Dynamic Mode Decomposition: A Tool to Extract Structures Hidden in Massive Datasets

Grenga, Temistocle; Müeller, Michael E.

doi:10.1007/978-3-030-44718-2_8

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Motheau et al [38] employed DMD for the analysis of a LES database, with the aim of developing a reduced order model to show that one of the mechanisms triggering combustion instabilities involves convected and acoustic entropy waves. Additional applications of DMD to identify patterns and flow instabilities in reacting flows include the work by Abou-Taouk et al [39], who studied a V-flame in an afterburner-type configuration; the work by Ghani et al [40], who identified the main patterns in an industrial gas turbine combustion chamber; and the work by Grenga et al [41,42], who proposed a more efficient implementation of DMD, reducing the memory consumption, which was tested employing a dataset obtained from a Direct Numerical Simulation (DNS) of a turbulent reacting jet.…”

Section: Introductionmentioning

confidence: 99%

Higher Order Dynamic Mode Decomposition to Model Reacting Flows

Corrochano

D’Alessio

Parente

et al. 2023

Sustainable Aviation

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This work presents a new application of higher order dynamic mode decomposition (HODMD) for the analysis of reactive flows. Due to the high complexity of the data analysed, consisting of more than 80 variables (i.e., temperature and chemically reacting species) a new extension of HODMD has been developed combining the multi-dimensional HODMD algorithm with classical preprocessing techniques generally used in machine learning analyses, such as principal component analysis (PCA). This new methodology has proved to be suitable to identify the main patterns driving the main dynamics of the flow, as well as to develop reduced order models grounded in physical principles. The new algorithm was tested by means of a database obtained from a Computational Fluid Dynamics simulation of an axy-symmetric time varying non-premixed co-flow nitrogen-diluted methane flame, carried out by means of a detailed kinetic mechanism. Different dynamics were identified, and they were associated to the different variables of the reactive flow analysed. Also, this algorithm has been coupled with an additional feature selection step carried out via PCA and varimax rotation. The results that were obtained by coupling PCA with varimax rotation show the outstanding capabilities of this novel methodology to compress original databases as function of the different preprocessing techniques that are used.

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Section: Introductionmentioning

confidence: 99%

Higher Order Dynamic Mode Decomposition to Model Reacting Flows

Corrochano

D’Alessio

Parente

et al. 2023

Sustainable Aviation

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“…Motheau et al [24] used DMD for the analysis of a LES database, with the aim at developing a low-order model showing that one of the mechanism triggering combustion instability is related to convected and acoustic entropy waves. More applications of DMD to identify patterns and flow instabilities in reactive flows include the work by Abou-Taouk et al [25], who studied a V-flame in an afterburner-type configuration; the work by Ghani et al [26], who identified the main patterns in an industrial gas turbine combustion chamber; and the work by Grenga et al [27,28], who proposed a more efficient implementation of DMD, suitable to elucidate multiphysics requiring with a reduced memory usage, which was tested in a DNS database solving a turbulent premixed flame problem.…”

Section: Introductionmentioning

confidence: 99%

Higher order dynamic mode decomposition to model reacting flows

Corrochano¹,

D’Alessio²,

Parente³

et al. 2022

Preprint

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In this work, the application of the multi-dimensional higher order dynamic mode decomposition (HODMD) is proposed for the first time to analyse combustion databases. In particular, HODMD has been adapted and combined with other pre-processing techniques (generally used in machine learning), in light of the multivariate nature of the data. A truncation step separate the main dynamics driving the flow from less relevant non-linear dynamics. The method is applied to analyse a database obtained from a Computational Fluid Dynamics (CFD) simulation of an axisymmetric, time varying, non-premixed, co-flow methane flame carried out by means of a detailed kinetic mechanism. Results show that HODMD can reconstruct the main jet dynamics with a reduced number of relevant modes, able to reproduce the system dynamics. These modes are found to be representative for the main flow physics with two main advantages: (i) they provide for the possibility to achieve a strong simplification with respect to the high-dimensional input data, and at the same time (ii) a small reconstruction error with respect to the original dataset is observed. In addition, the method was also validated considering a reduced matrix obtained using Principal Component Analysis (PCA) based feature selection and the Varimax rotation. This validation also reveals that it is not important to have all the variables in the dataset, just a group of them is necessary to obtain the main dynamics of the system. This has an impact on feature selection and on the cost these methodologies for very massive data.

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Predictive Data-Driven Model Based on Generative Adversarial Network for Premixed Turbulence-Combustion Regimes

Grenga

Nista

Schumann

et al. 2022

Combustion Science and Technology

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Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps.

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Dynamic Mode Decomposition: A Tool to Extract Structures Hidden in Massive Datasets

Cited by 3 publications

References 44 publications

Higher Order Dynamic Mode Decomposition to Model Reacting Flows

Higher Order Dynamic Mode Decomposition to Model Reacting Flows

Higher order dynamic mode decomposition to model reacting flows

Predictive Data-Driven Model Based on Generative Adversarial Network for Premixed Turbulence-Combustion Regimes

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