Data Assimilation and Optimal Calibration in Nonlinear Models of Flame Dynamics

Yu, Hans; Jaravel, Thomas; Ihme, Matthias; Juniper, Matthew P.; Magri, Luca

doi:10.1115/1.4044378

Cited by 14 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study by Labahn et al [18] successfully applied the EnKF to a non-premixed turbulent flame, serving as a proof of concept for turbulent combustion as a whole. Yu et al [38] successfully applied the EnKF to a reduced-order model of a premixed flame to not only correct the state estimate, but to also correct model parameters, making the reduced-order model more accurate even without observation data. Yu et al [39] used similar techniques in the development of an original reduced-order model for a ducted premixed flame.…”

Section: Applicationsmentioning

confidence: 99%

“…Reynolds-averaged Navier-Stokes (RANS) provides a fully time-averaged solution, which is limited in its ability to capture the geometry-dependent physics of large-scale fluid motion and transient phenomena. LES has therefore emerged as the preferred paradigm in reacting flow simulation [28], however Yu et al [38] and Poinsot [27] collectively identify three persistent sources of difficulty:…”

Section: Introduction 11 Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Reducing the Cost of Ensemble-Based Data Assimilation in Multiple-Query Scenarios through Covariance Augmentation

Ilersich

Schau

Oefelein

et al. 2021

AIAA Propulsion and Energy 2021 Forum

View full text Add to dashboard Cite

We present and assess a method to reduce the computational cost of performing data assimilation (DA) for reacting flow in multiple-query scenarios, where we consider several scenarios with similar underlying dynamics. We focus on ensemble-based DA, in particular the ensemble Kalman filter (EnKF). The accuracy of the EnKF, which depends on the quality of the sample covariance, improves with the ensemble size, but so does its computational cost. To reduce the ensemble size while maintaining accurate covariance, we propose a data-driven approach to augment the covariance based on the statistical behavior learned from model evaluations. We assess our augmentation method using one-dimensional model problems and synthetic reacting flow cases. We show in all cases that ensemble size, and thus computational cost, may be reduced by a factor of three to four while maintaining accuracy.I am grateful to Professor Masayuki Yano and Professor Adam Steinberg for their supervision and mentorship over the past two years. I appreciate your endless enthusiasm, patience, and encouragement to play around with new methods and explore what might be possible. I especially appreciate the efforts you made to maintain all this despite the pandemic. My thesis would not be where it is without your guidance.I would like to thank Professor Peter Grant for support in side projects and extracurriculars, especially in letting me use the aero design lab. You didn't have to do that, and I appreciate so much that you did. I would also like to thank Professor Gabriele D'Eleuterio for the nice chats and advice every time I passed by his office. I'm looking forward to seeing you again when the university reopens. Ci vediamo presto! I would like to thank the other founding members of the Bob Ross Fan Club, Madeline Zhang and Caulan Rupke, for the trips to Booster Juice, the paint nights, and showing off all the cool things they're up to.I would like to thank my UTIAS friends who made my time there so enjoyable. Thanks so much to

show abstract

Section: Applicationsmentioning

confidence: 99%

Section: Introduction 11 Motivationmentioning

confidence: 99%

Reducing the Cost of Ensemble-Based Data Assimilation in Multiple-Query Scenarios through Covariance Augmentation

Ilersich

Schau

Oefelein

et al. 2021

AIAA Propulsion and Energy 2021 Forum

View full text Add to dashboard Cite

show abstract

“…For the first time, experimental data is rigorously assimilated into the model. This is a significant advancement compared to the assimilation of synthetic or simulation data [31]. Firstly, even a direct numerical simulation introduces assumptions into its underlying physical model, e.g.…”

Section: Discussionmentioning

confidence: 99%

A data-driven kinematic model of a ducted premixed flame

Juniper

Magri

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Reduced-order models of flame dynamics can be used to predict and mitigate the emergence of thermoacoustic oscillations in the design of gas turbine and rocket engines. This process is hindered by the fact that these models, although often qualitatively correct, are not usually quantitatively accurate. As automated experiments and numerical simulations produce ever-increasing quantities of data, the question arises as to how this data can be assimilated into physicsinformed reduced-order models in order to render these models quantitatively accurate. In this study, we develop and test a physics-based reduced-order model of a ducted premixed flame in which the model parameters are learned from high speed videos of the flame. The experimental data is assimilated into a level-set solver using an ensemble Kalman filter. This leads to an optimally calibrated reduced-order model with quantified uncertainties, which accurately reproduces elaborate nonlinear features such as cusp formation and pinch-off. The reduced-order model continues to match the experiments after assimilation has been switched off. Further, the parameters of the model, which are extracted automatically, are shown to match the first order behavior expected on physical grounds. This study shows how reduced-order models can be updated rapidly whenever new experimental or numerical data becomes available, without the data itself having to be stored.

show abstract

“…Recently, Yu et al [21][22][23] investigated a related approach. Yu et al employed data assimilation techniques for online identification of state and parameters of a level-set based flame model, in order to match the dynamics of the identical flame resolved in CFD.…”

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

View full text Add to dashboard Cite

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.

show abstract

Data Assimilation and Optimal Calibration in Nonlinear Models of Flame Dynamics

Cited by 14 publications

References 47 publications

Reducing the Cost of Ensemble-Based Data Assimilation in Multiple-Query Scenarios through Covariance Augmentation

Reducing the Cost of Ensemble-Based Data Assimilation in Multiple-Query Scenarios through Covariance Augmentation

A data-driven kinematic model of a ducted premixed flame

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

Contact Info

Product

Resources

About