Thermoacoustic instability in a reacting flow field is characterized by high amplitude pressure fluctuations driven by a positive coupling between the unsteady heat release rate and the acoustic field of the combustor. In a turbulent flow, the transition of a thermoacoustic system from a state of chaos to periodic oscillations occurs via a state of intermittency. During the transition to periodic oscillations, the unsteady heat release rate synchronizes with the acoustic pressure fluctuations. Thermoacoustic systems are traditionally modeled by coupling the model for the heat source and the acoustic subsystem, each estimated independently. The response of the unsteady heat source, i.e., the flame, to acoustic fluctuations is characterized by introducing unsteady external forcing. The forced response of the flame need not be the same in the presence of an acoustic field due to their nonlinear coupling. Instead of characterizing individual subsystems, we introduce a neural ordinary differential equation (neural ODE) framework to model the thermoacoustic system as a whole. The neural ODE model for the thermoacoustic system uses time series of the heat release rate and the pressure fluctuations, measured simultaneously without introducing any external perturbations, to model their coupled interaction. Furthermore, we use the parameters of neural ODE to define an anomaly measure that represents the proximity of system dynamics to limit cycle oscillations and thus provide an early warning signal for the onset of thermoacoustic instability.

1.
M. P.
Juniper
and
R. I.
Sujith
, “
Sensitivity and nonlinearity of thermoacoustic oscillations
,”
Annu. Rev. Fluid Mech.
50
,
661
689
(
2018
).
2.
G. A.
Flandro
and
J.
Majdalani
, “
Aeroacoustic instability in rockets
,”
AIAA J.
41
,
485
497
(
2003
).
3.
M. H.
Hansen
, “
Aeroelastic instability problems for wind turbines
,”
Wind Energy
10
,
551
577
(
2007
).
4.
L. F.
Richardson
, “
Atmospheric diffusion shown on a distance-neighbour graph
,”
Proc. R. Soc. Lond. Ser. A
110
,
709
737
(
1926
).
5.
R. H.
Kraichnan
, “
Inertial ranges in two-dimensional turbulence
,”
Phys. Fluids
10
,
1417
1423
(
1967
).
6.
K.
Smith
,
H.
Brighton
, and
S.
Kirby
, “
Complex systems in language evolution: The cultural emergence of compositional structure
,”
Adv. Complex Syst.
06
,
537
558
(
2003
).
7.
T. C.
Lieuwen
and
V.
Yang
,
AIAA Prog. Astronaut. Aeronaut.
210
,
3–26
(
2005
).
8.
S. C.
Fisher
and
S. A.
Rahman
, Remembering the Giants: Apollo Rocket Propulsion Development, NASA Monographs in Aerospace History (NASA, 2009).
9.
R. I.
Sujith
and
V. R.
Unni
, “
Dynamical systems and complex systems theory to study unsteady combustion
,”
Proc. Combust. Inst.
38
,
3445
3462
(
2021
).
10.
V.
Nair
,
G.
Thampi
,
S.
Karuppusamy
,
S.
Gopalan
, and
R. I.
Sujith
, “
Loss of chaos in combustion noise as a precursor of impending combustion instability
,”
Int. J. Spray Combust. Dyn.
5
,
273
290
(
2013
).
11.
J.
Tony
,
E. A.
Gopalakrishnan
,
E.
Sreelekha
, and
R. I.
Sujith
, “
Detecting deterministic nature of pressure measurements from a turbulent combustor
,”
Phys. Rev. E
92
,
062902
(
2015
).
12.
V.
Nair
and
R. I.
Sujith
, “
Multifractality in combustion noise: Predicting an impending combustion instability
,”
J. Fluid Mech.
747
,
635
655
(
2014
).
13.
J.
Venkatramani
,
V.
Nair
,
R. I.
Sujith
,
S.
Gupta
, and
S.
Sarkar
, “
Multi-fractality in aeroelastic response as a precursor to flutter
,”
J. Sound Vib.
386
,
390
406
(
2017
).
