Browsing by Author "Gurgen, Samet"

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Crankcase Explosion for Two-Stroke Marine Diesel Engine by Using Fault Tree Analysis Method in Fuzzy Environment
(Pergamon-elsevier Science Ltd, 2019) Unver, Bedir; Gurgen, Samet; Sahin, Bekir; Altin, Ismail
The purpose of this study is to investigate crankcase explosion in two-stroke marine diesel engine systematically by using fault tree analysis method in fuzzy environment for system reliability and shipping sustainability. Maritime accidents might cause deadly and costly results in both human lives, commodities, environmental and financial aspects. Crankcase explosion is one of the most dangerous causalities that might possibly damage both ship structure and crew members. In this study, we determine all root causes of crankcase explosion, and calculate the probabilities in a stepwise manner by implementing field expert consultations. As a result, we find that the occurrence probability of the crankcase explosion is 1.249428E-007 which is close to real case. Bearing originated hot spots and personnel failures are also found as the most risky events.
Experimental Investigation on Cyclic Variability, Engine Performance, and Exhaust Emissions in a Diesel Engine Using Alcohol-Diesel Fuel Blends
(Vinca inst Nuclear Sci, 2017) Gurgen, Samet; Unver, Bedir; Altin, Ismail
This paper investigates the impacts of using n-butanol-diesel fuel and ethanol-diesel fuel blends on engine performance, exhaust emission, and cycle-by-cycle variation in a Diesel engine. The engine was operated at two different engine speed and full load condition with pure diesel fuel, 5% and 10% (by vol.) ethanol and n-butanol fuel blends. The coefficient of variation of indicated mean effective pressure was used to evaluate the cyclic variability of n-butanol-diesel fuel and ethanol-diesel fuel blends. The results obtained in this study showed that effective efficiency and brake specific fuel consumption generally increased with the use of the n-butanol-diesel fuel or ethanol-diesel fuel blends with respect to that of the neat diesel fuel. The addition of ethanol or n-butanol to diesel fuel caused a decrement in CO and NOx emissions. Also, the results indicated that cycle-by-cycle variation has an increasing trend with the increase of alcohol-diesel blending ratio for all engine speed. An increase in cyclic variability of alcohol-diesel fuel blends at low engine speed is higher than that of high engine speed.
Forecasting the Baltic Dry Index by Using an Artificial Neural Network Approach
(Tubitak Scientific & Technological Research Council Turkey, 2018) Sahin, Bekir; Gurgen, Samet; Unver, Bedir; Altin, Ismail
The Baltic Dry Index (BDI) is a robust indicator in the shipping sector in terms of global economic activities, future world trade, transport capacity, freight rates, ship demand, ship orders, etc. It is hard to forecast the BDI because of its high volatility and complexity. This paper proposes an artificial neural network (ANN) approach for BDI forecasting. Data from January 2010 to December 2016 are used to forecast the BDI. Three different ANN models are developed: (i) the past weekly observation of the BDI, (ii) the past two weekly observations of the BDI, and (iii) the past weekly observation of the BDI with crude oil price. While the performance parameters of these three models are close to each other, the most consistent model is found to be the second one. Results show that the ANN approach is a significant method for modeling and forecasting the BDI and proving its applicability.
Prediction of Cyclic Variability in a Diesel Engine Fueled With N-Butanol and Diesel Fuel Blends Using Artificial Neural Network
(Pergamon-elsevier Science Ltd, 2018) Gurgen, Samet; Unver, Bedir; Altin, Ismail
In this study, the cyclic variability of a diesel engine using diesel fuel and butanol diesel fuel blends is modeled using an artificial neural network (ANN) method. The engine was operated with ten different engine speeds and full load conditions using six different n-butanol diesel fuel blends. The coefficient of variation (COV) of the indicated mean effective pressure (IMEP), which is a well-accepted evaluation method, was used to assess the cyclic variability for 100 sequential engine cycles. Results indicated that adding n-butanol to diesel fuel caused an increase. Moreover, the COVimep values exhibited a decreasing trend with an increase in the engine speed for each fuel. The experimental results were used to train the ANN. The ANN network was trained with Levenberg - Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms. After training the ANN, it was found that the coefficient of determination (R-2) values were in the range of between 0.737 and 0.9677, the mean-absolute-percentage error (MAPE) values were smaller than 8.7 and the mean-square error values (MSE) were smaller than 0.042. The predictions of the developed ANN model showed reasonable consistency with the experimental results. (C) 2017 Elsevier Ltd. All rights reserved.
Risk Ranking of Maintenance Activities in a Two-Stroke Marine Diesel Engine Via Fuzzy Ahp Method
(Elsevier Sci Ltd, 2021) Unver, Bedir; Altin, Ismail; Gurgen, Samet
Maintenance activities in ship machineries are of vital importance due to economical and operational requirements. Most of ships in commercial activities have two-stroke marine diesel engines (MDEs) in the propulsion systems. Durability of the commercial activities depends on the stable operation of the main engine in the ship propulsion system. This phenomenon shows the importance of maintenance activities in the two-stroke MDEs. In this study, 46 identified maintenance activities in the two-stroke MDEs were investigated via fuzzy analytical hierarchy process (FAHP) method considering risk magnitudes. The results revealed that critical maintenance activities and physical dimensions of the parts have higher risk rankings. It was also achieved from the research that top and lowest risky maintenances are the overhaul of the turbocharger (T1) and dismantling and mounting of safety valve (C5), respectively.