• Korean Journal of Agricultural Science
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• v.43 no.1
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• pp.116-126
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• 2016

The objective of this study is to predict the fuel efficiency of an agricultural tractor. The fuel efficiency of the tractor during rotary tillage was predicted using numerical modeling. A numerical model was developed using Simulation X. Based on tractor power flow, numerical modeling consisted of an engine, transmission, PTO (power take off), and hydraulics. The specifications of major components utilized in the numerical model were the same as those of a 71 kW tractor (field test tractor). The load that was inputted for fuel efficiency prediction into the simulation model was obtained from a field test. Fuel efficiency predictions were conducted by comparing field test results and simulation results. In addition, it was performed by dividing the rotary tillage and steering section. Main results are as follows: first, t-values of engine torque were measured to be 0.31 in the rotary tillage and 0.92 in the steering section. Second, t-values of fuel consumption were measured to be 0.51 and 5.41 in the rotary tillage and the steering section, respectively. Finally, t-values of fuel efficiency were measured to be 1.72 and 40 in the rotary tillage and the steering section, respectively. The results show no significant differences with t-values of less than 5% in the rotary tillage. But, it shows significant differences in the steering section. Therefore, simulation for accurate fuel efficiency prediction requires a suitable algorithm or detailed design of the simulation model in the steering section.

• 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 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 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 the Korean Society for Aviation and Aeronautics
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• v.29 no.3
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• pp.44-51
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• 2021

PIP is an abbreviation of 'Performance Improvement Package', which is a package that can improve performance by applying some design changes to existing aircraft. Boeing provides PIP applicable to B777-200, and Airbus provides PIP applicable to A350-900 as standard. PIP provided by Boeing and Airbus is a separate task, but it is expected to reduce fuel consumption by reducing drag through aerodynamic improvements. The PIP applied to the A350-900 includes work such as increasing Winglet Height and re-twisting Outboard Wing. This study is to verify the effect of PIP application of the A350-900 aircraft and use it as basic data for economic analysis. The aerodynamic improvement studies and expected effects of the PIP application were examined, and the actual flight data of the PIP-applied and the non-applied aircraft were compared to confirm the PIP application effect. This paper provides empirical results for the aviation industry on the PIP application efficiency as a method of improving fuel efficiency and reducing carbon emission.

• 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.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.4
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• pp.98-103
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• 2019

Integrity prediction of the operation equipment of naval vessels is essential to maintain the efficiency of the operation performance in urgent situations. Recently, the integrated condition assessment system(ICAS) was introduced and maintained to improve operational performance. This technology is related with ICAS, and it must be localized through extensive research. In this paper, we present the results of applying the data-driven model to the predictability methods of diesel generators, which are naval vessel operation equipment.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.16-22
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• 2004

Dimethyl Ether (DME) has been considered as one of the most attractive alternative fuels for a compression ignition engine. The major advantage of DME-fuelled engine is a great potential for soot-free combustion without sacrificing an inherent high thermal efficiency of diesel engine, despite a necessity for modification of the conventional fuel injection system. An experimental study on DME and conventional diesel sprays was conducted by employing a common-rail type fuel injection system with a 5-holes sac type nozzle, including a constant volume vessel pressurized with nitrogen gas. The injection rates of DME and diesel fuel were recorded with the Bosch type injection rate meter. The injection delay of DME was shorter than that of diesel fuel. The measured injection rates of DME and diesel fuel were correlated with spray penetrations. The prediction method of spray penetration was established using the injection rates, which was verified with the Dent's penetration model and found to agree well for DME case.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.74-81
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• 2013

Due to its accuracy and efficiency, reduced kinetic mechanism of diesel surrogate is widely used as fuel model when applying 3-D diesel engine simulation. But for the well-developed prediction of diesel surrogate reduced kinetic mechanism, it is important to know some meaningful factors which affect to ignition delay time. Meanwhile, ignition delay time consists of two parts. One is the chemical ignition delay time related with the chemical reaction, and the other is the physical ignition delay time which is affected by physical behavior of the fuel droplet. Especially for chemical ignition delay time, chemical properties of each fuel were studied for a long time, but researches on their mixtures have not been done widely. So it is necessary to understand the chemical characteristics of their mixtures for more precise and detailed modeling of surrogate diesel oil. And it shows same ignition trend of paraffin mixture with those of single component, and shorter ignition delay at low/high initial temperature when mixing paraffin and toluene.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.758_759
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• 2009

In the design and analysis of electric machines the precise calculation of iron loss has incredible significance. It is tough to foresee iron losses precisely in machines due to distribution of non sinusoidal flux density. It is necessary to approximate the iron losses for the precise computation of efficiency. This paper presents a novel approach for the prediction of iron losses of the brushless dc (BLDC) motors by considering the effects of minor hysteresis loops in the simplified model. The novel iron loss model results are compared with the simplified model and with finite element method (FEM).