• Transactions of the Korean hydrogen and new energy society
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• v.34 no.6
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• pp.773-784
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• 2023

In this study, a comparative analysis between an electric heat pump cooling system and a hybrid desiccant cooling system is conducted. Desiccant cooling is a thermal driven system with potentially lower electric power consumption than electric heat pump. Hybrid desiccant cooling system simulation includes components such as a desiccant rotor, direct and indirect evaporative coolers, heat exchangers, fans, and a heat pump system. Using dynamic simulations by climate conditions, house cooling temperatures and power consumption for both systems are analyzed for 16 days period in the summer season under climate scenarios for the year 2100 prediction. The results reveal that the hybrid desiccant cooling system exhibits a 5-18% reduction in electric consumption compared to the heat pump system.

• Journal of Mechanical Science and Technology
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• v.16 no.6
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• pp.839-850
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• 2002

A performance simulation program for the turboprop engine (PT6A-62), which is the power plant of the first Korean indigenous basic trainer KT-1, was developed for performance prediction, development of an EHMS (Engine Health Monitoring System) and the flight simulator. Characteristics of components including compressors, turbines, power turbines and the constant speed propeller were required for the steady state and transient performance analysis with on and off design point analysis. In most cases, these were substituted for what scaled from similar engine components'characteristics with the scaling law. The developed program was evaluated with the performance data provided by the engine manufacturer and with analysis results of GASTURB program, which is well known for the performance simulation of gas turbines. Performance parameters such as mass flow rate, compressor pressure ratio, fuel flow rate, specific fuel consumption and turbine inlet temperature were discussed to evaluate validity of the developed program at various cases. The first case was the sea level static standard condition and other cases were considered with various altitudes, flight velocities and part loads with the range between idle and 105% rotational speed of the gas generator. In the transient analysis, the Continuity of Mass Flow Method was utilized under the condition that mass stored between components is ignored and the flow compatibility is satisfied, and the Modified Euler Method was used for integration of the surplus torque. The transient performance analysis for various fuel schedules was performed. When the fuel step increase was considered, the overshoot of the turbine inlet temperature occurred. However, in case of ramp increase of the fuel longer than step increase of the fuel, the overshoot of the turbine inlet temperature was effectively reduced.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• Ocean Systems Engineering
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• v.10 no.3
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• pp.333-357
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• 2020

International Maritime Organisation (IMO) regulations insist on reduced emission of CO₂, noxious and other environmentally dangerous gases from ship, which are usually let out while burning fossil fuel for running its propulsive machinery. Contrallability of ship during sailing has a direct implication on its course keeping and changing ability, and tries to have an optimised routing. Bad coursekeeping ability of a ship may lead to frequent use of rudder and resulting changes in the ship's drift angle. Consequently, it increases vessels resistance and also may lead to longer path for its journey due to zigzag movements. These adverse effects on the ship journey obviously lead to the increase in fuel consumption and higher emission. Hence, IMO has made it mandatory to evaluate the manoeuvring qualities of a ship at the designed stage itself. In this paper a numerical horizontal planar motion mechanism is simulated in CFD environment and from the force history, the hydrodynamic derivatives appearing in the manoeuvring equation of motion of a ship are estimated. These derivatives along with propeller thrust and rudder effects are used to simulate different standard manoeuvres of the vessel and check its parameters against the IMO requirements. The present study also simulates these manoeuvres by using numerical free running model for the same ship. The results obtained from both these studies are presented and discussed here.

• The KSFM Journal of Fluid Machinery
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• v.17 no.2
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• pp.48-53
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• 2014

A low-noise regenerative blower is developed for fuel cell application by combining the FANDAS-Regen code and design optimization algorithm under several performance constraints for flow capacity, static pressure, efficiency and power consumption. The optimized blower design model is manufactured with some impeller modification based on low noise design concept and tested by using aerodynamic performance chamber facility and narrow-band noise measurement apparatus. The measured results of the optimized blower satisfy the performance requirements and are also compared favorably with the FANDAS-Regen prediction results within a few percent relative error. Furthermore, the present study shows the remarkable noise reduction by 26 dBA can be achieved through design optimization and low noise design concept.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.278-287
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• 2015

The potential impact of aircraft emissions on the current and projected climate of our planet is one of the more important environmental issues facing the aviation industry. Increasing concern over the potential negative effects of greenhouse gas emissions has motivated the development of an aircraft emission estimation and prediction system as one of the ways to reduce aircraft emissions and mitigate the impact of aviation on climate. Hence, in this research, using Piano-X software which was developed by Lissys Co., fuel consumption and emissions for 3 types of aircraft were estimated for different design payloads with various flight distances and flight paths. Fuel burns for economy speed, long range cruise speed, maximum range speed were also investigated with various flight distances and altitudes.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.1-7
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• 2012

The transportation is almost dependent on a single fuel petroleum with transportation energy dilemma. Hybrid Electric Vehicle(HEV) technology holds more advantages on efficiency improvements for petroleum consumption at the transportation. And bearing is recognized as the important component of gearbox. Gearboxes for HEV transmission have been ensured the highest reliability over some years in withstanding high dynamic loads. At the same time, the demands of lightweight design and cost minimization are required by thought-out design, high-quality material, superior production quality and maintenance. In order to design a reliable and lightweight gearbox, it is necessary to analyze bearing rating life methods between standard and different bearing companies with calculation methods for modification factors. In this paper, the influence of life time of bearings will be pointed out. Bearing contact stress and load stress distribution of HEV gearbox are obtained and compared with Romaxdesigner and BearinX. And the unequal wear of the left bearing for the gearbox intermediate shaft is investigated between simulation and test.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.106-111
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• 1992

In order to achieve stable and efficient use of energy at iron and steel works, a model for the prediction of supply and demand of electric power system is developed on the basis of the information on operation and particular patterns of electric power consumption. The optimal amount of electric power to be purchased and the optimal fuel allocation for the in-house electric power plants are also obtained by a mixed-integer linear programming(MILP) and a nonlinear programming (NLP) solutions, respectively. The validity and the effectiveness of the proposed model are investigated by several illustrative examples. The simulation results show the satisfactory energy saving by the optimal solution obtained through this research.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.25-30
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• 2012

The transportation is almost dependent on a single fuel petroleum with transportation energy dilemma. Hybrid Electric Vehicle(HEV) technology holds more advantages on efficiency improvements for petroleum consumption at the transportation. And bearing is recognized as the important component of gearbox. Gearboxes for HEV transmission have been ensured the highest reliability over some years in withstanding high dynamic loads. At the same time, the demands of lightweight design and cost minimization are required by thought-out design, high-quality material, superior production quality and maintenance. In order to design a reliable and lightweight gearbox, it is necessary to analyze bearing rating life methods between standard and different bearing companies with calculation methods for modification factors. In this paper, the influence of life time of bearings will be pointed out. Bearing contact stress and load stress distribution of HEV gearbox are obtained and compared with Romaxdesigner and BearinX. And the unequal wear of the left bearing for the gearbox intermediate shaft is investigated between simulation and test.