Nicolas Noiray

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Noiray

First Name

Nicolas

ORCID

0000-0003-3362-9721

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09471 - Noiray, Nicolas / Noiray, Nicolas

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Publications 1 - 10 of 22

Feedback control of thermoacoustic instabilities in sequential combustor with nanosecond repetitively pulsed discharges
Dharmaputra, Bayu Adi; Noiray, Nicolas; Shcherbanev, Sergey (2024)
INTER-NOISE and NOISE-CON Congress and Conference Proceedings ~ INTER-NOISE24
Controlling thermoacoustic instabilities is a necessary task for the development and operation of gas turbines. With the increasing proportion of renewable energy sources, gas turbines must have flexibility in terms of both fuel and load. This implies that thermoacoustic instabilities must be controlled under different operating conditions. Active control strategies are indeed very suitable in this case, as they can adapt to the changes in operating conditions. Nanosecond repetitively pulsed discharges (NRPD) has shown to be promising active control actuators because they require no moving parts. In this study, the effectiveness of feedback and open-loop control law with NRPD actuation to suppress thermoacoustic instabilities in a lab-scale sequential combustor is evaluated. The effect of the controller parameters on the NO emissions of the combustor is also quantified. The results indicate that there is a trade-off between acoustic pulsation reduction and NO emission.
Measuring acoustic transfer matrices of high-pressure hydrogen/air flames for aircraft propulsion
Faure Beaulieu, Abel; Dharmaputra, Bayu; Schuermans, Bruno; et al. (2024)
Combustion and Flame
Destructive thermoacoustic instabilities may potentially slow down significantly the ongoing development of hydrogen combustors for decarbonizing aviation. Their early prediction requires the knowledge of the heat release rate response of individual flames to acoustic perturbations. Obtaining this response at engine conditions is very challenging as it requires the development of sophisticated acoustic actuation and sensing techniques for harsh temperature and pressure environment. To date, experimental measurements of the response of single-flames to upstream and downstream acoustic excitation have been limited to academic burners operated at atmospheric condition. Moreover, to the authors knowledge, the response of turbulent non-premixed H2/air flames has not been experimentally investigated yet, not even at atmospheric pressure. Our experiments address this challenge by determining the acoustic transfer matrix of rich-quench-lean H2 flames anchored on an industrial prototype burner at engine-relevant conditions, including high-altitude flight. The response of the flame is measured up to 2 kHz by using the multi microphone method (MMM). It is shown that the MMM becomes more sensitive to temperature estimations at high frequency and we outline a strategy to improve the method. It is found that the acoustic response of these H2/air non-premixed flames exhibit large gains with non-monotonic trends over a wide frequency range. Different fuel-to-air ratios and flow velocities are considered up to nearly 7 bar. We show that the equivalence ratio and operating pressure do not alter significantly the acoustic flame response, while the flow velocity does, although the flame shape is nearly unchanged when the latter parameter is varied. Furthermore, we extend the classic model of low-Mach-number flame transfer matrices to the relevant case of RQL combustors. Novelty and Significance The ability to accurately measure, at relevant mean pressure, the transfer matrix linking acoustic pressure and velocity across a single burner and its turbulent H2/air flame is key for the development of H2 powered medium-range civil aircrafts. This is because such measurement enables predictions of potential thermoacoustic instabilities in the full annular combustor featuring a large number of burners and flames, and therefore it offers possibilities for burner prototype selection and optimization before full engine tests. The present study is the first demonstration of such challenging measurement, revealing the peculiar acoustic response of non-premixed H2/air flames.
Editorial: Proceedings of the 3rd Symposium on Thermoacoustics in Combustion
Noiray, Nicolas (2024)
International Journal of Spray and Combustion Dynamics
A real-time digital twin of azimuthal thermoacoustic instabilities
Nóvoa, Andrea; Noiray, Nicolas; Dawson, James R.; et al. (2024)
Journal of Fluid Mechanics
