Nayak, D and Jonnalagadda, A and Balakrishnan, U and Kolla, H and Aditya, K (2024) A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks. In: Combustion and Flame, 259 .
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Abstract
For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, are a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analyses with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA-/PCA-based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine, a priori, the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates, which are nonlinear functions of the reconstructed state, as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: zero-dimensional (homogeneous) reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage (low and high temperature) ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional dataset representing turbulent autoignition of ethanol in a homogeneous charge compression ignition (HCCI) engine. Results from the analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones. © 2023 The Combustion Institute
| Item Type: | Journal Article |
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| Publication: | Combustion and Flame |
| Publisher: | Elsevier Inc. |
| Additional Information: | The copyright for this article belongs to Elsevier Inc. . |
| Keywords: | Deep neural networks; Ethylene; Higher order statistics; Ignition; Nonlinear analysis; Strain rate, Air mixtures; Chemical state; Co-kurtosis tensor; Dimensionality reduction; Low-dimensional representation; Network-based; Principal-component analysis; Reaction zones; Reconstruction; Thermo-chemical, Principal component analysis |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 27 Feb 2024 11:27 |
| Last Modified: | 27 Feb 2024 11:27 |
| URI: | https://eprints.iisc.ac.in/id/eprint/83621 |
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