14.
H.
Kobayashi
,
H.
Gotoda
,
S.
Tachibana
, and
S.
Yoshida
, “
Detection of frequency-mode-shift during thermoacoustic combustion oscillations in a staged aircraft engine model combustor
,”
J. Appl. Phys.
122
,
224904
(
2017
).
15.
L.
Lacasa
,
B.
Luque
,
F.
Ballesteros
,
J.
Luque
, and
J. C.
Nuño
, “
From time series to complex networks: The visibility graph
,”
Proc. Natl. Acad. Sci. U.S.A.
105
,
4972
4975
(
2008
).
16.
M.
Murugesan
and
R. I.
Sujith
, “
Combustion noise is scale-free: Transition from scale-free to order at the onset of thermoacoustic instability
,”
J. Fluid Mech.
772
,
225
245
(
2015
).
17.
S.
Murayama
,
H.
Kinugawa
,
I. T.
Tokuda
, and
H.
Gotoda
, “
Characterization and detection of thermoacoustic combustion oscillations based on statistical complexity and complex-network theory
,”
Phys. Rev. E
97
,
022223
(
2018
).
18.
V.
Nair
,
G.
Thampi
, and
R. I.
Sujith
, “
Intermittency route to thermoacoustic instability in turbulent combustors
,”
J. Fluid Mech.
756
,
470
487
(
2014
).
19.
V.
Nair
and
R. I.
Sujith
, “
Precursors to self-sustained oscillations in aeroacoustic systems
,”
Int. J. Aeroacoust.
15
,
312
323
(
2016
).
20.
J.
Venkatramani
,
V.
Nair
,
R. I.
Sujith
,
S.
Gupta
, and
S.
Sarkar
, “
Precursors to flutter instability by an intermittency route: A model free approach
,”
J. Fluids Struct.
61
,
376
391
(
2016
).
21.
S. A.
Pawar
,
A.
Seshadri
,
V. R.
Unni
, and
R. I.
Sujith
, “
Thermoacoustic instability as mutual synchronization between the acoustic field of the confinement and turbulent reactive flow
,”
J. Fluid Mech.
827
,
664
693
(
2017
).
22.
S.
Mondal
,
V. R.
Unni
, and
R. I.
Sujith
, “
Onset of thermoacoustic instability in turbulent combustors: An emergence of synchronized periodicity through formation of chimera-like states
,”
J. Fluid Mech.
811
,
659
681
(
2017
).
23.
S. L.
Brunton
,
B. R.
Noack
, and
P.
Koumoutsakos
, “
Machine learning for fluid mechanics
,”
Annu. Rev. Fluid Mech.
52
,
477
508
(
2020
).
24.
C. S.
Daw
,
C. E. A.
Finney
, and
E. R.
Tracy
, “
A review of symbolic analysis of experimental data
,”
Rev. Sci. Instrum.
74
,
915
930
(
2003
).
25.
S.
Chakraborty
,
S.
Sarkar
,
A.
Ray
, and
S.
Phoha
, “Symbolic identification for anomaly detection in aircraft gas turbine engines,” in Proceedings of the 2010 American Control Conference (IEEE, 2010).
26.
V. R.
Unni
,
A.
Mukhopadhyay
, and
R. I.
Sujith
, “
Online detection of impending instability in a combustion system using tools from symbolic time series analysis
,”
Int. J. Spray Combust. Dyn.
7
,
243
256
(
2015
).
27.
S.
Sarkar
,
K. G.
Lore
, and
S.
Sarkar
, “Early detection of combustion instability by neural-symbolic analysis on hi-speed video,” in COCO’15: Proceedings of the 2015th International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches (CEUR-WS.org, 2015), Vol. 1583.
28.
S. L.
Brunton
and
B. R.
Noack
, “
Closed-loop turbulence control: Progress and challenges
,”
Appl. Mech. Rev.
67
,
050801
(
2015
).
29.
