$ is developed and used to predict point defect concentrations, oxygen diffusivity, and fuel stoichiometry at various temperatures and oxygen pressures. The simulations of coupled heat transfer and species diffusion demonstrate that including the dependence of thermal conductivity and density on composition can lead to changes in the calculated centerline temperature and thermal expansion displacements that exceed 5%. A review of advanced nuclear fuel performance codes reveals that the many codes are too dedicated to specific fuel forms and make excessive use of empirical correlations in describing properties of materials. The paper ends with a review of international collaborations and a list of lessons learned that includes the importance of education in creating a large pool of experts to cover all necessary theoretical, experimental, and computational tasks.

• Nuclear Engineering and Technology
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• v.37 no.6
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• pp.511-524
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• 2005

Thermalhydraulic reactor simulation of tomorrow will require a new generation of codes combining at least three scales, the CFD scale in open medium, the component scale and the system scale. DNS will be used as a support for modelling more macroscopic models. NEPTUNE is such a new generation multi-scale platform developed jointly by CEA-DEN and EDF-R&D and also supported by IRSN and FRAMATOME-ANP. The major steps towards the next generation lie in new physical models and improved numerical methods. This paper presents the advances obtained so far in physical modelling for each scale. Macroscopic models of system and component scales include multi-field modelling, transport of interfacial area, and turbulence modelling. Two-phase CFD or CMFD was first applied to boiling bubbly flow for departure from nucleate boiling investigations and to stratified flow for pressurised thermal shock investigations. The main challenges of the project are presented, some selected results are shown for each scale, and the perspectives for future are also drawn. Direct Numerical Simulation tools with Interface Tracking Techniques are also developed for even smaller scale investigations leading to a better understanding of basic physical processes and allowing the development of closure relations for macroscopic and CFD models.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.