Startseite Numerical analysis of pollutant formation and exhaust emission in a short annular combustor under part load operation
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Numerical analysis of pollutant formation and exhaust emission in a short annular combustor under part load operation

  • Ranjan K. Mishra EMAIL logo
Veröffentlicht/Copyright: 13. März 2025
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Abstract

Emission characteristic of a single annular gas turbine combustor has been studied at engine part-load conditions from idle to max power setting. Under fuel-rich condition. The temperature field and pollutant formation at different power settings are studied in detail. The equivalence ratio varies from 0.35 corresponding idle power setting to maximum 1.1 at max power setting at take-off condition. Concentration of major pollutant species at combustor exit such as NOx, CO, CO2, soot, unburned hydrocarbons and water vapor are analyzed for varying inlet conditions and primary zone equivalence ratios at different power settings using model in ANSYS CFX. The pollutant concentrations are found to be increased at combustor exit with increase in power setting as equivalence ratio and flame temperature increase. The numerical predictions at low regime of power setting are validated against gas sampling data obtained from actual combustor testing in aerothermal test facility. The numerical analysis based on satisfactory validation with limited experimental data is very reasonable and provides input for prediction of pollutant emissions of the intended derivative engine during the landing-takeoff (LTO) cycle prior to engine test.


Corresponding author: Ranjan K. Mishra, Department of Aeronautical Engineering, MVJ College of Engineering, Bangalore, 560067, India, E-mail:

Acknowledgments

The author is very grateful to the Regional Director, Regional Center for Military Airworthiness, Koraput, and Engineers of Aero Engine R&D Center, Bangalore for their valuable suggestions and supports during this study.

  1. Research ethics: Ethical conduct has been followed.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors accepts responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: Not applicable.

  6. Research funding: None declared.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2025-02-02
Accepted: 2025-02-27
Published Online: 2025-03-13
Published in Print: 2025-08-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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