Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron

Tathawadekar, Nilam; Doan, Nguyen Anh Khoa; Silva, Camilo F.; Thuerey, Nils

doi:10.1016/j.proci.2020.07.115

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…Selimefendigil & Polifke (2011) developed a low-order model in the frequency domain for predicting thermoacoustic limit cycles using the feed-forward neural network identification method. Consequently, neural networks are exploited for deducing heat release models (Jaensch & Polifke 2017) and nonlinear flame responses (Tathawadekar et al 2021) in thermoacoustic systems. Recently, Nóvoa & Magri (2022) used an echo state network, a reservoir-computing-based recurrent neural network, for the real-time bias-aware estimation of the states and parameters of a numerical Rijke tube model.…”

Section: Thermoacoustic Oscillations Via Hopf Bifurcationmentioning

confidence: 99%

A PINN approach for identifying governing parameters of noisy thermoacoustic systems

Son,

Lee

2024

J. Fluid Mech.

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Identifying the governing parameters of self-sustained oscillation is crucial for the diagnosis, prediction and control of thermoacoustic instabilities. In this paper, we propose and validate a novel method for computing the parameters of thermoacoustic oscillation in a stochastic environment, which exploits a physics-informed neural network (PINN). Specifically, we introduce a negative log-likelihood loss function that integrates the stochastic samples and the solution of the Fokker–Planck equation. The proposed framework is validated using the numerically generated signal and the experimental data obtained from an annular combustor, both before and after the supercritical Hopf bifurcation. The results of PINN-based system identification show good agreement with the actual system parameters and the original stochastic signal, with improved accuracy compared to established methods. To the best of our knowledge, this study constitutes the first demonstration of the PINN-inverse approach that uses the noise-induced dynamics of thermoacoustic systems, opening up new pathways for diagnosing and predicting the thermoacoustic behaviour of various combustion systems.

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Section: Thermoacoustic Oscillations Via Hopf Bifurcationmentioning

confidence: 99%

A PINN approach for identifying governing parameters of noisy thermoacoustic systems

Son,

Lee

2024

J. Fluid Mech.

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“…Recently, a different modelling strategy was introduced for the modelling of the non-linear flame response in the time domain (Tathawadekar et al 7 ). The derived model is obtained relatively easy from data and is more general than the FDF as it accounts for interaction between frequencies.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear flame response modelling by a parsimonious set of ordinary differential equations

Doehner

Haeringer

Silva

2022

International Journal of Spray and Combustion Dynamics

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In this work we present a parsimonious set of ordinary differential equations (ODEs) that describes with satisfactory precision the linear and non-linear dynamics of a typical laminar premixed flame in time and frequency domain. The proposed model is characterized by two ODEs of second-order that can be interpreted as two coupled mass-spring-damper oscillators with a symmetric, nonlinear damping term. This non-linear term is identified as function of the rate of displacement following [Formula: see text]. The model requires only four constants to be calibrated. This is achieved by carrying out an optimization procedure on one input and one output broadband signal obtained from high-fidelity numerical simulations (CFD). Note that the Transfer Function (FTF) or describing function (FDF) of the flame under investigation are not known a-priori, and therefore not used in the optimization procedure. Once the model is trained on CFD input and output time series, it is capable of recovering with quantitative accuracy the impulse response of the laminar flame under investigation and, hence, the corresponding frequency response (FTF). If fed with harmonic signals of different frequency and amplitude, the trained model is capable of retrieving with qualitative precision the flame describing function (FDF) of the studied flame. We show that the non-linear term [Formula: see text] is essential for capturing the gain saturation for high amplitudes of the input signal. All results are validated against CFD data.

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“…While this is inherently the case for the flame resolved by CFD, the LOM must also include non-linear flame models representing the dynamics of all remaining flames. However, non-linear flame models, which are valid for a wide range of frequencies and amplitudes -the relevant frequencies and amplitudes are typically not known in advance -are generally very costly to determine [17][18][19] or are only of qualitative nature. 20 Instead, here we propose to identify the non-linear flame models "on-the-fly", based on a linear model of flame dynamics plus observations of non-linear flame dynamics in CFD during the simulation of the selfexcited configuration.…”

Section: Introductionmentioning

confidence: 99%

Hybrid CFD/low-order modeling of thermoacoustic limit cycle oscillations in can-annular configurations

Haeringer¹,

Polifke²

2022

International Journal of Spray and Combustion Dynamics

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We propose a hybrid strategy for modeling non-linear thermoacoustic phenomena, e.g. limit-cycle (LC) oscillations, in can-annular combustion systems. The suggested model structure comprises a compressible CFD simulation limited to the burner/flame zone of one single can, coupled to a low-order model (LOM) representing the remaining combustor. In order to employ the suggested strategy for modeling non-linear phenomena like LC oscillations, the LOM must capture non-linear flame dynamics in the cans, which are not resolved by CFD. Instead of identifying such non-linear flame models in preliminary simulations, we aim at learning the non-linear dynamics “on-the-fly”, while simulating the self-excited system under consideration. Based on the observation of flame dynamics in the CFD domain, the parameters of the employed non-linear models are estimated during run time. The present study reveals that block-oriented models, which comprise a linear dynamic part followed by a static non-linear function, are well suited for this purpose. The proposed hybrid model is applied to a laminar can-annular combustor. Results agree well with the monolithic CFD simulation of the entire combustor, while the computational cost is drastically reduced. The employed flame models, whose parameters are identified during the simulation of the self-excited LC oscillation, represent well the relevant non-linear dynamics of the considered flame.

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Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron

Cited by 12 publications

References 19 publications

A PINN approach for identifying governing parameters of noisy thermoacoustic systems

A PINN approach for identifying governing parameters of noisy thermoacoustic systems

Nonlinear flame response modelling by a parsimonious set of ordinary differential equations

Hybrid CFD/low-order modeling of thermoacoustic limit cycle oscillations in can-annular configurations

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