C. W.
Rowley
and
S. T.
Dawson
, “
Model reduction for flow analysis and control
,”
Annu. Rev. Fluid Mech.
49
,
387
417
(
2017
).
30.
R. T. Q.
Chen
,
Y.
Rubanova
,
J.
Bettencourt
, and
D. K.
Duvenaud
, “
Neural ordinary differential equations
,”
Adv. Neural Inf. Process. Syst.
31
,
6571
6583
(
2018
).
31.
T.
Szandała
, “Review and comparison of commonly used activation functions for deep neural networks,” in Bio-Inspired Neurocomputing (Springer Singapore, 2021).
32.
K.
Janocha
and
W.
Czarnecki
, “
On loss functions for deep neural networks in classification
,”
Schedae Inform.
25
,
49
59
(
2016
).
33.
C. M.
Bishop
,
Pattern Recognition and Machine Learning (Information Science and Statistics)
(
Springer-Verlag
,
2006
).
34.
H. B.
Curry
, “
The method of steepest descent for non-linear minimization problems
,”
Q. Appl. Math.
2
,
258
261
(
1944
).
35.
D. E.
Rumelhart
,
G. E.
Hinton
, and
R. J.
Williams
, “
Learning representations by back-propagating errors
,”
Nature
323
,
533
536
(
1986
).
36.
F.
Takens
, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol. 898 (Springer, Berlin, 1981), pp. 366–381.
37.
H. D. I.
Abarbanel
,
R.
Brown
,
J. J.
Sidorowich
, and
L. S.
Tsimring
, “
The analysis of observed chaotic data in physical systems
,”
Rev. Mod. Phys.
65
,
1331
1392
(
1993
).
38.
H.
Kantz
and
T.
Schreiber
,
Nonlinear Time Series Analysis
, 2nd ed. (
Cambridge University Press
,
2003
).
39.
H.
Kurt
,
S.
Maxwell
, and
H.
White
, “
Multilayer feedforward networks are universal approximators
,”
Neural Netw.
2
,
359
366
(
1989
).
40.
T.
Schuller
,
T.
Poinsot
, and
S.
Candel
, “
Dynamics and control of premixed combustion systems based on flame transfer and describing functions
,”
J. Fluid Mech.
894
,
P1
(
2020
).
41.
V. R.
Unni
and
R. I.
Sujith
, “
Multifractal characteristics of combustor dynamics close to lean blowout
,”
J. Fluid Mech.
784
,
30
50
(
2015
).
42.
G. W.
Ernst Hairer
and
S. P.
Nørsett
, Solving Ordinary Differential Equations I: Nonstiff Problems, Springer Series in Computational Mathematics (Springer-Verlag, Berlin, 1993).
43.
R. G.
Gallager
,
Information Theory and Reliable Communication
(
Wiley
,
1968
).
44.
M. S.
Howe
, “
Contributions to the theory of aerodynamic sound, with application to excess jet noise and the theory of the flute
,”
J. Fluid Mech.
71
,
625
673
(
1975
).
45.
P.
Kriesels
,
M.
Peters
,
A.
Hirschberg
,
A.
Wijnands
,
A.
Iafrati
,
G.
Riccardi
,
R.
Piva
, and
J.
Bruggeman
, “
High amplitude vortex-induced pulsations in a gas transport system
,”
J. Sound Vib.
184
,
343
368
(
1995
).
46.
D.
Tonon
,
A.
Hirschberg
,
J.
Golliard
, and
S.
Ziada
, “
Aeroacoustics of pipe systems with closed branches
,”
Int. J. Aeroacoust.
10
,
201
275
(
2011
).
47.
I.
Pavithran
,
V. R.
Unni
,
A. J.
Varghese
,
R. I.
Sujith
,
A.
Saha
,
N.
Marwan
, and
J.
Kurths
, “
Universality in the emergence of oscillatory instabilities in turbulent flows
,”
EPL
129
,
24004
(
2020
).
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