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Hakan Aydin

Publications -  30
Citations -  850

Hakan Aydin is an academic researcher. The author has contributed to research in topics: Exergy & Exergy efficiency. The author has an hindex of 14, co-authored 25 publications receiving 667 citations.

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Exergetic sustainability analysis of LM6000 gas turbine power plant with steam cycle

Hakan Aydin
- 01 Aug 2013 - 
TL;DR: In this article, a comprehensive exergy analysis of GTE is carried out then the exergetic sustainability indicators are calculated for two power plant configuration, case A for LM6000 GTE based power plant, case B for LM 6000 GTE-based power plant with steam turbine cycle.
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Exergetic Sustainability Indicators as a Tool in Commercial Aircraft: A Case Study for a Turbofan Engine

TL;DR: In this article, the exergetic sustainability indicators of a medium-range commercial aircraft engine for constant reference environment and ground running conditions were investigated, based on the second law of the thermodynamics.
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Exergo-sustainability indicators of a turboprop aircraft for the phases of a flight

TL;DR: In this article, the authors presented exergetic sustainability indicators of the turboprop engine for eight flight phases and analyzed and discussed in detail for better understanding of sustainability performances of a turbo-prop aircraft.
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Environmental impact assessment of a turboprop engine with the aid of exergy

TL;DR: In this paper, a turboprop engine used in regional aircrafts that produces 1948 shp and 640 N.m torque is examined using exergo-environmental method, and the results show compressor, combustion chamber, gas generator turbine, power turbine and exhaust nozzle create 9%, 69%, 13, 7, 7%, 2% of total environmental impact of the engine, respectively.
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Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks

TL;DR: In this paper, a comprehensive dynamic modeling of turboprop engine components plant is accomplished using hybrid GA (genetic algorithm) ANN (artificial neural networks) strategy, which takes into account five independent engine variables (i.e., torque, power, gas generator speed, engine mass air flow and fuel flow).