Combustion modeling using Principal Component Analysis: A posteriori validation on Sandia flames D, E and F

Malik, Mohammad Rafi; Vega, Pedro Obando; Coussement, Axel; Parente, Alessandro

doi:10.1016/j.proci.2020.07.014

Cited by 43 publications

(25 citation statements)

References 26 publications

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“…This mechanism has been developed to simulate natural gas mixtures, it is characterized by a comprehensive range of validated applicability, and for its low tendency to run into stiffness problems [1]. For this reason it has been widely employed, a non exhaustive list of applications includes the CH 4 -Air chemical database generation for a novel coupled Principal Components Analysis (PCA)-LES model [39], NO x prediction [40], cryogenic CH 4 -O 2 flames [41], auto-ignition of CH 4 in hot vitiated coflow [42], and biomass-derived syngas modeling [43]. The progress variable is computed according to Equation (6), the selected species i are CO 2 , H 2 O and H 2 , and the weight factor γ * i = 1/MW i is the inverse of the molecular weight as proposed in [33], though highly promising techniques such as PCA [44] might as well offer appealing alternatives to be appraised in future studies.…”

Section: Filtered Chemical Databasementioning

confidence: 99%

Non-Premixed Filtered Tabulated Chemistry: Filtered Flame Modeling of Diffusion Flames

Vega

Coussement

Sadiki

et al. 2021

Fuels

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The flame front filtering is a well-known strategy in turbulent premixed combustion. An extension of this approach for the non-premixed combustion context has been proposed by means of directly filtering counterflow diffusion flamelets. Promising results were obtained for the non-premixed filtered tabulated chemistry formalism on 1-D and 2-D unresolved counterflow flame configurations. The present paper demonstrates the soundness of this approach on a 3-D real laminar non-premixed coflow flame. The model results are compared against the direct filtering of the fully resolved laminar diffusion flame showing that the formalism adequately describes the underlying physics. The study reveals the importance of the one-dimensional counterflow flamelet hypothesis, so that the model activation under this condition is ensured by means of a flame sensor. The consistent coupling between the model and the flame sensor adequately retrieves the flame lift-off and satisfactorily predicts the profile extension due to the filtering process.

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Section: Filtered Chemical Databasementioning

confidence: 99%

Non-Premixed Filtered Tabulated Chemistry: Filtered Flame Modeling of Diffusion Flames

Vega

Coussement

Sadiki

et al. 2021

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“…For instance, progress variables can be identified automatically, without the need for human insight [10]. Evidence from previous research [11,12,13,14,15] suggests that physical meaning can still be attributed to PCs. A common way to interpret PCs is to assess coefficients of the linear combination ORCID(s): that formed them.…”

Section: Introductionmentioning

confidence: 99%

Local manifold learning and its link to domain-based physics knowledge

Zdybał¹,

D’Alessio²,

Attili³

et al. 2022

Preprint

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In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from the training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke-Schumann model, the chemical equilibrium model and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed -heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes.

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“…Ranade and Echekki [11] used PCA to isolate lower dimensional manifolds to develop closure models for turbulent combustion with the reconstruction being performed using a modified version of the pairwise-mixed stirred reactor model. Malik et al [12] applied the PC-transport approach to 3D LES simulations and used Gaussian Process Regression (GPR) for reconstructing the missing species; the use of GPR reconstruction was motivated by a comparative study performed by Isaac et al [13].…”

Section: Introductionmentioning

confidence: 99%

A co-kurtosis based dimensionality reduction method for combustion datasets

Jonnalagadda¹,

Kulkarni²,

Rodhiya³

et al. 2022

Preprint

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Principal Component Analysis (PCA) is a dimensionality reduction technique widely used to reduce the computational cost associated with numerical simulations of combustion phenomena. However, PCA, which transforms the thermochemical state space based on eigenvectors of co-variance of the data, could fail to capture information regarding important localized chemical dynamics, such as the formation of ignition kernels, appearing as outlier samples in a dataset.

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Combustion modeling using Principal Component Analysis: A posteriori validation on Sandia flames D, E and F

Cited by 43 publications

References 26 publications

Non-Premixed Filtered Tabulated Chemistry: Filtered Flame Modeling of Diffusion Flames

Non-Premixed Filtered Tabulated Chemistry: Filtered Flame Modeling of Diffusion Flames

Local manifold learning and its link to domain-based physics knowledge

A co-kurtosis based dimensionality reduction method for combustion datasets

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