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Technical Brief

Dynamic Data-Driven Combustor Design for Mitigation of Thermoacoustic Instabilities

[+] Author and Article Information
Pritthi Chattopadhyay

Fellow ASME, Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802, USA
pritthichatterjee@gmail.com

Sudeepta Mondal

Fellow ASME, Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802, USA
sbm5423@psu.edu

Asok Ray

Fellow ASME, Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802, USA
axr2@psu.edu

Achintya Mukhopadhyay

Mechanical Engineering Department, Jadavpur University, Kolkata 700 032, India
achintya.mukho@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4040210 History: Received February 08, 2018; Revised April 23, 2018

Abstract

A critical issue in design and operation of combustors in gas turbine engines is mitigation of thermoacoustic instabilities, because such instabilities may cause severe damage to the mechanical structure of the combustor. Hence, it is important to quantitatively assimilate the knowledge of the system conditions that would potentially lead to these instabilities. This technical brief proposes a dynamic data-driven technique for design of combustion systems by taking stability of pressure oscillations into consideration. Given appropriate experimental data at selected operating conditions, the proposed design methodology determines a mapping from a set of operating conditions to a set of quantified stability conditions for pressure oscillations. This mapping is then used as an extrapolation tool for predicting the system stability for other conditions for which experiments have not been conducted. Salient properties of the proposed design methodology are: 1. It is dynamic in the sense that no fixed model structure needs to be assumed; and a suboptimal model (under specified user-selected constraints) is identified for each operating condition. An information-theoretic measure is then used for performance comparison among different models of varying structures and/or parameters. 2. It quantifies a (statistical) confidence level in the estimate of system stability for an unobserved operating condition by using a Bayesian nonparametric technique. The proposed design methodology has been validated with experimental data of pressure time-series, acquired from a laboratory-scale lean-premixed swirl-stabilized combustor.

Copyright (c) 2018 by ASME
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