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Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning — arXiv:2609.16237

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.16237
License (as archived): CC0-1.0
Archived: 2026-10-15T22:14:26.472Z
SHA-256 of the archived text: b73d30529092fa792118f66f45cfaf243654762729cf8fd99712acd215dd6d0b
Stable URL of this page: https://metraipolis.com/documento/dc_3208b37528b5

How to cite

Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning — arXiv:2609.16237 — arXiv (en), https://arxiv.org/abs/2609.16237 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3208b37528b5 (Metraipolis archive dc_3208b37528b5, sha256 b73d30529092fa792118f66f45cfaf243654762729cf8fd99712acd215dd6d0b)

BibTeX

@misc{dc3208b37528b5,
  title = {Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning — arXiv:2609.16237},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.16237},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3208b37528b5; sha256 b73d30529092fa792118f66f45cfaf243654762729cf8fd99712acd215dd6d0b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.16237. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Rotating detonation engines (RDEs) exhibit strongly nonlinear, multiscale wave dynamics that set the observed thermal field. High-fidelity simulations (DNS/LES) resolve these structures but remain computationally prohibitive, while low-order models such as the one-dimensional Koch-Kutz model capture circumferential wave motion yet lack the expressivity for high-frequency content. We use continuous data assimilation (nudging) to synchronize the Koch-Kutz solver with processed high-fidelity temperature data, introducing the prediction-observation mismatch as a relaxation source in the conserved energy equation; where observations are temporally sparse, interpolation supplies a target at every source update. As the nudging strength increases, the reduced model is progressively drawn onto the high-fidelity trajectory, and the forcing recorded along it provides an explicit, state-dependent estimate of the correction the model requires. We then train a Jacobian-regularized closure a priori on this recorded source. With the observation term removed, the corrected model advances autonomously, remains bounded, and recovers the temperature spectrum and the marginal statistics of the conserved variables relative to the baseline.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-16 in physics.comp-ph).
Categorie arXiv: Fluid Dynamics (physics.flu-dyn) ; Machine Learning (cs.LG); Dynamical Systems (math.DS); Computational Physics (physics.comp-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.16237 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T21:59:32.902Z dalla pagina https://arxiv.org/catchup/physics.comp-ph/2026-09-16?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).