0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Laera, D. , 2015, “ Nonlinear Combustion Instabilities Analysis of Azimuthal Mode in Annular Chamber,” Energy Procedia, 82, pp. 921–928. [CrossRef]
Lieuwen, T. C. , and Yang, V. , 2005, Combustion Instabilities in Gas Turbine Engines: Operational Experience, Fundamental Mechanisms, and Modeling, American Institute of Aeronautics and Astronautics, Reston, VA.
Lefebvre, A. H. , 1999, Gas Turbine Combustion, 2nd ed., Taylor & Francis, Philadelphia, PA. [PubMed] [PubMed]
Mattingly, J. , Heiser, W. , and Pratt, D. , 2002, Aircraft Engine Design (AIAA Education Series), 2nd ed., American Institute of Aeronautics and Astronautics, Reston, VA.
O'Connor, J. , Acharya, V. , and Lieuwen, T. , 2015, “ Transverse Combustion Instabilities: Acoustic, Fluid Mechanic, and Flame Processes,” Prog. Energy Combust. Sci., 49, pp. 1–39. [CrossRef]
Zahn, M. , Schulze, M. , Hirsch, C. , and Sattelmayer, T. , 2016, “ Impact of Quarter Wave Tube Arrangement on Damping of Azimuthal Modes,” ASME Paper No. GT2016-56450.
Lei, L. , Zhihui, G. , Chengyu, Z. , and Xiaofeng, S. , 2010, “ A Passive Method to Control Combustion Instabilities With Perforated Liner,” Chin. J. Aeronaut., 23(6), pp. 623–630. [CrossRef]
Ćosić, B. , Bobusch, B. C. , Moeck, J. P. , and Paschereit, C. O. , 2011, “ Open-Loop Control of Combustion Instabilities and the Role of the Flame Response to Two-Frequency Forcing,” ASME Paper No. GT2011-46503.
Jones, C. , Lee, J. , and Santavicca, D. , 1999, “ Closed-Loop Active Control of Combustion Instabilities Using Subharmonic Secondary Fuel Injection,” J. Propul. Power, 15(4), pp. 584–590. [CrossRef]
Paschereit, C. O. , Gutmark, E. J. , and Weisenstein, W. , 1998, “ Control of Thermoacoustic Instabilities and Emissions in an Industrial Type Gas-Turbine Combustor,” Symp. Combust., 27(2), pp. 1817–1824. [CrossRef]
Bauerheim, M. , Parmentier, J.-F. , Salas, P. , Nicoud, F. , and Poinsot, T. , 2014, “ An Analytical Model for Azimuthal Thermo-Acoustic Modes in Annular Chamber Fed by an Annular Plenum,” Combust. Flame, 161(5), pp. 1374–1389. [CrossRef]
Bourgouin, J.-F. , Durox, D. , Moeck, J. P. , Schuller, T. , and Candel, S. , 2015, “ Characterization and Modeling of a Spinning Thermoacoustic Instability in an Annular Combustor Equipped With Multiple Matrix Injectors,” ASME J. Eng. Gas Turbines Power, 137(2), p. 021503. [CrossRef]
Innocenti, A. , Andreini, A. , Facchini, B. , and Cerutti, M. , 2016, “ Numerical Analysis of the Dynamic Flame Response and Thermo-Acoustic Stability of a Full-Annular Lean Partially-Premixed Combustor,” ASME Paper No. GT2016-57182.
Sarkar, S. , Chakravarthy, S. R. , Ramanan, V. , and Ray, A. , 2016, “ Dynamic Data-Driven Prediction of Instability in a Swirl-Stabilized Combustor,” Int. J. Spray Combust. Dyn., 8(4), pp. 235–253. [CrossRef]
Sen, S. , Sarkar, S. , Chaudhari, R. R. , Mukhopadhyay, A. , and Ray, A. , 2017, “ Lean Blowout (LBO) Prediction Through Symbolic Time Series Analysis,” Combustion for Power Generation and Transportation: Technology, Challenges and Prospects, A. K. Agarwal , S. De , A. Pandey , and A. P. Singh , eds., Springer, Singapore, pp. 153–167. [CrossRef]
