158 research outputs found
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Adjoint Methods as Design Tools in Thermoacoustics
In a thermoacoustic system, such as a flame in a combustor, heat release oscillations couple with acoustic pressure oscillations. If the heat release is sufficiently in phase with the pressure, these oscillations can grow, sometimes with catastrophic consequences. Thermoacoustic instabilities are still one of the most challenging problems faced by gas turbine and rocket motor manufacturers. Thermoacoustic systems are characterized by many parameters to which the stability may be extremely sensitive. However, often only few oscillation modes are unstable. Existing techniques examine how a change in one parameter affects all (calculated) oscillation modes, whether unstable or not. Adjoint techniques turn this around: They accurately and cheaply compute how each oscillation mode is affected by changes in all parameters. In a system with a million parameters, they calculate gradients a million times faster than finite difference methods. This review paper provides: (i) the methodology and theory of stability and adjoint analysis in thermoacoustics, which is characterized by degenerate and nondegenerate nonlinear eigenvalue problems; (ii) physical insight in the thermoacoustic spectrum, and its exceptional points; (iii) practical applications of adjoint sensitivity analysis to passive control of existing oscillations, and prevention of oscillations with ad hoc design modifications; (iv) accurate and efficient algorithms to perform uncertainty quantification of the stability calculations; (v) adjoint-based methods for optimization to suppress instabilities by placing acoustic dampers, and prevent instabilities by design modifications in the combustor's geometry; (vi) a methodology to gain physical insight in the stability mechanisms of thermoacoustic instability (intrinsic sensitivity); and (vii) in nonlinear periodic oscillations, the prediction of the amplitude of limit cycles with weakly nonlinear analysis, and the theoretical framework to calculate the sensitivity to design parameters of limit cycles with adjoint Floquet analysis. To show the robustness and versatility of adjoint methods, examples of applications are provided for different acoustic and flame models, both in longitudinal and annular combustors, with deterministic and probabilistic approaches. The successful application of adjoint sensitivity analysis to thermoacoustics opens up new possibilities for physical understanding, control and optimization to design safer, quieter, and cleaner aero-engines. The versatile methods proposed can be applied to other multiphysical and multiscale problems, such as fluid–structure interaction, with virtually no conceptual modification.</jats:p
Progress in analytical methods to predict and control azimuthal combustion instability modes in annular chambers
Longitudinal low-frequency thermoacoustic unstable modes in combustion chambers have been intensively studied experimentally, numerically, and theoretically, leading to significant progress in both understanding and controlling these acoustic modes. However, modern annular gas turbines may also exhibit azimuthal modes, which are much less studied and feature specific mode structures and dynamic behaviors, leading to more complex situations. Moreover, dealing with 10–20 burners mounted in the same chamber limits the use of high fidelity simulations or annular experiments to investigate these modes because of their complexity and costs. Consequently, for such circumferential acoustic modes, theoretical tools have been developed to uncover underlying phenomena controlling their stability, nature, and dynamics. This review presents recent progress in this field. First, Galerkin and network models are described with their pros and cons in both the temporal and frequency framework. Then, key features of such acoustic modes are unveiled, focusing on their specificities such as symmetry breaking, non-linear modal coupling, forcing by turbulence. Finally, recent works on uncertainty quantifications, guided by theoretical studies and applied to annular combustors, are presented. The objective is to provide a global view of theoretical research on azimuthal modes to highlight their complexities and potential
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Feedback control of oscillations in combustion and cavity flows
This thesis considers the control of combustion oscillations, motivated by the susceptibility of lean premixed combustion to such oscillations, and the long and expensive development and commissioning times that this is giving rise to. The controller used is both closed-loop, employing an actuator to modify some system parameter in response to a measured signal, and adaptive, meaning that it is able to maintain control over a wide range of operating conditions. The controller is applied to combustion systems with annular geometries, where instabilities can occur both longitudinally and azimuthally, and which require multiple sensors and multiple actuators for control.
