A novel two and a half dimensional method for emissions prediction of a reverse flow combustor

Development efforts to reduce pollutants such as NOx and CO produced by fossil-fuelled systems such as gas turbine engines have become a necessity. The realisation of these efforts in conceptual and detailed design phases before production is very important in terms of reducing the time and costs of the projects. In spite of rapid computer technology development, it is still a difficult and time-consuming process to calculate the minor chemical species such as NOx and CO which are released during the combustion process. In this study, a methodology is proposed to predict the emissions of a reverse flow combustor of a 1000 horsepower turbo-shaft helicopter engine in the design process. In this methodology, the temperature and mass flow values are obtained by computational fluid dynamics (CFD) simulations and the CRN model is constructed by using these values. A MATLAB based tool was developed for chemical reactor network (CRN) modelling. And obtained results were compared with empirical correlations.

• Record URL:
  • https://doi.org/10.1504/IJSA.2018.092913
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20500467
• Supplemental Notes:
  • Copyright © 2018 Inderscience Enterprises Ltd.
• Authors:
  • Varol, Gökhan
  • Sarıkay, Gürkan
  • Tunçer, Onur
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 31-47
• Serial:
  • International Journal of Sustainable Aviation
  • Volume: 4
  • Issue Number: 1
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 2050-0467
  • EISSN: 2050-0475
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=ijsa

Subject/Index Terms

• TRT Terms: Carbon monoxide; Combustion; Combustors; Fluid dynamics; Mathematical models; Nitric oxide; Pollutants; Turbine engines; Vehicle design
• Subject Areas: Aviation; Environment; Vehicles and Equipment;

Filing Info

• Accession Number: 01678999
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 27 2018 2:05PM