Darema, F. , 2004, “ Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements,” International Conference on Computational Science (ICCS), Kraków, Poland, June 6–9, pp. 662–669.
Daw, C. , Finney, C. , and Tracy, E. , 2003, “ A Review of Symbolic Analysis of Experimental Data,” Rev. Sci. Instrum., 74(2), pp. 915–930. [CrossRef]
Hauser, M. , Li, Y. , Li, J. , and Ray, A. , 2016, “ Real-Time Combustion State Identification Via Image Processing: A Dynamic Data-Driven Approach,” American Control Conference (ACC), Boston, MA, July 6–8, pp. 3316–3321.
Beim Graben, P. , 2001, “ Estimating and Improving the Signal-to-Noise Ratio of Time Series by Symbolic Dynamics,” Phys. Rev. E, 64(5), p. 051104. [CrossRef]
Kim, K. T. , Lee, J. G. , Quay, B. D. , and Santavicca, D. A. , 2010, “ Response of Partially Premixed Flames to Acoustic Velocity and Equivalence Ratio Perturbations,” Combust. Flame, 157(9), pp. 1731–1744. [CrossRef]
Sipser, M. , 2012, Introduction to the Theory of Computation, Cengage Learning, Boston, MA.
Lind, D. , and Marcus, B. , 1995, An Introduction to Symbolic Dynamics and Coding, Cambridge University Press, Cambridge, UK. [CrossRef]
Ray, A. , 2004, “ Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection,” Signal Process., 84(7), pp. 1115–1130. [CrossRef]
Mukherjee, K. , and Ray, A. , 2014, “ State Splitting and Merging in Probabilistic Finite State Automata for Signal Representation and Analysis,” Signal Process., 104, pp. 105–119. [CrossRef]
Bahrampour, S. , Ray, A. , Sarkar, S. , Damarla, T. , and Nasrabadi, N. M. , 2013, “ Performance Comparison of Feature Extraction Algorithms for Target Detection and Classification,” Pattern Recognit. Lett., 34(16), pp. 2126–2134. [CrossRef]
Rajagopalan, V. , and Ray, A. , 2006, “ Symbolic Time Series Analysis Via Wavelet-Based Partitioning,” Signal Process., 86(11), pp. 3309–3320. [CrossRef]
Vidal, E. , Thollard, F. , de la Higuera, C. , Casacuberta, F. , and Carrasco, R. C. , 2005, “ Probabilistic Finite-State Machines—Part I,” IEEE Trans. Pattern Anal. Mach. Intell., 27(7), pp. 1013–1025. [CrossRef] [PubMed]
Mallapragada, G. , Ray, A. , and Jin, X. , 2012, “ Symbolic Dynamic Filtering and Language Measure for Behavior Identification of Mobile Robots,” IEEE Trans. Sys. Man Cybern., Part B, 42(3), pp. 647–659. [CrossRef]
Rao, C. , Ray, A. , Sarkar, S. , and Yasar, M. , 2009, “ Review and Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns,” Signal Image Video Process., 3(2), pp. 101–114. [CrossRef]
Bishop, C. , 2006, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag, New York.
Rasmussen, C. , and Williams, C. , 2005, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), MIT Press, Cambridge, MA.
Cortes, C. , and Vapnik, V. , 1995, “ Support-Vector Networks,” Mach. Learn., 20(3), pp. 273–297.

Figures

Grahic Jump Location
Fig. 1

Schematic diagram of the combustion apparatus

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

Flowchart of the combustor design algorithm

Grahic Jump Location
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

Grahic Jump Location
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

Grahic Jump Location
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

Grahic Jump Location
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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In