JOURNAL ARTICLE

Nonlinear global flame response behaviors for triggering of combustion instabilities.

  • Published In: International Journal of Spray & Combustion Dynamics, 2024, v. 16, n. 4. P. 290 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Acharya, Vishal 3 of 3

Abstract

This article focuses on the nonlinear global heat release characteristics that enable a linearly stable combustor to exhibit bi-stable behavior, known as "triggering," where a sufficiently large disturbance can destabilize the system. Using a fifth-order expansion of the global unsteady heat release rate in disturbance amplitude, the study derives analytical constraints on the phase relationships between third- and fifth-order nonlinear flame responses under assumptions of linear damping and either zero or non-zero linear flame response. These constraints define narrow parameter regions where triggering can occur, with the presence of non-zero linear response broadening the triggering parameter space. The methodology includes a physics-based fitting strategy applied to experimental datasets, validating the theoretical predictions, and is further illustrated through a nonlinear model of an axisymmetric premixed flame, identifying flame angles and frequencies conducive to triggering. The work highlights the importance of nonlinear flame dynamics in combustion instability and provides a framework for screening experimental data to assess triggering tendencies.

Additional Information

  • Source:International Journal of Spray & Combustion Dynamics. 2024/12, Vol. 16, Issue 4, p290
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:1756-8277
  • DOI:10.1177/17568277241297627
  • Accession Number:181232597
  • Copyright Statement:Copyright of International Journal of Spray & Combustion Dynamics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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