Pith Number

pith:F6VZU2KV

pith:2026:F6VZU2KV5WMX6RP5NDK4WZPPRW

not attested not anchored not stored refs pending

A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades

Francesca di Mare, Yuan Huang

A progressive PINN framework screens turbine blade families at CFD-comparable accuracy across many conditions with one workflow.

arxiv:2605.07131 v2 · 2026-05-08 · physics.flu-dyn

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\pithnumber{F6VZU2KV5WMX6RP5NDK4WZPPRW}

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Claims

C1strongest claim

To the best of our knowledge, this is the first study to deploy a single PINN workflow for large-scale, engineering-grade screening of turbomachinery blade families across multiple operating conditions, covering ten NACA6 variants and 30 subsonic operating points. The proposed framework achieves CFD-comparable accuracy for pressure and velocity fields while reducing the computational cost required for family-wide blade screening.

C2weakest assumption

That gradually relaxing boundary conditions from tunnel flow to full outlet static pressure, combined with a geometry-aware dynamic loss-weighting scheme, will ensure reliable convergence and accuracy for complex blade geometries and off-design flows where standard PINNs often struggle.

C3one line summary

A progressive Euler-PINN with geometry-aware dynamic loss weighting delivers CFD-comparable pressure and velocity fields for ten NACA6 blade variants across thirty subsonic points at lower computational cost than traditional methods.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-25T02:01:22.557276Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2fab9a6955ed997f45fd68d5cb65ef8d9117af03c815ddc03ab2a6b9e7c6ece1

Aliases

arxiv: 2605.07131 · arxiv_version: 2605.07131v2 · doi: 10.48550/arxiv.2605.07131 · pith_short_12: F6VZU2KV5WMX · pith_short_16: F6VZU2KV5WMX6RP5 · pith_short_8: F6VZU2KV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2fab9a6955ed997f45fd68d5cb65ef8d9117af03c815ddc03ab2a6b9e7c6ece1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "be09c2add47056df087e20f8b5f4c6bedc83d802d62d24a53fc8ce3d87df48e4",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.flu-dyn",
    "submitted_at": "2026-05-08T02:09:35Z",
    "title_canon_sha256": "8d77e241de64496f65eb7632282a2df221fe95729b00fc9d033e45887c56e1c4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.07131",
    "kind": "arxiv",
    "version": 2
  }
}