{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NWEDYN3GRGGZQX7KUQKUV6DT67","short_pith_number":"pith:NWEDYN3G","schema_version":"1.0","canonical_sha256":"6d883c3766898d985feaa4154af873f7e0a3cc59c187ceb18b2b75860f28f4be","source":{"kind":"arxiv","id":"1907.11281","version":1},"attestation_state":"computed","paper":{"title":"Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.app-ph","physics.flu-dyn","stat.ML"],"primary_cat":"cs.LG","authors_text":"G\\\"unther Waxenegger-Wilfing, Jan Christian Deeken, Kai Dresia, Michael Oschwald","submitted_at":"2019-07-24T16:49:09Z","abstract_excerpt":"Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design reliable and efficient cooling systems, heat transfer modeling is a fundamental issue to address. Advanced computational fluid dynamics (CFD) calculations achieve sufficient accuracy, but the associated computational cost prevents an efficient integration in optimization loops. Surrog"},"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":"1907.11281","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-24T16:49:09Z","cross_cats_sorted":["physics.app-ph","physics.flu-dyn","stat.ML"],"title_canon_sha256":"5609f0866ebd0a2322c521f95981f2c4e4481f6fac7874e40ed14c153346259b","abstract_canon_sha256":"bc8549d56db71895d9e971ae8a00e61673673ad3242dd8ee592f0b893dd7a1e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:38:45.848485Z","signature_b64":"+GkaX53/3tG4XjT8hoZPvmxTt1Nj8I5xUDM1ZX4G2sGCJbboRWjz7/me7zo5R9p2r0dp7Xt0brDhLol+QHxGAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d883c3766898d985feaa4154af873f7e0a3cc59c187ceb18b2b75860f28f4be","last_reissued_at":"2026-07-05T00:38:45.848047Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:38:45.848047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.app-ph","physics.flu-dyn","stat.ML"],"primary_cat":"cs.LG","authors_text":"G\\\"unther Waxenegger-Wilfing, Jan Christian Deeken, Kai Dresia, Michael Oschwald","submitted_at":"2019-07-24T16:49:09Z","abstract_excerpt":"Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design reliable and efficient cooling systems, heat transfer modeling is a fundamental issue to address. Advanced computational fluid dynamics (CFD) calculations achieve sufficient accuracy, but the associated computational cost prevents an efficient integration in optimization loops. Surrog"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11281","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/1907.11281/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":"1907.11281","created_at":"2026-07-05T00:38:45.848106+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.11281v1","created_at":"2026-07-05T00:38:45.848106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11281","created_at":"2026-07-05T00:38:45.848106+00:00"},{"alias_kind":"pith_short_12","alias_value":"NWEDYN3GRGGZ","created_at":"2026-07-05T00:38:45.848106+00:00"},{"alias_kind":"pith_short_16","alias_value":"NWEDYN3GRGGZQX7K","created_at":"2026-07-05T00:38:45.848106+00:00"},{"alias_kind":"pith_short_8","alias_value":"NWEDYN3G","created_at":"2026-07-05T00:38:45.848106+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67","json":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67.json","graph_json":"https://pith.science/api/pith-number/NWEDYN3GRGGZQX7KUQKUV6DT67/graph.json","events_json":"https://pith.science/api/pith-number/NWEDYN3GRGGZQX7KUQKUV6DT67/events.json","paper":"https://pith.science/paper/NWEDYN3G"},"agent_actions":{"view_html":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67","download_json":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67.json","view_paper":"https://pith.science/paper/NWEDYN3G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.11281&json=true","fetch_graph":"https://pith.science/api/pith-number/NWEDYN3GRGGZQX7KUQKUV6DT67/graph.json","fetch_events":"https://pith.science/api/pith-number/NWEDYN3GRGGZQX7KUQKUV6DT67/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67/action/storage_attestation","attest_author":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67/action/author_attestation","sign_citation":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67/action/citation_signature","submit_replication":"https://pith.science/pith/NWEDYN3GRGGZQX7KUQKUV6DT67/action/replication_record"}},"created_at":"2026-07-05T00:38:45.848106+00:00","updated_at":"2026-07-05T00:38:45.848106+00:00"}