• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.3
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• pp.373-380
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• 2019

In this paper, a pico hydro turbine employing low head circulation water at fish farms is designed and evaluated. Due to the advantages of simple structures, small head requirements, and low-cost investment, the constant thickness propeller turbine is considered as a feasible solution. The design process based on the free vortex method is presented in full detail, and a 4-blade runner is built using BladeGen. The turbine performance is analyzed both numerically and via experimental methods. Despite slight differences, the results show similar trends between CFD simulations and experiments carried out on factory test-rigs in a wide range of working conditions. At the design flow rate, the turbine achieves the best efficiency of 70 %, generating 3.5 kW power when rotating at 420 rpm. The internal flow field, as well as the turbine's behavior, are investigated through the distribution of blade streamlines, pressure, and velocity around the runner. Moreover, the pressure coefficient on the blade surface at 3 span positions is plotted while the head loss for each simulation domain is calculated and displayed by charts.

• Steel and Composite Structures
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• v.31 no.2
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• pp.201-216
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• 2019

This scientific paper provides a theoretical, numerical and experimental analysis of local stability of axially compressed columns made of thin-walled rectangular concrete-filled steel tubes (CFSTs), with the consideration of initial geometric imperfections. The work presented introduces the theory of elastic critical stresses in local buckling of rectangular wall members under uniform compression. Moreover, a numerical calculation method for the determination of the critical stress coefficient is presented, using a differential equation for a slender wall with a variety of boundary conditions. For comparison of the results of the numerical analysis with those collected by experiments, a new model is created to study the behaviour of the composite members in question by means of the ABAQUS computational-graphical software whose principles are based on the finite element method (FEM). In modelling the analysed members, the actual boundary and loading conditions and real material properties are taken into account, obtained from the experiments and material tests on these members. Finally, the results of experiments on such members are analysed and then compared with the numerical values. In conclusion, several recommendations for the design of axially compressed composite columns made of rectangular concrete-filled thin-walled steel tubes are suggested as a result of this comparison.

• Steel and Composite Structures
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• v.42 no.2
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• pp.161-172
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• 2022

In this paper, four specimens of CFST column joints with endplates and long bolts are tested in the scenario of progressive collapse. Flush endplate and extended endplate are both adopted in this study. The experimental results show that increasing the thickness of the endplate could improve the behavior of the joint, but delay the mobilization of catenary action. The thickness of the endplate should not be relatively thick in comparison to the diameter of the bolts, otherwise catenary action would not be mobilized or work effectively. Effective bending deformation of the endplate could help the formation and development of catenary action in the joints. The performance of flexural action in the joint would affect the formation of catenary action in the joint. Extra middle-row bolts set at the endplates and structural components set below the bottom beam flange should be used to enhance the robustness of joints. A special weld access hole between beam and endplate should be adopted to mitigate the chain damage potential of welds. It is suggested that the structural components of joints should be independent of each other to enhance the robustness of joints. Based on the component method, a formula calculating the stiffness coefficient of preloaded long bolts was proposed whose results matched well with the experimental results.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1652-1657
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• 2021

Nuclear materials can be utilized not only for peaceful uses, but also for military purposes; hence, the international community has devoted itself to the control, management and safeguarding of nuclear materials. Nuclear materials are of varying degrees of usability for development of nuclear weapons. Thus, several methods for assessing the attractiveness of nuclear materials for nuclear weapons purposes have been proposed. When these methods are applied to unirradiated depleted, natural, and low-enriched uranium (DU, NU, and LEU), they are certainly classified as non-attractive nuclear materials. However, when nuclear material attractiveness is to be evaluated for potential radiological dispersal device (RDD) uses, it is required to develop a different method for the different aspects and factors. In the present study, we derived a novel method for evaluating nuclear material attractiveness for use in RDD development. To this end, the specific activity and dose coefficient were identified as the two sub-factors, and, in consideration of those, the mass causing detrimental health effects was determined to be the main factor impacting on nuclear materials attractiveness. Based on this factor, the attractiveness of unirradiated DU, NU, and LEU for RDD use was qualitatively compared with that of ¹³⁷Cs.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.183-194
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• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.110-119
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• 2023

The prediction of ship resistance performance is typically obtained by Computational Fluid Dynamics (CFD) simulations or model tests in towing tank. However, these methods are both costly and time-consuming, so hull-form designers use statistical methods for a quick feed-back during the early design stage. It is well known that results from statistical methods are often less accurate compared to those from CFD simulations or model tests. To overcome this problem, this study suggests a new approach using a Convolution Neural Network (CNN) with voxelized hull-form data. By converting the original Computer Aided Design (CAD) data into three dimensional voxels, the CNN is able to abstract the hull-form data, focusing only on important features. For the verification, suggested method in this study was compared to a parametric method that uses hull parameters such as length overall and block coefficient as inputs. The results showed that the use of voxelized data significantly improves resistance performance prediction accuracy, compared to the parametric approach.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.723-729
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• 2022

In this study, the multi perforated plate is used to reduce the compressor noise in the low frequency band inside the refrigerator machine room. To predict the sound absorption results, the impedance of the sound absorption material is measured. Using the measured impedance results, it is confirmed that the results used for FEM analysis is almost similar to the experimental values. The sound-absorbing structure that can operate in the target frequency band inside the refrigerator machine room is designed by controlling the hole diameter and arrangement in the perforated plate. The effect of reducing noise in the low frequency band is confirmed by applying perforated plate-based sound absorbing structures to the machine room.

• New & Renewable Energy
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• v.18 no.1
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• pp.17-28
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• 2022

This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.142-142
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• 2022

Adjusting weather radar data is a prerequisite for its use in various hydrological studies. Effect of spatial variables are considered to adjust weather radar data in many of these researches. The existence of diverse topography in South Korea has increased the importance of analyzing these variables. In this study, some spatial variable like slope, elevation, aspect, distance from the sea, plan and profile curvature was considered. To investigate different topographic conditions, tried to use three radar station of Gwanaksan, Gwangdeoksan and Gudeoksan which are located in northwest, north and southeast of South Korea, respectively. To form the suitable fuzzy model and create the best membership functions of variables, ANFIS-PSO model was applied. After optimizing the model, the correlation coefficient and sensitivity of adjusted Quantitative Precipitation Estimation (QPE) based on spatial variables was calculated to find how variables work in adjusted QPE process. The results showed that the variable of elevation causes the most change in rainfall and consequently in the adjustment of radar data in model. Accordingly, the sensitivity ratio calculated for variables shows that with increasing rainfall duration, the effects of these variables on rainfall adjustment increase. The approach of this study, due to the simplicity and accuracy of this method, can be used to adjust the weather radar data and other required models.