Technical Brief

Dynamic Data-Driven Combustor Design for Mitigation of Thermoacoustic Instabilities

[+] Author and Article Information
Pritthi Chattopadhyay

Mechanical Engineering Department,
Pennsylvania State University,
University Park, PA 16802

Sudeepta Mondal

Mechanical Engineering Department,
Pennsylvania State University,
University Park, PA 16802

Asok Ray

Fellow ASME
Mechanical Engineering Department,
Pennsylvania State University,
University Park, PA 16802
e-mail: axr2@psu.edu

Achintya Mukhopadhyay

Mechanical Engineering Department,
Jadavpur University,
Kolkata 700 032, India

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received February 8, 2018; final manuscript received April 23, 2018; published online September 7, 2018. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 141(1), 014501 (Sep 07, 2018) (7 pages) Paper No: DS-18-1068; doi: 10.1115/1.4040210 History: Received February 08, 2018; Revised April 23, 2018

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 and (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.

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Fig. 1

Schematic diagram of the combustion apparatus

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Fig. 2

Mean error in prediction over 20 runs

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Fig. 3

Mean uncertainty in estimation over 20 runs

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Fig. 4

Standard deviation of mean error over 20 runs

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Fig. 5

Standard deviation of uncertainty over 20 runs

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Fig. 6

Profile of Prms plot for a typical subset

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Fig. 7

Entropy rate for the same subset as in Fig. 6

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Fig. 8

Correlations of Prms divergence with D = 1 and D≥1 feature divergence for |Σ| = 3, Nmax = 20, η = 0.02

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Fig. 9

Correlations of Prms divergence with D = 1 and D≥1 feature divergences for |Σ| = 5, Nmax = 20, η = 0.02



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