• 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.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.2
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• pp.79-86
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• 2012

An economic ship routing means to sail a ship with a goal of minimizing the fuel consumption by utilizing weather forecast information, and various such systems have been recently studied. For a successful economic ship routing system, an efficient algorithm is needed to search an optimal geographical path, and most of the previous systems were approaching to that problem through a minimal static-cost path search algorithm based on the Dijkstra algorithm. To apply that kind of search algorithm, the cost of every edge assigned with the estimated fuel consumption should be constant. However, that assumption is not practical at all considering that the actual fuel consumption is determined by the weather condition when the ship will pass the edge. To overcome such a limitation, we propose a new optimal ship routing system based on a minimal dynamic-cost path search algorithm by properly modifying the Dijkstra algorithm. In addition, we propose a method which efficiently reduces the search space by using the $A^*$ algorithm to decrease the running time. We compared our system with the shortest path-based sailing method over ten testing routes and observed that the former reduced the estimated fuel consumption than the latter by 2.36% on average and the maximum 4.82% with little difference of estimated time of arrival.

• Aerospace Engineering and Technology
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• v.2 no.1
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• pp.1-10
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• 2003

Methodology of predicting steady performance of gas turbine engine from transient test data was explored to develop an economic performance test technique. Discrepancy of transient performance from steady performance was categorized as dynamic, thermal and aerodynamic transient effects. Each effect was mathematically modeled and quantified to provide correction factors for calculating steady performance. The influence of engine inlet/outlet condition change on engine performance was corrected firstly, and then steady performance was predicted from the correction factors. The result was compared with steady performance test data. This correction method showed an acceptable level of precision, 3.68% difference of fuel flow.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.6
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• pp.788-793
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• 2012

This study was focused on the estimations of air pollutants, such as PM(Particulate matters), SOx(Sulfur Oxides), $CO_2$(Carbon diOxides) and NOx(Nitrogen Oxides), from a diesel propulsion engine installed on a naval vessel. Legislative and regulatory actions for exhaust emissions from ships are being strengthened in international communities and national governments to protect human health and the environment. In this context, various technologies have been developed from all of the nations of the world to meet strict standards. These regulations are based on commercial ship applications and according to size, but are not suitable for military naval vessels, which have much different engine operating conditions and hull architectures. Additionally, there is no international emission control system for military ships. Emission factors have been updated for commercial ship types from work at various research institutes; however, it is difficult to develop emission factors for military vessels because of their characteristics. In this paper, exhaust emissions from diesel engines installed on naval vessels under steady navigation condition were estimated with emission inventory methodology applied to ocean going vessels using fuel-based methods and fuel sulfur content analysis.

• 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 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.5
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• pp.62-70
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• 2002

Methodology of predicting steady performance of gas turbine engine from transient test data was explored to develop an economic performance test technique. Discrepancy of transient performance from steady performance was categorized as dynamic, thermal and aerodynamic transient effects. Each effect was mathematically modeled and quantified to provide correction factors for calculating steady performance. Engine performance tests were conducted at Altitude Engine Test Facility of KARI. The influence of engine inlet/outlet condition change on engine performance was corrected firstly, and then steady performance was predicted from the correction factors. The result was compared with steady performance test data. This correction method showed an acceptable level of precision, 3.68% difference of fuel flow.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.1-6
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• 2019

The objective of this study was to investigate performance characteristics of thermal management system(TMS) in a fuel cell electric vehicle with 100kW Fuel Cell(FC) system. In order to build up analytic modelling for TMS, each component was installed and tested under various operating conditions, such as water pump, radiator, 3-Way valve, COD heater, and FC stack etc. and as the results of them, correlations reflecting component's characteristics with flow rate, air velocity were developed. Developed analytic modelling was carried out under various operating conditions on the road. To verify modelling's accuracy, after prediction for optimum coolant flow rate was fulfilled under certain operating conditions, such as FC system, water pump speed, opening of 3-way valve, and pipe resistance, analytic and experimental values were compared and good agreement was shown. In order to predict cold-start operating performance for analytic modelling, coolant temperature variation was analyzed with $-20^{\circ}C$ ambient temperature and duration was predicted to rise in optimum temperature for FC. Because there is appropriate temperature difference between inlet and outlet of FC stack to operate FC system properly, related analysis was performed with respect to power consumption for TMS and heat rejection rate and performance map was depicted along with FC operating conditions.

• International Journal of Highway Engineering
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• v.19 no.4
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.