{
  "abstract": "Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations. In the enthalpy-based pressure-correction formulation, the closure evaluates temperature T, density $ρ$, and compressibility coefficient $ψ$ from the solver state (h,p,Y) through enthalpy-temperature inversion and repeated real-fluid equation-of-state evaluations. Neural-network surrogates offer fixed-cost inference, but direct mapping from (h,p,Y) to $(T,ρ,ψ)$ must capture the enthalpy-temperature relation and non-ideal equation-of-state response, resulting in a complex regression problem. This work introduces a thermodynamics-informed input reparameterization strategy, termed target-aligned input reparameterization (TAIR). TAIR replaces the raw enthalpy coordinate of each property network with a target-matched thermodynamic coordinate: the temperature network uses a temperature estimate obtained by inverting a constant-$c_p$ ideal-gas mixture enthalpy approximation, whereas the density and compressibility networks use an ideal-gas density estimate. These algebraic transformations use only solver-available variables and species constants, guiding the networks to learn real-fluid departures from ideal-gas baselines rather than reconstructing the full closure from raw enthalpy. The method is assessed using supercritical methane-oxygen counterflow flame data against a raw-input baseline and target-inconsistent cross-reparameterization controls. TAIR reduces held-out RMSE by factors of about 1.5, 2.0, and 7.5 for T, $ρ$, and $ψ$, respectively. For an unseen strain-rate flame within the augmented thermodynamic envelope, the corresponding factors are 3.6, 14.5, and 6.0. The target-inconsistent controls perform worse, indicating that the gains arise from thermodynamically matched input design rather than generic preprocessing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Physics of Fluids 38(10), 107502 (2026)",
  "arxiv": "2607.19241",
  "author_total": 6,
  "authors": [
    {
      "name": "Haoze Zhang"
    },
    {
      "name": "Han Li"
    },
    {
      "name": "Ke Xiao"
    },
    {
      "name": "Yangchen Xu"
    },
    {
      "name": "Runze Mao"
    },
    {
      "name": "Zhi X. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4g1jgzgy11173.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.19241",
  "fulltext_url": null,
  "id": "4g1jgzgy11173",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.19241",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.19241",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4g1jgzgy11173.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2607.19241",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.19241"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion",
  "title_text": "Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/4g1jgzgy11173",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1063/5.0352874",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}