Research Papers: D3 Applications and Case Studies

Dynamic Data-Driven Design of Lean Premixed Combustors for Thermoacoustically Stable Operations

[+] Author and Article Information
Pritthi Chattopadhyay

Department of Mechanical and
Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: pritthichatterjee@gmail.com

Sudeepta Mondal

Department of Mechanical and
Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: sudeepta979@gmail.com

Chandrachur Bhattacharya

Department of Mechanical and
Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: chandrachur.bhattacharya@gmail.com

Achintya Mukhopadhyay

Department of Mechanical Engineering,
Jadavpur University,
Kolkata 700 032, India
e-mail: achintya.mukho@gmail.com

Asok Ray

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

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 20, 2017; final manuscript received June 19, 2017; published online October 2, 2017. Assoc. Editor: Yan Wang.

J. Mech. Des 139(11), 111419 (Oct 02, 2017) (10 pages) Paper No: MD-17-1154; doi: 10.1115/1.4037307 History: Received February 20, 2017; Revised June 19, 2017

Prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems. Sustained high-amplitude pressure and temperature oscillations may cause stresses in structural components of the combustor, leading to thermomechanical damage. Therefore, the design of combustion systems must take into account the dynamic characteristics of thermoacoustic instabilities in the combustor. From this perspective, there needs to be a procedure, in the design process, to recognize the operating conditions (or parameters) that could lead to such thermoacoustic instabilities. However, often the available experimental data are limited and may not provide a complete map of the stability region(s) over the entire range of operations. To address this issue, a Bayesian nonparametric method has been adopted in this paper. By making use of limited experimental data, the proposed design method determines a mapping from a set of operating conditions to that of stability regions in the combustion system. This map is designed to be capable of (i) predicting the system response of the combustor at operating conditions at which experimental data are unavailable and (ii) statistically quantifying the uncertainties in the estimated parameters. With the ensemble of information thus gained about the system response at different operating points, the key design parameters of the combustor system can be identified; such a design would be statistically significant for satisfying the system specifications. The proposed method has been validated with experimental data of pressure time-series from a laboratory-scale lean-premixed swirl-stabilized combustor apparatus.

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Fig. 1

Schematic diagram of the combustion apparatus

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Fig. 2

Examples of pressure signals in the combustor: (a) stable signal oscillations and (b) unstable signal oscillations

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Fig. 3

Flowchart of the combustor design algorithm

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Fig. 4

Effects of time series length on Prms profiles: (a) profile of Prms for a typical stable pressure signal and (b) profile of Prms for a typical unstable pressure signal

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Fig. 5

Feature divergence Fdiv predicted by GP regression algorithm for inlet velocity = 40 m/s and ϕ = 0.55: (a) Fdiv using 2 s of data, (b) Fdiv using 4 s of data, (c) Fdiv using 6 s of data, and (d) Fdiv using 8 s of data

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Fig. 6

Feature divergence Fdiv predicted by GP regression algorithm for inlet velocity = 25 m/s and ϕ = 0.525: (a) Fdiv using 2 s of data, (b) Fdiv using 4 s of data, (c) Fdiv using 6 s of data, and (d) Fdiv using 8 s of data

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Fig. 7

Sensitivity comparison of PFSA and Prms features: (a) PFSA and Prms feature divergence for 2 s data and (b) PFSA and Prms feature divergence for 8 s data



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