• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.735-742
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• 2021

Recently, renewable energy-related power generation facilities have been surging due to the government's "Renewable Energy 3020", "Green New Deal", "2050 Carbon Neutrality" and "K-RE100" policies. Most renewable energy facilities are small and distributed, making it difficult to manage efficiently, and small distributed resources less than 1MW are having a hard time with participating in the market due to the limited sales and avoidance of trading. In particular, the intermittency of renewable energy has a significant impact on the stability of the power grid. The government is seeking to address volatility and intermittency issues through 'small distributed resource brokerage trading, and to expand the systematic resourceization and acceptability of heterogeneous large and small distributed resources. In this work, we intend to apply an AI-based power generation prediction model to a distributed resource brokerage trading system so that it can be utilized as a foundation platform for pioneering new energy business markets.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1195-1205
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• 2022

Recent developments on spectral diffusion algorithms, i.e., algorithms which exploit the projection of the solution on the eigenfunctions of the Laplacian operator, demonstrated their effective applicability in fast transient conditions. Nevertheless, the numerical error introduced by these algorithms, together with the uncertainties associated with model parameters, may impact the reliability of the predictions on short-lived volatile fission product release from nuclear fuel. In this work, we provide an upper bound on the numerical error introduced by the presented spectral diffusion algorithm, in both constant and time-varying conditions, depending on the number of modes and on the time discretization. The definition of this upper bound allows introducing a methodology to a priori bound the numerical error on short-lived volatile fission product retention.

• Journal of Ocean Engineering and Technology
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• v.37 no.5
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• pp.181-189
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• 2023

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.571-578
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• 2024

The bistable thin cylindrical shell is developable structure with the ability to transition between its two stable configurations. This structure offers significant potential applications due to its excellent deformability. In this paper, the composite thin cylindrical shell consisting of the composite layer and the piezoelectric layer was investigated. The material and geometric parameters of the shell were found to influence its stable characteristics. The analysis model of the composite thin cylindrical shell incorporating the piezoelectric layer was developed, and the expressions for its strain energy were derived. By applying the minimum energy principle, the impact of the electric field intensity on the bi-stable behaviors of the cylindrical shell was analyzed. The results showed that the shell exhibited the bistability only under the appropriate electric field strength. And the accuracy of the theoretical prediction was verified by simulation experiments. This study provides an important reference for the application of deployable structures.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.12-19
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• 2006

It is positively necessary to study on the stiffness of a 13degree-type impact tester in order to improve the fracture prediction of impact testing in wheels using FE(finite-element) analysis. The 13degree-type impact tester consists of an impact striker, a wheel fixer, a steel plate, and four cylindrical rubbers. Important parts of the tester are the steel plate and four cylindrical rubbers which play a role of absorbing impact energy during impact testing. Because of these buffers, the RF(reaction force) variation of the lower part in the 13degree-type impact tester showed the tendency like a damped harmony oscillation during impact testing. In order to investigate the stiffness of a 13degree-type impact tester, this work measured each stiffness of a steel plate and cylindrical rubbers. The stiffness of a cylindrical rubber was measured using a compressive tester. On the other hand, the stiffness of a steel plate was predicted by simulating experimental method using FE analysis.

• Structural Engineering and Mechanics
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• v.58 no.5
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• pp.799-823
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• 2016

A finite element model for the non-linear dynamic analysis of a reinforced concrete (RC) containment shell of a nuclear power plant subjected to extreme loads such as impact and earthquake is presented in this work. The impact is modeled by using an uncoupled approach in which a load function is applied at the impact zone. The earthquake load is modeled by prescribing ground accelerations at the base of the structure. The nuclear containment is discretized spatially by using 20-node brick finite elements. The concrete in compression is modeled by using a modified $Dr{\ddot{u}}cker$-Prager elasto-plastic constitutive law where strain rate effects are considered. Cracking of concrete is modeled by using a smeared cracking approach where the tension-stiffening effect is included via a strain-softening rule. A model based on fracture mechanics, using the concept of constant fracture energy release, is used to relate the strain softening effect to the element size in order to guaranty mesh independency in the numerical prediction. The reinforcing bars are represented by incorporated membrane elements with a von Mises elasto-plastic law. Two benchmarks are used to verify the numerical implementation of the present model. Results are presented graphically in terms of displacement histories and cracking patterns. Finally, the influence of the shear transfer model used for cracked concrete as well as the effect due to a base slab incorporation in the numerical modeling are analyzed.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.4
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• pp.380-386
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• 2020

This paper represents an analysis of the economic impact of firing natural gas/diesel and natural gas/by-product oil mixtures in diesel engine power plants. The objects of analysis is a power plant with electricity generation capacity (300 kW). Using performance data of original diesel engines, the fuel consumption characteristics of the duel fuel engines were simulated. Then, economic assessment was carried out using the performance data and the net present value method. A special focus was given to the evaluation of fuel cost saving when firing natural gas/diesel and natural gas/by-product oil mixtures instead of the pure diesel firing case. Analyses were performed by assuming fuel price changes in the market as well as by using current prices. The analysis results showed that co-firing of natural gas/diesel and natural gas/by-product oil would provide considerable fuel cost saving, leading to meaningful economic benefits.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.6
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• pp.608-614
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• 2023

The fast filling process of high-pressure hydrogen has an important impact on the filling efficiency and safety. In this paper, a specific study is carried out on the thermophysical phenomena during the fast filling process. Starting from the gas state equation of hydrogen, the change law of the hydrogen storage temperature is obtained, and then the temperature rise prediction is constructed. The model can clarify the relationship between the filling parameters and the temperature rise during the fast filling process, thereby revealing the flow and heat transfer laws of the fast charging process. To improve the theoretical research basis for the evaluation of vehicle-mounted hydrogen fast charging capacity, temperature prediction and optimization of hydrogenation methods.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.135-155
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• 2019

One of the safest and the most economical methods to transfer oil and gas is pipeline system. Prediction and prevention of pipeline failures during its assessed lifecycle has considerable importance. The dropped object is one of the accidental scenarios in the failure of the submarine pipelines. In this paper, using Monte Carlo Sampling, the probability of damage to a submarine pipeline due to a box-shaped dropped object has been calculated in terms of dropped object impact frequency and energy transfer according to the DNV-RP-F107. Finally, Reliability sensitivity analysis considering random variables is carried out to determine the effect intensity of each parameter on damage probability. It is concluded that impact area and drag coefficient have the highest sensitivity and mass and add mass coefficient have the lowest sensitivity on probability of failure.