{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:F6VZU2KV5WMX6RP5NDK4WZPPRW","short_pith_number":"pith:F6VZU2KV","schema_version":"1.0","canonical_sha256":"2fab9a6955ed997f45fd68d5cb65ef8d9117af03c815ddc03ab2a6b9e7c6ece1","source":{"kind":"arxiv","id":"2605.07131","version":2},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":true},"canonical_record":{"source":{"id":"2605.07131","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2026-05-08T02:09:35Z","cross_cats_sorted":[],"title_canon_sha256":"8d77e241de64496f65eb7632282a2df221fe95729b00fc9d033e45887c56e1c4","abstract_canon_sha256":"be09c2add47056df087e20f8b5f4c6bedc83d802d62d24a53fc8ce3d87df48e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:22.558040Z","signature_b64":"PcYJrBO/S2ItLYyO581rTMNiFU+H6UoElUyvnUMapupyWKk6UACRpmhNLoZiKfOIWxtdQvJaPMv4Z5g7lJNcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fab9a6955ed997f45fd68d5cb65ef8d9117af03c815ddc03ab2a6b9e7c6ece1","last_reissued_at":"2026-05-25T02:01:22.557276Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:22.557276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.07131","created_at":"2026-05-25T02:01:22.557414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.07131v2","created_at":"2026-05-25T02:01:22.557414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.07131","created_at":"2026-05-25T02:01:22.557414+00:00"},{"alias_kind":"pith_short_12","alias_value":"F6VZU2KV5WMX","created_at":"2026-05-25T02:01:22.557414+00:00"},{"alias_kind":"pith_short_16","alias_value":"F6VZU2KV5WMX6RP5","created_at":"2026-05-25T02:01:22.557414+00:00"},{"alias_kind":"pith_short_8","alias_value":"F6VZU2KV","created_at":"2026-05-25T02:01:22.557414+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW","json":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW.json","graph_json":"https://pith.science/api/pith-number/F6VZU2KV5WMX6RP5NDK4WZPPRW/graph.json","events_json":"https://pith.science/api/pith-number/F6VZU2KV5WMX6RP5NDK4WZPPRW/events.json","paper":"https://pith.science/paper/F6VZU2KV"},"agent_actions":{"view_html":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW","download_json":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW.json","view_paper":"https://pith.science/paper/F6VZU2KV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.07131&json=true","fetch_graph":"https://pith.science/api/pith-number/F6VZU2KV5WMX6RP5NDK4WZPPRW/graph.json","fetch_events":"https://pith.science/api/pith-number/F6VZU2KV5WMX6RP5NDK4WZPPRW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW/action/storage_attestation","attest_author":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW/action/author_attestation","sign_citation":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW/action/citation_signature","submit_replication":"https://pith.science/pith/F6VZU2KV5WMX6RP5NDK4WZPPRW/action/replication_record"}},"created_at":"2026-05-25T02:01:22.557414+00:00","updated_at":"2026-05-25T02:01:22.557414+00:00"}