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Hierarchical autoencoder enhanced chemistry coordinate mapping

Feng, Sheng LU ; Yu, Rixin LU orcid and Bai, Xue Song LU (2025) In Physics of Fluids 37(7).
Abstract

We propose a machine learning-enhanced chemistry coordinate mapping (CCM) method that accelerates the integration of detailed chemical kinetics in reactive flow simulations by grouping chemically similar computational cells into clusters/zones within a low-dimensional latent space. Unlike original CCM methods that rely on manually selecting latent variables, our approach automatically discovers compact and representative latent spaces using a hierarchical autoencoder trained on one-dimensional numerical simulation results. Validation on high-fidelity two-dimensional numerical simulation datasets of diesel/ammonia dual-fuel combustion demonstrates that our method consistently reduces mapping errors and achieves significantly higher... (More)

We propose a machine learning-enhanced chemistry coordinate mapping (CCM) method that accelerates the integration of detailed chemical kinetics in reactive flow simulations by grouping chemically similar computational cells into clusters/zones within a low-dimensional latent space. Unlike original CCM methods that rely on manually selecting latent variables, our approach automatically discovers compact and representative latent spaces using a hierarchical autoencoder trained on one-dimensional numerical simulation results. Validation on high-fidelity two-dimensional numerical simulation datasets of diesel/ammonia dual-fuel combustion demonstrates that our method consistently reduces mapping errors and achieves significantly higher computational speed-up compared to principal component analysis-based CCM and original CCM approaches. These results highlight the potential of data-driven latent space discovery for efficient and scalable combustion modeling.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Physics of Fluids
volume
37
issue
7
article number
075143
publisher
American Institute of Physics (AIP)
external identifiers
  • scopus:105010843964
ISSN
1070-6631
DOI
10.1063/5.0274059
language
English
LU publication?
yes
id
3dae4588-e285-4ec7-b2ec-5bd1d7fbe36a
date added to LUP
2025-12-12 12:18:11
date last changed
2025-12-12 12:18:24
@article{3dae4588-e285-4ec7-b2ec-5bd1d7fbe36a,
  abstract     = {{<p>We propose a machine learning-enhanced chemistry coordinate mapping (CCM) method that accelerates the integration of detailed chemical kinetics in reactive flow simulations by grouping chemically similar computational cells into clusters/zones within a low-dimensional latent space. Unlike original CCM methods that rely on manually selecting latent variables, our approach automatically discovers compact and representative latent spaces using a hierarchical autoencoder trained on one-dimensional numerical simulation results. Validation on high-fidelity two-dimensional numerical simulation datasets of diesel/ammonia dual-fuel combustion demonstrates that our method consistently reduces mapping errors and achieves significantly higher computational speed-up compared to principal component analysis-based CCM and original CCM approaches. These results highlight the potential of data-driven latent space discovery for efficient and scalable combustion modeling.</p>}},
  author       = {{Feng, Sheng and Yu, Rixin and Bai, Xue Song}},
  issn         = {{1070-6631}},
  language     = {{eng}},
  number       = {{7}},
  publisher    = {{American Institute of Physics (AIP)}},
  series       = {{Physics of Fluids}},
  title        = {{Hierarchical autoencoder enhanced chemistry coordinate mapping}},
  url          = {{http://dx.doi.org/10.1063/5.0274059}},
  doi          = {{10.1063/5.0274059}},
  volume       = {{37}},
  year         = {{2025}},
}