pith:QLYD7LUT
Spatio-Temporal Signatures of Intermittency in Helically Rotating Turbulence through Topological Data Analysis
Topological data analysis detects space-time locations of strong turbulent fluctuations in helically rotating flows via changes in persistence diagrams of vorticity and eddy-size fields.
arxiv:2605.17560 v1 · 2026-05-17 · physics.flu-dyn
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Claims
Pronounced variations in Wasserstein-distance heatmaps provide direct signatures of STFs across space and time, and TDA offers an effective complementary tool for detecting STFs that lead to intermittency within turbulent regime.
That the chosen scalar fields (vorticity magnitude and length-scale) and the persistence-diagram representation are sufficient to isolate genuine intermittent events rather than artifacts of the 128^3 grid or the specific helical forcing.
TDA on vorticity magnitude and length-scale fields in helically rotating turbulence produces Wasserstein-distance heatmaps that flag spatiotemporal signatures of strong turbulent fluctuations more sensitively than conventional statistics.
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| First computed | 2026-05-20T00:04:45.941752Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
82f03fae933e99fd79e3d46237af9b796c8c575677fe5ef2e98bce667fde2088
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QLYD7LUTH2M726PD2RRDPL43PF \
| 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: 82f03fae933e99fd79e3d46237af9b796c8c575677fe5ef2e98bce667fde2088
Canonical record JSON
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