{"paper":{"title":"Effect of Turbulence-Closure Consistency on Airfoil Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Inconsistencies among turbulence closures produce up to 250 percent differences in airfoil shapes identified from wake velocity fields.","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"George Em Karniadakis, Zhen Zhang","submitted_at":"2025-11-11T15:17:35Z","abstract_excerpt":"We consider an inverse flow problem in which the airfoil shape is identified from its wake signature, namely the velocity field in the wake of a target airfoil. This is an ill-posed problem and highly sensitive to the accuracy and consistency of the employed turbulence closure. We first demonstrate that shape identification based on a single flow condition is ill-posed, whereas incorporating multiple wake signatures obtained at different angles of attack substantially mitigates this ill-posedness. We then compare the inferred geometries obtained using different turbulence closures and find tha"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"inconsistencies among the models lead to markedly divergent shapes... up to a 250 percent difference among these sensitivities... turbulence-closure consistency is essential for reliable shape identification","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the wake velocity fields supplied to the inverse solver are free of measurement or discretization error and that the optimization procedure itself does not introduce additional model-dependent bias when comparing closures.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Turbulence model choice causes up to 250% differences in geometric sensitivities during airfoil shape identification from wake data, showing that closure consistency is required for reliable inverse results.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Inconsistencies among turbulence closures produce up to 250 percent differences in airfoil shapes identified from wake velocity fields.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"88dd91c7608a2d0c73a535152b67fd2cb54707aaa280092933379b0c1c8490f2"},"source":{"id":"2511.08341","kind":"arxiv","version":3},"verdict":{"id":"098e9f29-f81b-46a2-87ec-6b1047f4c6d2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T23:36:57.201428Z","strongest_claim":"inconsistencies among the models lead to markedly divergent shapes... up to a 250 percent difference among these sensitivities... turbulence-closure consistency is essential for reliable shape identification","one_line_summary":"Turbulence model choice causes up to 250% differences in geometric sensitivities during airfoil shape identification from wake data, showing that closure consistency is required for reliable inverse results.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the wake velocity fields supplied to the inverse solver are free of measurement or discretization error and that the optimization procedure itself does not introduce additional model-dependent bias when comparing closures.","pith_extraction_headline":"Inconsistencies among turbulence closures produce up to 250 percent differences in airfoil shapes identified from wake velocity fields."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.08341/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":12,"sample":[{"doi":"10.1137/1.9780898717921","year":2005,"title":"Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, SIAM, Philadelphia, PA, 2005.doi:10.1137/1.9780898717921","work_id":"79b7b50c-7898-4b96-9648-2d36546dc011","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1017/s0962492910000061","year":2010,"title":"Inverse problems: A Bayesian perspective","work_id":"18c614bd-d813-4c90-a26f-8a338c0d7391","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.2514/6.1992-439","year":1992,"title":"Spalart and Steven R","work_id":"ad288477-b7ff-4f40-8462-de9ed20f59d5","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1994,"title":"F. R. Menter, Two-equation eddy-viscosity turbulence models for engi- neering applications, AIAA Journal 32 (1994) 1598–1605. doi:10.2514/ 3.12149","work_id":"62ef06fb-9811-4144-9094-de4cc30409f1","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1016/0142-727x(95","year":1996,"title":"T. J. Craft, B. E. Launder, K. Suga, Development and application of a cubic eddy-viscosity model of turbulence, International Journal of Heat and Fluid Flow 17 (1996) 108–115. doi:10.1016/0142-727X(95","work_id":"75854868-ac73-470e-8755-972bc3aac78d","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":12,"snapshot_sha256":"bd98ecd8c51ee5ac7d703d11b8756c7a3dc08b8b499dad3bec42d3c095e5005e","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"}