Startseite Performance analysis of a gas turbine engine via intercooling and regeneration- Part 2
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Performance analysis of a gas turbine engine via intercooling and regeneration- Part 2

  • Suhas Poojary , Jaimon D. Quadros EMAIL logo , Prashanth Thalambeti , Hanumanthraya Rangaswamy und Ma Mohin
Veröffentlicht/Copyright: 16. April 2024
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Abstract

The current study aims to amplify the predictive ability of the numerical model developed for a gas turbine engine-based power plants by process of regeneration and intercooling. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. The performance parameters namely, specific power (SP), thermal efficiency (η), and enthalpy based specific fuel consumption (EBSFC) of a Turboprop engine were predicted using thermodynamic parameters namely, pressure ratio (PR), nozzle pressure ratio (NPR), turbine inlet temperature (TIT), for constant regeneration (R), and intercooling (E) efficiencies. The results showed that a high regression result R 2 of 0.9831 and 0.9899 was found for the ANFIS model for η for training and testing, respectively. Also, the ANFIS model resulted in best performance of the performance characteristics when compared to ANN.


Corresponding author: Jaimon D. Quadros, Department of Mechanical Engineering, University of Bolton, RAK Academic Centre, 16038 Ras Al Khaimah, UAE, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Not applicable.

Nomenclature

ANN

artificial neural network

ANFIS

adaptive neuro fuzzy interference system

EBSFC

engine brake specific fuel consumption (kg/h/kW)

I-R

intercooling and regeneration

p a

ambient pressure (N/m2)

M/M 0

flight Mach number

NPR

nozzle pressure ratio

PR

pressure ratio

TIT

turbine inlet temperature (K)

η

thermal efficiency

SP

specific power (kW/kg/s)

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Received: 2023-11-02
Accepted: 2024-03-18
Published Online: 2024-04-16
Published in Print: 2024-12-17

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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