{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CA7HDD76OC7OB77GYYT4FBXWN3","short_pith_number":"pith:CA7HDD76","schema_version":"1.0","canonical_sha256":"103e718ffe70bee0ffe6c627c286f66edad6f7b43f210c24592d3b871cc3dc5b","source":{"kind":"arxiv","id":"1904.04099","version":2},"attestation_state":"computed","paper":{"title":"Extension of the sub-grid-scale gradient model for compressible magnetohydrodynamics turbulent instabilities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"physics.flu-dyn","authors_text":"Carlos Palenzuela, Daniele Vigan\\`o, Ricard Aguilera-Miret","submitted_at":"2019-04-08T14:42:44Z","abstract_excerpt":"Performing accurate large eddy simulations in compressible, turbulent magnetohydrodynamics is more challenging than in non-magnetized fluids due to the complex interplay between kinetic, magnetic and internal energy at different scales. Here we extend the sub-grid-scale gradient model, so far used in the momentum and induction equations, to account also for the unresolved scales in the energy evolution equation of a compressible ideal MHD fluid with a generic equation of state. We assess the model by considering box simulations of the turbulence triggered across a shear layer by the Kelvin-Hel"},"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":"1904.04099","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2019-04-08T14:42:44Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"c88e6619a6e843a699958b79bdc4a780b23edec489142138eac349318324dad0","abstract_canon_sha256":"4a19f9dad2635a51c8d6fe6e241080862cd69352c5306489adad6b0091c68f69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:44.357474Z","signature_b64":"rfacsJ0QQ1Pxn8rAlsKRKUuhrFs82Nxh0kaKNNinElfkCpzc4rLr6mbFKEbyLxO6QVSrs2boL7NhBmDEcYOfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"103e718ffe70bee0ffe6c627c286f66edad6f7b43f210c24592d3b871cc3dc5b","last_reissued_at":"2026-07-05T00:13:44.357049Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:44.357049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extension of the sub-grid-scale gradient model for compressible magnetohydrodynamics turbulent instabilities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"physics.flu-dyn","authors_text":"Carlos Palenzuela, Daniele Vigan\\`o, Ricard Aguilera-Miret","submitted_at":"2019-04-08T14:42:44Z","abstract_excerpt":"Performing accurate large eddy simulations in compressible, turbulent magnetohydrodynamics is more challenging than in non-magnetized fluids due to the complex interplay between kinetic, magnetic and internal energy at different scales. Here we extend the sub-grid-scale gradient model, so far used in the momentum and induction equations, to account also for the unresolved scales in the energy evolution equation of a compressible ideal MHD fluid with a generic equation of state. We assess the model by considering box simulations of the turbulence triggered across a shear layer by the Kelvin-Hel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04099","kind":"arxiv","version":2},"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/1904.04099/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":"1904.04099","created_at":"2026-07-05T00:13:44.357108+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.04099v2","created_at":"2026-07-05T00:13:44.357108+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04099","created_at":"2026-07-05T00:13:44.357108+00:00"},{"alias_kind":"pith_short_12","alias_value":"CA7HDD76OC7O","created_at":"2026-07-05T00:13:44.357108+00:00"},{"alias_kind":"pith_short_16","alias_value":"CA7HDD76OC7OB77G","created_at":"2026-07-05T00:13:44.357108+00:00"},{"alias_kind":"pith_short_8","alias_value":"CA7HDD76","created_at":"2026-07-05T00:13:44.357108+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21659","citing_title":"Subgrid Modelling for Relativistic Magnetohydrodynamics with Machine Learning","ref_index":59,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3","json":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3.json","graph_json":"https://pith.science/api/pith-number/CA7HDD76OC7OB77GYYT4FBXWN3/graph.json","events_json":"https://pith.science/api/pith-number/CA7HDD76OC7OB77GYYT4FBXWN3/events.json","paper":"https://pith.science/paper/CA7HDD76"},"agent_actions":{"view_html":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3","download_json":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3.json","view_paper":"https://pith.science/paper/CA7HDD76","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.04099&json=true","fetch_graph":"https://pith.science/api/pith-number/CA7HDD76OC7OB77GYYT4FBXWN3/graph.json","fetch_events":"https://pith.science/api/pith-number/CA7HDD76OC7OB77GYYT4FBXWN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3/action/storage_attestation","attest_author":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3/action/author_attestation","sign_citation":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3/action/citation_signature","submit_replication":"https://pith.science/pith/CA7HDD76OC7OB77GYYT4FBXWN3/action/replication_record"}},"created_at":"2026-07-05T00:13:44.357108+00:00","updated_at":"2026-07-05T00:13:44.357108+00:00"}