Browsing by Author "Sahin, Bekir"

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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.
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.