When they occur, azimuthal thermoacoustic oscillations can detrimentally affect the safe operation of gas turbines and aeroengines. We develop a real-time digital twin of azimuthal thermoacoustics of a hydrogen-based annular combustor. The digital twin seamlessly combines two sources of information about the system: (i) a physics-based low-order model; and (ii) raw and sparse experimental data from microphones, which contain both aleatoric noise and turbulent fluctuations. First, we derive a low-order thermoacoustic model for azimuthal instabilities, which is deterministic. Second, we propose a real-time data assimilation framework to infer the acoustic pressure, the physical parameters, and the model bias and measurement shift simultaneously. This is the bias-regularized ensemble Kalman filter, for which we find an analytical solution that solves the optimization problem. Third, we propose a reservoir computer, which infers both the model bias and measurement shift to close the assimilation equations. Fourth, we propose a real-time digital twin of the azimuthal thermoacoustic dynamics of a laboratory hydrogen-based annular combustor for a variety of equivalence ratios. We find that the real-time digital twin (i) autonomously predicts azimuthal dynamics, in contrast to bias-unregularized methods; (ii) uncovers the physical acoustic pressure from the raw data, i.e. it acts as a physics-based filter; (iii) is a time-varying parameter system, which generalizes existing models that have constant parameters, and capture only slow-varying variables. The digital twin generalizes to all equivalence ratios, which bridges the gap of existing models. This work opens new opportunities for real-time digital twinning of multi-physics problems.
Flame transfer function measurement of a sequential combustor fuelled with natural gas and hydrogen
Dharmaputra, Bayu; Nagpure, Pushkin; Impagnatiello, Matteo; et al. (2025)
Combustion and Flame
The flame transfer function (FTF) relates acoustic perturbations and the coherent heat release rate response. This frequency-dependent function governs the thermoacoustic stability of a combustor. The FTF measurement is therefore of great interest for predicting the stability of the practical combustor connected to the engine’s compressor and turbine. So far, the FTFs of the second stage of constant pressure sequential combustors (CPSC) have only been obtained from numerical simulations. In this study, second-stage FTFs are measured experimentally. The thermal power of the first- and second-stage flames is configured to be equal. The effects of hydrogen blending in the first- and second-stage fuel mixtures on the sequential FTF are analyzed. The FTF of the sequential flame is fitted with a distributed time delay (DTD) model with two pulses. The trends of the model parameters obtained are consistent with the chemiluminescence of the sequential flame.
A deep operator network for Bayesian parameter identification of self-oscillators
Sugandi, Tobias; Dharmaputra, Bayu; Noiray, Nicolas (2024)
Data-Centric Engineering
Many physical systems exhibit limit-cycle oscillations that can typically be modeled as stochastically driven self-oscillators. In this work, we focus on a self-oscillator model where the nonlinearity is on the damping term. In various applications, it is crucial to determine the nonlinear damping term and the noise intensity of the driving force. This article presents a novel approach that employs a deep operator network (DeepONet) for parameter identification of self-oscillators. We build our work upon a system identification methodology based on the adjoint Fokker–Planck formulation, which is robust to the finite sampling interval effects. We employ DeepONet as a surrogate model for the operator that maps the first Kramers–Moyal (KM) coefficient to the first and second finite-time KM coefficients. The proposed approach can directly predict the finite-time KM coefficients, eliminating the intermediate computation of the solution field of the adjoint Fokker–Planck equation. Additionally, the differentiability of the neural network readily facilitates the use of gradient-based optimizers, further accelerating the identification process. The numerical experiments demonstrate that the proposed methodology can recover desired parameters with a significant reduction in time while maintaining an accuracy comparable to that of the classical finite-difference approach. The low computational time of the forward path enables Bayesian inference of the parameters. Metropolis-adjusted Langevin algorithm is employed to obtain the posterior distribution of the parameters. The proposed method is validated against numerical simulations and experimental data obtained from a linearly unstable turbulent combustor.
Flame transfer function analysis of hydrogen diffusion swirl flames
Wang, Guoqing; Faure Beaulieu, Abel; Schuermans, Bruno; et al. (2024)
Proceedings of the Combustion Institute
This paper investigates the first Flame Transfer Functions (FTFs) of hydrogen diffusion swirl flames, which are crucial for predicting and mitigating thermoacoustic instabilities. Given the need to develop new combustion technologies for hydrogen, it is therefore essential to accurately measure and analyze these FTFs. Employing acoustic and optical methods, we obtained the FTFs over a wide frequency range from 50 to 1000 Hz. Using the acoustic method, the FTFs are deduced from the flame transfer matrices. The FTFs exhibit a low-pass filter behavior with gains decreasing significantly above 150 Hz. Strouhal number normalization effectively collapses the FTFs across various thermal powers, bulk mass flow rates and global equivalence ratios. This result suggests that a generic flame response to acoustic perturbations exists and that the FTF can be interpolated over a range of operating conditions. This study identifies two dominant combustion modes in these hydrogen diffusion swirl flames: a diffusion-mode thin reaction layer near the nozzle and a partially premixed thicker reaction layer downstream. Phase-averaged OH* and OH-PLIF imaging revealed non-uniform transversal oscillations of the reaction zone, offering key insights into the complex swirling flow and the convective wavelength of the coherent heat release rate oscillations along these turbulent hydrogen diffusion swirl flames.
Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation
Malé, Quentin; Lapeyre, Corentin J.; Noiray, Nicolas (2025)
Data-Centric Engineering
This article establishes a data-driven modeling framework for lean hydrogen (H2)-air reaction rates for the Large Eddy Simulation (LES) of turbulent reactive flows. This is particularly challenging since H2 molecules diffuse much faster than heat, leading to large variations in burning rates, thermodiffusive instabilities at the subfilter scale, and complex turbulence-chemistry interactions. Our data-driven approach leverages a Convolutional Neural Network (CNN), trained to approximate filtered burning rates from emulated LES data. First, five different lean premixed turbulent H2-air flame Direct Numerical Simulations (DNSs) are computed each with a unique global equivalence ratio. Second, DNS snapshots are filtered and downsampled to emulate LES data. Third, a CNN is trained to approximate the filtered burning rates as a function of LES scalar quantities: progress variable, local equivalence ratio, and flame thickening due to filtering. Finally, the performances of the CNN model are assessed on test solutions never seen during training. The model retrieves burning rates with very high accuracy. It is also tested on two filter and downsampling parameters and two global equivalence ratios between those used during training. For these interpolation cases, the model approximates burning rates with low error even though the cases were not included in the training dataset. This a priori study shows that the proposed data-driven machine learning framework is able to address the challenge of modeling lean premixed H2-air burning rates. It paves the way for a new modeling paradigm for the simulation of carbon-free hydrogen combustion systems.
Synchronization under saturable nonlinearity
Stoychev, Alexander K.; Pedergnana, Tiemo; Noiray, Nicolas (2024)
Chaos
In this theoretical work, we introduce a nonlinear gain saturation law representative of the experimentally observed properties manifested by phenomena ranging from aeroacoustic shear layers in self-sustained cavity oscillations to flame heat release rate in thermoacoustic instabilities. Furthermore, this type of saturable gain may be relevant for a wider class of physical systems, such as active laser media in photonics. The nonlinearity discussed herein governs the fullscale behavior of a self-oscillator exhibiting linear loss under large amplitude perturbations, in contrast to the cubic damping and linear gain of the Van der Pol model. A distinctive characteristic of the proposed equation is the simple, well behaved gain term in the slow timescale dynamics.
Identification of instantaneous flame transfer functions
Radack, Justus Florian; Schuermans, Bruno; Noiray, Nicolas (2025)
Combustion and Flame
Most existing system identification methods are designed for time-invariant systems. However, for many practical applications, data collection over a wide range of parameters under stationary conditions is either infeasible or costly. To address this limitation, we propose a time-domain, nonparametric methodology for linear, time-varying (LTV) systems, extending the classical paradigm of impulse response function estimation from broadband data using least-squares regression. We introduce the time-varying impulse response function (TV-IRF), which uniquely characterizes the dynamic behavior of LTV systems, and represent it as a series expansion over an orthonormal basis. The collected nonstationary data is projected onto each basis function, and the TV-IRF is estimated using least-squares regression. To validate and analyze this methodology, we first apply it to data generated from measurements of a swirled, hydrogen-enriched flame. Subsequently, we apply it to identify the TV-IRF and time-varying flame transfer functions (TV-FTF) of a canonical slit flame. Using both stationary and nonstationary direct numerical simulations across a wide range of mean flow velocities in the burner, we demonstrate that the instantaneous flame transfer functions derived from the TV-FTF closely match those identified in a stationary setting. Notably, this accuracy is maintained even when the length of nonstationary time series is equivalent to that used for stationary identification at a single velocity. This methodology promises substantial reductions in computational and experimental costs, paving the way for efficient exploration and identification of dynamical systems across large parameter spaces.

Publications 1 - 10 of 22

Nicolas Noiray

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