• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.29-35
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• 2020

In this paper, we established a prediction model of fuel consumption at the aircraft's taxi operation. To look for countermeasures to reduce fuel consumption and carbon emissions, Airbus A380's actual ground taxi data was used. As a result, the number of stops or turnings during the taxi operation was not related to fuel consumption. It was confirmed that the amount of fuel consumption in the taxi operation was the taxi time and the thrust change. It can be confirmed that ground control optimization, which is the result of close cooperation between the control organization and the airline, is absolutely necessary to reduce taxi time and minimize the occurrence of thrust change events.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.203-210
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• 2003

A simulation study was carried to analyze the vehicle fuel consumption on component basis. Experiments was also carried out to identify the simulation results, under FTP-75 Hot Phase driving conditions. and arbitrary driving conditions. A good quantitative agreement was obtained. Based on the simulation, fuel energy was used in pumping loss(3.7%), electric power generation(0.7%), engine friction(12.7%), engine inertia(0.7%), torque converter loss(4.6%), drivetrain friction(0.6%), road-load(9.2%), and vehicle inertia(13.4%) under FTP-75 Hot Phase driving conditions. Using simulation program, the effects of capacity factor and idle speed on fuel consumption were estimated. A increment of capacity factor of torque converter resulted in fuel consumption improvement under FTP-75 Hot Phase driving conditions. Effect of a decrement of idle speed on fuel consumption was negligible under the identical driving conditions.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.335-343
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• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.182-190
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• 2023

This study proposes a predictive model using XGBoost and SHapley Additive exPlanation (SHAP) to estimate fuel consumption in bulk carriers. Previous studies have also utilized ship engine data and weather data. However, they lacked reliability in predicted results and explanations of variables used in the fuel consumption prediction model implementation. To address these limitations, this study developed a predictive model using XGBoost and SHAP. It provides research background, scope, relevant regulations, previous studies, and research methodology. Additionally, it explains the data cleaning method for bulk carriers and verifies results of the predictive model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2582-2589
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• 2011

This Paper proposed the prediction method of fuel consumption from vehicle diagnosis informations through OBD-II Interface. We assumed mass air flow (MAF), shor-term fuel trim (STFT), and long-term fuel trim (LTFT) had a relationship with fuel consumption. We got the output as fuel-consumption from MAF, STFT, and LTFT as input variables. We had modelling as combustion reaction equation with OBD-II data and fuel consumption data supported by automotive company in real. In order to verify the effectiveness of proposed method, 5 km real road-test was performed. The results showed that the proposed method can estimate precisely the fuel consumption from vehicle data.

• Journal of Biosystems Engineering
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• v.35 no.1
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• pp.1-9
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• 2010

A mathematical model was developed to predict the fuel consumption rate consumed by agricultural tractors under arbitrary loaded conditions. The model utilizes the measured data on the fuel consumptions at the full load and at the rated engine speed with partial loads, which can easily be obtained from the official OECD tractor test reports. It was found from the analysis of the measured fuel consumption data that the fuel consumptions at two different speeds does not change with power. The model was developed based on this fact and validated with the measured data of the 159 tractor test reports. The fuel consumptions predicted by the model were compared with those measured under the partially loaded conditions specified in the official OECD tractor test code II. The percent errors of the predicted fuel consumptions were in a range from 0.36 to 2.86% which assured that the developed fuel consumption model can be used practically to predict the fuel consumptions at any speed and power combinations. It was also shown that the developed model predicts the fuel consumption rate better than the Grisso's model.

• 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.

• Journal of the korean Society of Automotive Engineers
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• v.3 no.3
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• pp.49-57
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• 1981

A cycle simulation of 4 cycle spark ignition engine using methanol-water blend as a fuel has been developed for study of prediction of power, specific fuel consumption, mean effective pressure and thermal efficiency. One-dimensional flow model for intake process and thermodynamic model for combustion process were selected. After, performance test was made with conventional engine which was modified in consideration of fuel properties. And computational results by simulation have been compared with experimental results. As the agreement between computational and experimental results was good, prediction of engine performance by was possible.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.5
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• pp.541-547
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• 2015

In this study, the performance of a low-floor midsize bus under development is predicted through simulations. To predict the vehicle's acceleration, maximum speed, and uphill driving performance, a forward simulator which calculates the vehicle power is developed. Also we verify the forward simulator by comparing simulations and test result for benchmarking vehicle. To predict the fuel consumption, we use a backward simulator for a specified road cycle. However, to predict the fuel consumption using the backward simulation the engine fuel-consumption map is needed. The engine fuel-consumption map extracting data from a similar sized diesel engine is used by re-scaling the maximum torque. As a result, we simulate the vehicle's forward performance with a new engine. Further, we simulated the backward performance to optimize the fuel efficiency and gearshift timing.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.633-645
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• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.