• Journal of the Society of Naval Architects of Korea
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• v.59 no.3
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• pp.183-191
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• 2022

A design method for a propeller type rim-driven axial-flow turbine for a micro-hydropower system is presented. The turbine consists of pre-stator, impeller and post-stator, where the pre-stator plays a role as a guide vane to provide circumferential velocity to the on-coming flow, and the impeller as a rotational power generator by absorbing angular momentum of the flow. BEM(Blade Element Method), which is based on the turbine Euler equation, is employed to design the pre-stator and impeller blades. NACA 66 thickness form and a=0.8 mean camber line, which is widely accepted as a marine propeller blade section, is used for the pre-stator and turbine blade section. A CFD method, derived from the discretization of the RANS equations, is applied for the analysis of the designed turbine system. The design conditions of the turbine is confirmed by the CFD calculation. Turbine characteristic curve is calculated by the CFD method, in order to provide the performance characteristics at off-design operation conditions. The proposed procedures for the design of a propeller type rim-driven axial-flow turbine are established and confirmed by the CFD analysis.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2599-2605
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• 2022

The Korea Retrospective Dosimetry network (KREDOS) performed an inter-laboratory comparison to confirm the harmonization and reliability of the results of retrospective dosimetry using mobile phone. The mobile phones were exposed to ¹⁹²Ir while attached to the human phantoms in the field experiment, and the exposure doses read by each laboratory were compared. This paper describes the reference dosimetry performed to present the reference values for inter-comparison and to obtain additional information about the dose distribution. Reference dosimetry included both measurement using LiF:Mg,Cu,Si and calculation via MCNP simulation to allow a comparison of doses obtained with the two different methodologies. When irradiating the phones, LiF elements were attached to the phones and phantoms and irradiated at the same time. The comparison results for the front of the phantoms were in good agreement, with an average relative difference of about 10%, while an average of about 16% relative difference occurred for the back and side of the phantom. The differences were attributed to the different characteristics of the physical and simulated phantoms, such as anatomical structure and constituent materials. Nevertheless, there was about 4% of under-estimation compared to measurements in the overall linear fitting, indicating the calculations were well matched to the measurements.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.778-789
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• 2021

Recently, various lightweight methods for using Convolutional Neural Network(CNN) models in mobile devices have emerged. Weight quantization, which lowers bit precision of weights, is a lightweight method that enables a model to be used through integer calculation in a mobile environment where GPU acceleration is unable. Weight quantization has already been used in various models as a lightweight method to reduce computational complexity and model size with a small loss of accuracy. Considering the size of memory and computing speed as well as the storage size of the device and the limited network environment, this paper proposes a method of compressing integer weights after quantization using a video codec as a method. To verify the performance of the proposed method, experiments were conducted on VGG16, Resnet50, and Resnet18 models trained with ImageNet and Places365 datasets. As a result, loss of accuracy less than 2% and high compression efficiency were achieved in various models. In addition, as a result of comparison with similar compression methods, it was verified that the compression efficiency was more than doubled.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• Atmosphere
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• v.33 no.4
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• pp.331-341
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• 2023

A numerical forecasting models usually predict future states by performing time integration considering fixed static time-steps. A time-step that is too long can cause model instability and failure of forecast simulation, and a time-step that is too short can cause unnecessary time integration calculations. Thus, in numerical models, the time-step size can be determined by the CFL (Courant-Friedrichs-Lewy)-condition, and this condition acts as a necessary condition for finding a numerical solution. A static time-step is defined as using the same fixed time-step for time integration. On the other hand, applying a different time-step for each integration while guaranteeing the stability of the solution in time advancement is called an adaptive time-step. The adaptive time-step algorithm is a method of presenting the maximum usable time-step suitable for each integration based on the CFL-condition for the adaptive time-step. In this paper, the adaptive time-step algorithm is applied for the Korean Integrated Model (KIM) to determine suitable parameters used for the adaptive time-step algorithm through the monthly verifications of 10-day simulations (during January and July 2017) at about 12 km resolution. By comparing the numerical results obtained by applying the 25 second static time-step to KIM in Supercomputer 5 (Nurion), it shows similar results in terms of forecast quality, presents the maximum available time-step for each integration, and improves the calculation efficiency by reducing the number of total time integrations by 19%.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.1-6
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• 2023

