{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5Y5SNAA5AF4GWPH24NX7XYQT3J","short_pith_number":"pith:5Y5SNAA5","schema_version":"1.0","canonical_sha256":"ee3b26801d01786b3cfae36ffbe213da4e12bf098abca1857dc56e30a50e0d11","source":{"kind":"arxiv","id":"2503.17977","version":1},"attestation_state":"computed","paper":{"title":"Cost-effective multi-fidelity strategy for the optimization of high-Reynolds number turbine flows guided by LES","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Camille Matar, Paola Cinnella, Xavier Gloerfelt","submitted_at":"2025-03-23T08:14:18Z","abstract_excerpt":"A cost-effective multi-objective shape optimization strategy is proposed for high-Reynolds number flows involving complex phenomena such as boundary layer transition, shock-wave interactions, and turbulent wakes. These processes are poorly captured by Reynolds-Averaged Navier--Stokes (RANS) models, necessitating higher-fidelity approaches like Large Eddy Simulation (LES). However, LES is computationally prohibitive at high Reynolds numbers, making its direct use in optimization impractical. To address this, we introduce a low-dimensional design space representation using Singular Value Decompo"},"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":false},"canonical_record":{"source":{"id":"2503.17977","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2025-03-23T08:14:18Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"6a8e4818df168eadc6b8f3a253e42d45a9a51962d2d6dc2b834f7ca5f515ac42","abstract_canon_sha256":"2edfab1610b29e43af51bd9edb7d8d50c8b16efaa0976d11e236862d9ca671ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:59.729389Z","signature_b64":"F3y+811ou1qyHXbA7pYINm5fi47uBu75M0209CpJSxaSIFgClDvD4Y1uRlkPwu+sam5Py/sv5gFgNp/4yTfqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee3b26801d01786b3cfae36ffbe213da4e12bf098abca1857dc56e30a50e0d11","last_reissued_at":"2026-07-05T10:37:59.728613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:59.728613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cost-effective multi-fidelity strategy for the optimization of high-Reynolds number turbine flows guided by LES","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Camille Matar, Paola Cinnella, Xavier Gloerfelt","submitted_at":"2025-03-23T08:14:18Z","abstract_excerpt":"A cost-effective multi-objective shape optimization strategy is proposed for high-Reynolds number flows involving complex phenomena such as boundary layer transition, shock-wave interactions, and turbulent wakes. These processes are poorly captured by Reynolds-Averaged Navier--Stokes (RANS) models, necessitating higher-fidelity approaches like Large Eddy Simulation (LES). However, LES is computationally prohibitive at high Reynolds numbers, making its direct use in optimization impractical. To address this, we introduce a low-dimensional design space representation using Singular Value Decompo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17977","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.17977/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"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":"2503.17977","created_at":"2026-07-05T10:37:59.728705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17977v1","created_at":"2026-07-05T10:37:59.728705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17977","created_at":"2026-07-05T10:37:59.728705+00:00"},{"alias_kind":"pith_short_12","alias_value":"5Y5SNAA5AF4G","created_at":"2026-07-05T10:37:59.728705+00:00"},{"alias_kind":"pith_short_16","alias_value":"5Y5SNAA5AF4GWPH2","created_at":"2026-07-05T10:37:59.728705+00:00"},{"alias_kind":"pith_short_8","alias_value":"5Y5SNAA5","created_at":"2026-07-05T10:37:59.728705+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17279","citing_title":"Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J","json":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J.json","graph_json":"https://pith.science/api/pith-number/5Y5SNAA5AF4GWPH24NX7XYQT3J/graph.json","events_json":"https://pith.science/api/pith-number/5Y5SNAA5AF4GWPH24NX7XYQT3J/events.json","paper":"https://pith.science/paper/5Y5SNAA5"},"agent_actions":{"view_html":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J","download_json":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J.json","view_paper":"https://pith.science/paper/5Y5SNAA5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17977&json=true","fetch_graph":"https://pith.science/api/pith-number/5Y5SNAA5AF4GWPH24NX7XYQT3J/graph.json","fetch_events":"https://pith.science/api/pith-number/5Y5SNAA5AF4GWPH24NX7XYQT3J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J/action/storage_attestation","attest_author":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J/action/author_attestation","sign_citation":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J/action/citation_signature","submit_replication":"https://pith.science/pith/5Y5SNAA5AF4GWPH24NX7XYQT3J/action/replication_record"}},"created_at":"2026-07-05T10:37:59.728705+00:00","updated_at":"2026-07-05T10:37:59.728705+00:00"}