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    Exergetic performance analysis and comparison of oxy-combustion and conventional gas turbine power cycles
    (Yildiz Technical Univ, 2023) Ozsari, Ibrahim; Ust, Yasin; Karakurt, Asim Sinan
    Oxy-combustion technologies are green energy systems and an impressive solution to climate change and global warming. This study presents a detailed exergy analysis obtained for oxy-combustion power systems in comparison with a conventional gas turbine power system. The results include net power, overal thermal efficiency, exergy destruction, exergy efficiency, power density, exergetic performance coefficient (EPC), ecological performance coefficient (ECOP), effective ecological power density (EFECPOD), and mean exergy density (MED), and cost of power density (COPD), which are calculated as functions of pressure and oxygen ratios. The conventional gas turbine power system obtained a pressure ratio for maximum net power of 20.8. Similarly, oxy-combustion power cycles at 26%, 28%, and 30% oxygen ratios have respective pressure ratios for maximum net power of 23.3, 27.4, and 29.7. Results from 24%-30% oxygen ratios are displayed to show the reactant oxygen's effect on the oxy-combustion power cycles. Increases in the pressure ratio show decreases in the total exergy destruction in both the conventional gas turbine power system and the oxy-combustion power systems. Meanwhile, increases in the pressure ratio show increases in the total efficiency, power density, exergy efficiency, EPC, EFFECPOD, and MED in both the conventional gas turbine and the oxy-combustion power systems. In addition, increases in the oxygen ratio in the oxy-combustion power systems show different characteristics for these parameters based on the pressure ratio of the cycle. In terms of COPD, conventional gas turbine power systems are more advantageous than oxy-combustion power systems. Optimum COPD is obtained at a pressure ratio of 25.6.
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    Predicting tanker main engine power using regression analysis and artificial neural networks
    (Yildiz Technical Univ, 2023) Gunes, Umit; Bashan, Veysi; Ozsari, Ibrahim; Karakurt, Asim Sinan
    The purpose-oriented design and planning of ships is maintained throughout production. Outer form of ship equipment starts with the steel construction process. The outer body production process moves ahead with painting, quality control tests, and bureaucratic procedures. In accordance with all these form and block operations, choosing a main engine suitable for all other technical parameters is vital, especially regarding ship speed and the amount of cargo it will carry. As a result, estimating main engine power is attempted with the help of artificial neural network (ANN) and regression analyses by considering a ship's technical parameters (e.g., draught, depth, deadweight tonnage [DWT], gross tonnage [GT], and engine power). This study conducts regression and ANN analyses over 836 tanker ships from the Marine Traffic database to predict main engine power using input parameters (deadweight (DWT), Length (L), Breadth (B), and gross ton (GT) values). The regression analyses show Model7 to perform the best approximation with a determination value = 0.827 usable for estimating main engine power. After all the examinations, a very accomplished result of 0.98047 was additionally obtained from the ANN analysis. The study makes beneficial and innovative contributions to predicting tankers' required main engine power.