One of the economical ways to satisfy the quality of service desired by the user in a web service environment is to adjust the size of the object. To this end, this study finds the maximum size of objects that satisfy this constraint when the mean response time is given below an arbitrary threshold for quality of service. It can be inferred that in the steady state of system, the mean response time in the deterministic model by using the round-robin will be the same as that of the queueing model following the general distribution. Based on this, analytical formulas and procedures for finding the maximum object size are obtained. As a service distribution of web traffic, the Pareto distribution is appropriate, so the maximum object size is computed by applying the M/G(Pareto)/1 model and the M/G/1/PS model using exponential distribution as computational experience. Performance evaluation through numerical calculation shows that as the shape parameter in the Pareto distribution increases, the M/G(Pareto)/1 model and M/G/1/PS model have the same maximum object size. The results of this study can be used to environments where objects can be sized for economical web service control.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.121-129
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• 2009

In this study, dam-break flows are simulated numerically by using an efficient and accurate Cartesian cut-cell mesh system. In the system, most of the computational domain is discretized by the Cartesian mesh, while peculiar grids are done by a cutcell mesh system. The governing equations are then solved by the finite volume method. An HLLC approximate Riemann solver and TVD-WAF method are employed to calculation of advection flux of the shallow-water equations. To validate the numerical model, the model is applied to some problems such as a steady flow convergence on an ideal bed, a steady flow over an irregular bathymetry, and a rectangular tank problem. The present model is finally applied to a simulation of dam-break flow on an experimental channel. The predicted water surface elevations are compared with available laboratory measurements. A very reasonable agreement is observed.

• Journal of Veterinary Science
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• v.24 no.5
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• pp.67.1-67.15
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• 2023

Background: Feline immunodeficiency virus (FIV) causes an acquired immunodeficiency-like syndrome in cats. FIV is latent. No effective treatment has been developed for treatment the infected cats. The first and second generations non-nucleoside reverse transcriptase inhibitors (NNRTIs) for HIV treatment, nevirapine (NVP) and efavirenz (EFV), and rilpivirine (RPV), were used to investigate the potential of NNRTIs for treatment of FIV infection. Objective: This study aims to use experimental and in silico approaches to investigate the potential of NNRTIs, NVP, EFV, and RPV, for inhibition of FIV reverse transcriptase (FIV-RT). Methods: The FIV-RT and human immunodeficiency virus reverse transcriptase (HIV-RT) were expressed and purified using chromatography approaches. The purified proteins were used to determine the IC₅₀ values with NVP, EFV, and RPV. Surface plasmon resonance (SPR) analysis was used to calculate the binding affinities of NNRTIs to HIV-RT and FIV-RT. The molecular docking and molecular dynamic simulations were used to demonstrate the mechanism of FIV-RT and HIV-RT with first and second generation NNRTI complexes. Results: The IC₅₀ values of NNRTIs NVP, EFV, and RPV against FIV-RT were in comparable ranges to HIV-RT. The SPR analysis showed that NVP, EFV, and RPV could bind to both enzymes. Computational calculation also supports that these NNRTIs can bind with both FIV-RT and HIV-RT. Conclusions: Our results suggest the first and second generation NNRTIs (NVP, EFV, and RPV) could inhibit both FIV-RT and HIV-RT.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.117-123
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• 2024

Bubbles generated in water receive an upward buoyant force due to the density and pressure difference of the surrounding fluid. Additionally, the behavior, shape, and heat exchange process of bubbles vary depending on the viscosity, surface tension, rising speed, and size difference with the surrounding fluid. In this study, we modeled speed, and heat transfer of a high-temperature single bubble rising in a cylindrical water tank. For this purpose, velocity, and temperature of the bubbles were calculated using theoretical equations, to be compared with numerical simulation results. The numerical analysis was performed using a commercial software, and the stability of the numerical analysis with mesh size was confirmed through calculation of the grid convergence index. The numerical analysis of the rising speed and temperature of a single bubble showed the values to converge when the minimum cell size was 1/160 of the bubble diameter, and the temperature decrease was confirmed to be the same as that of the surrounding fluid within 0.05 seconds.