{"paper":{"title":"A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A progressive PINN framework screens turbine blade families at CFD-comparable accuracy across many conditions with one workflow.","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Francesca di Mare, Yuan Huang","submitted_at":"2026-05-08T02:09:35Z","abstract_excerpt":"Rapid aerodynamic screening of turbomachinery blades across wide operating envelopes remains a major computational bottleneck in preliminary design, particularly for energy-conversion and storage systems such as emerging Carnot batteries. Physics-informed neural networks (PINNs) offer a mesh-free alternative to conventional CFD, yet convergence and accuracy often deteriorate for complex blade geometries and off-design flows. We propose a progressive Euler-PINN framework that (i) gradually relaxes boundary conditions from tunnel flow without a blade to full outlet static pressure, and (ii) empl"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A progressive PINN framework screens turbine blade families at CFD-comparable accuracy across many conditions with one workflow.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1794116fc035462035abed5286558b50f38d068e9e6927e176cd25eb69d95b7e"},"source":{"id":"2605.07131","kind":"arxiv","version":2},"verdict":{"id":"c3899e0c-2eab-4095-a755-eeffe038e679","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T02:22:59.378404Z","strongest_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.","one_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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_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.","pith_extraction_headline":"A progressive PINN framework screens turbine blade families at CFD-comparable accuracy across many conditions with one workflow."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.07131/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T11:22:03.389682Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-20T06:36:04.979786Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T17:01:20.056774Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T12:03:02.703346Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"2a146ca9c6a383e7796c77790faac0083f03d0443f8dd9e20203ede6fd0d3750"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"aea493465a920ff7d876e9e770b6119326ecc9953dbcfb19678a3b208bc7bf3a"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}