One of the requirements of Lyapunov-based adaptive control which is particularly troublesome for combustion systems is then addressed: that the sign of the high-frequency gain of the open-loop system is known. We address it by using an adaptive controller which employs a Nussbaum gain, and successfully apply it experimentally to combustion oscillations in a Rijke tube.
Another type of fluid-acoustic resonance is then considered: the compressible flow past a shallow cavity. We start by finding a linear model of the cavity flow's dynamics, or its `transfer function', which we identify from direct numerical simulations. We compare this measured transfer function to that given by a conceptual model which is based on the Rossiter mechanism, and which models each component of the flow physics separately.
We then look at using closed-loop control to eliminate these cavity oscillations. We start by designing a robust controller based on a balanced reduced order model of the system, the model being provided by the Eigensystem Realization Algorithm (ERA). The robust controller provides closed-loop stability over a much wider Mach number range than seen in previous studies. Finally, we look at the suitability of the adaptive controller, earlier developed for combustion oscillations, for the cavity. Based on some general properties of the cavity flow, and by using collocated control, the oscillations are eliminated at all Mach numbers tested in the range .This work was supported by the EPSRC and Rolls-Royce plc
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
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Inverse problems in thermoacoustics
Thermoacoustics is a branch of fluid mechanics, and is as such governed by the conservation laws of mass, momentum, energy and species.
While computational fluid dynamics (CFD) has entered the design process of many applications in fluid mechanics, its success in thermoacoustics is limited by the multi-scale, multi-physics nature of the subject.
In his influential monograph from 2006, Prof. Fred Culick writes about the role of CFD in thermoacoustic modeling:
The main reason that CFD has otherwise been relatively helpless in this subject is that problems of combustion instabilities involve physical and chemical matters that are still not well understood.
Moreover, they exist in practical circumstances which are not readily approximated by models suitable to formulation within CFD.
Hence, the methods discussed and developed in this book will likely be
useful for a long time to come, in both research and practice.
[. . . ] It seems to me that eventually the most effective ways of formulating predictions and theoretical interpretations of combustion instabilities in practice will rest on combining methods of the sort discussed in this book with computational fluid dynamics, the whole confirmed by experimental results.
Despite advances in CFD and large-eddy simulation (LES) in particular, unsteady simulations for more than a few selected operating points are computationally infeasible.
The ‘methods discussed in this book’ refer to reduced-order models of thermoacoustic oscillations.
Whether intentional or not, the last sentence anticipates the advent of data-driven methods, and encapsulates the philosophy behind this work.
This work brings together two workhorses of the design process:
physics-informed reduced-order models and data from higher-fidelity sources such as simulations and experiments.
The three building blocks to all our statistical inference frameworks are:
(i) a hierarchical view of reduced-order models consisting of states, parameters and governing equations;
(ii) probabilistic formulations with random variables and stochastic processes;
and (iii) efficient algorithms from statistical learning theory and machine learning.
While leveraging advances in statistical and machine learning, we demonstrate the feasibility of Bayes’ rule as a first principle in physics-informed statistical inference.
In particular, we discuss two types of inverse problems in thermoacoustics:
(i) implicit reduced-order models representative of nonlinear eigenproblems from linear stability analysis;
and (ii) time-dependent reduced-order models used to investigate nonlinear dynamics.
The outcomes of statistical inference are improved predictions of the state, estimates of the parameters with uncertainty quantification and an assessment of the reduced-order model itself.
This work highlights the role that data can play in the future of combustion modeling for thermoacoustics.
It is increasingly impractical to store data, particularly as experiments become automated and numerical simulations become more detailed.
Rather than store the data itself, the techniques in this work optimally assimilate the data into the parameters of a physics-informed reduced-order model.
With data-driven reduced-order models, rapid prototyping of combustion systems can feed into rapid calibration of their reduced-order
models and then into gradient-based design optimization.
