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pith:VCNPVVXP

pith:2026:VCNPVVXPJPMDSNB6ZVITTNBWNW
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Boundary treatment algorithms for meshfree RANS turbulence modeling

J\"org Kuhnert, Mohan Padmanabha, Nicolas R. Gauger, Pratik Suchde

The nearest-band neighbor method enables stable wall-function enforcement in meshfree simulations of turbulent flows over curved surfaces.

arxiv:2601.10661 v2 · 2026-01-15 · physics.flu-dyn

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Claims

C1strongest claim

This work establishes the NBN method as a reliable, robust foundation for simulating turbulent flows over practical geometries using meshfree methods.

C2weakest assumption

The assumption that point distributions and normal vectors remain suitable without excessive clustering or uncorrected shifting effects, especially for the SB method on curved surfaces with adverse pressure gradients.

C3one line summary

New nearest-band neighbor and shifted boundary wall treatments for meshfree RANS turbulence models outperform the baseline closest-neighbor approach on flat plates, with NBN proving more robust on curved airfoils.

References

57 extracted · 57 resolved · 0 Pith anchors

[1] Smoothed particle hydrodynamics method from a large eddy simulation perspective. Generalization to a quasi-Lagrangian model 2021
[2] Large eddy simulation and a simple wall model for turbulent flow calculation by a particle method 2013
[3] Extensions of the Spalart–Allmaras turbulence model to account for wall roughness 2003
[4] Smoothed particle hydrodynamics with k - ϵ closure for simulating wall-bounded turbulent flows at medium and high Reynolds numbers 2023
[5] Meshless methods: An overview and recent developments 1996

Formal links

1 machine-checked theorem link

Receipt and verification
First computed 2026-05-17T23:39:16.640731Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

a89afad6ef4bd839343ecd5139b4366db497de9787dc7fcb55105d4df5129bf2

Aliases

arxiv: 2601.10661 · arxiv_version: 2601.10661v2 · doi: 10.48550/arxiv.2601.10661 · pith_short_12: VCNPVVXPJPMD · pith_short_16: VCNPVVXPJPMDSNB6 · pith_short_8: VCNPVVXP
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VCNPVVXPJPMDSNB6ZVITTNBWNW \
  | 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: a89afad6ef4bd839343ecd5139b4366db497de9787dc7fcb55105d4df5129bf2
Canonical record JSON
{
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.flu-dyn",
    "submitted_at": "2026-01-15T18:30:30Z",
    "title_canon_sha256": "d3b95c4980ced80233daadda1f87b028859d91f4a1a4cf1e503f55fdfa60773b"
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