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    "headline": "One Model Crosses Nine Kinds of Turbulence",
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    "dek": "Multi-objective learning produced a foundation turbulence model that generalizes across flow mechanisms without case-by-case tuning.",
    "summary": "Multi-objective learning produced a foundation turbulence model that generalizes across flow mechanisms without case-by-case tuning.",
    "body_text": "Researchers trained a unified machine-learning turbulence model on nine representative flows drawn from a library of 36 canonical and complex cases, then tested it on the remainder. The model improved predictions over its baseline across categories while preserving robust behavior across different flow mechanisms. The work targets Reynolds-averaged Navier-Stokes engineering simulations, where turbulence is modeled rather than fully resolved. Its reported generalization does not eliminate validation for new aircraft, turbines, rivers, or climate applications.",
    "why_it_matters": "Multi-objective learning produced a foundation turbulence model that generalizes across flow mechanisms without case-by-case tuning.",
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      "Its reported generalization does not eliminate validation for new aircraft, turbines, rivers, or climate applications."
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        "qualification": "Its reported generalization does not eliminate validation for new aircraft, turbines, rivers, or climate applications."
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    "tags": [
      "turbulence",
      "machine learning",
      "fluid dynamics"
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      "title": "Science China Press via EurekAlert: Foundation turbulence model",
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    "title": "One Model Crosses Nine Kinds of Turbulence",
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