While it has been shown, e.g. in the context of ignition and extinction, that large-eddy simulations become quantitatively predictive when augmented with data, the reduced-order modeling of flame dynamics in turbulent flows remains challenging.
For these challenging situations, this work opens up new possibilities for the development of reduced-order models that adaptively change any time that data from experiments or simulations becomes available.Schlumberger Cambridge International Scholarshi
Accounting for mean flow effects in a zero-Mach number thermo-acoustic solver: application to entropy induced combustion instabilities
Pratiquement toutes les chambres de combustion présentent des instabilités. Par conséquent, il est nécessaire de mieux les comprendre afin de les contrôler. Une possibilité est de simuler l’écoulement réactif à l’intérieur d’une chambre de combustion grâce à la Simulation aux Grandes Echelles (SGE). Cependant la SGE est très coûteuse en terme de capacité de calcul. Une autre possibilité est de réduire la complexité du problème à une simple équation d’onde thermoacoustique (équation dite de Helmholtz), qui peut être résolue en fréquence comme un problème aux valeurs propres. Le couplage entre l’acoustique et la flamme est alors prise en compte au travers des modèles appropriés. Le principal problème de cette méthode est qu’elle repose sur l’hypothèse d’un nombre de Mach nul. Tous les phénomènes liés à l’écoulement moyen sont donc négligés. La présente thèse propose une nouvelle stratégie pour prendre en compte certains effets de l’écoulement dans un contexte à Mach nul. Dans une première partie, la manière la plus judicieuse d’imposer un élément présentant un écoulement très rapide est étudiée. La seconde partie se focalise sur le couplage entre l’acoustique et les hétérogénéités de température qui sont générées par la flamme et naturellement convectées par l’écoulement moyen. Ce phénomène est important car il est responsable du bruit indirect de combustion qui peut conduire à une instabilité thermoacoustique. Un nouveau type de condition limite (DECBC) est proposé afin de prendre en compte ce mécanisme dans un contexte de résolution de l’équation de Helmholtz à Mach nul. Dans la dernière partie, une chambre de combustion aéronautique présentant une instabilité mixte acoustique/entropique est étudiée. Le bénéfice des méthodes développées dans la présente thèse est testé et comparé à des calculs avec la SGE. Il est montré que les calculs avec un solveur de Helmholtz peuvent reproduire une instabilité de combustion complexe, et que cet outil s’avère avoir le potentiel pour prédire les instabilités afin de concevoir de nouvelles chambres de combustion. ABSTRACT : Virtually all combustion chambers are subject to instabilities. Consequently there is a need to better understand them so as to control them. A possibility is to simulate the reactive flow within a combustor with the Large-Eddy Simulation (LES) method. However LES results come at a tremendous computational cost. Another route is to reduce the complexity of the problem to a simple thermoacoustic Helmholtz wave equation, which can be solved in the frequency domain as an eigenvalue problem. The coupling between the flame and the acoustics is then taken into account via proper models. The main drawback of this latter methodology is that it relies on the zero-Mach number assumption. Hence all phenomena inherent to mean flow effects are neglected. The present thesis aims to provide a novel strategy to introduce back some mean flow effects within the zero-Mach number framework. In a first part, the proper way to impose high-speed elements such as a turbine is investigated. The second part focuses on the coupling between acoustics and temperature heterogeneities that are naturally generated at the flame and convected downstream by the flow. Such phenomenon is important because it is responsible for indirect combustion noise that may drive a thermoacoustic instability. A Delayed Entropy Coupled Boundary Condition (DECBC) is then derived in order to account for this latter mechanism in the framework of a Helmholtz solver where the baseline flow is assumed at rest. In the last part, a realistic aero-engine combustor that features a mixed acoustic/entropy instability is studied. The methodology developed in the present thesis is tested and compared to LES computations. It is shown that computations with the Helmholtz solver can reproduce a complex combustion instability, and that this latter methodology is a potential tool to design new combustors so as to predict and avoid combustion instabilities
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