{"id":"W2990423821","doi":"10.1115/ajkfluids2019-4773","title":"Optimizing a Segmented Transmission Line Model for Dynamic Flows in Arbitrarily Tapered Pipelines","year":2019,"lang":"en","type":"article","venue":"","topic":"Thermal Analysis in Power Transmission","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Pipeline transport; Pipeline (software); Solver; Computer science; Computation; Transmission line; Electric power transmission; Line (geometry); Transmission (telecommunications); Laminar flow; Algorithm; Engineering; Mathematics; Telecommunications; Geometry; Mechanical engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002802139,0.0006127742,0.0004753962,0.0003564269,0.0002983092,0.0007051908,0.0008009945,0.0006318631,0.002152486],"category_scores_gemma":[0.0007282461,0.0003611033,0.0005371706,0.0003833517,0.0005372974,0.0007250344,0.0004236672,0.0004596338,0.0002897943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008847585,"about_ca_system_score_gemma":0.001133262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009433402,"about_ca_topic_score_gemma":0.006766104,"domain_scores_codex":[0.9998564,0.00003903089,0.000005952616,0.00002409605,0.00005316069,0.00002141168],"domain_scores_gemma":[0.9997827,0.00008883468,0.00003442477,0.00001954538,0.00006037547,0.0000140219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001030025,0.000007444998,0.00009052296,0.000009777152,0.000002438604,0.00001643425,0.000009231554,0.9956655,0.001792575,0.000976169,0.00008212057,0.001337558],"study_design_scores_gemma":[0.000001929861,0.000005832389,0.00001717855,0.000001120959,9.899996e-7,0.000002272752,0.000002007166,0.9993834,0.000303432,0.0001399853,0.0001408851,9.499166e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1176985,0.0002041118,0.8701143,0.0001755158,0.00003004517,0.00007192067,0.0002064631,0.0007419948,0.01075713],"genre_scores_gemma":[0.8936155,0.0002573832,0.09873597,0.0000372041,0.00001783684,0.0001374696,0.0002067461,0.0002171246,0.00677477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009433402,"threshold_uncertainty_score":0.01875699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008912392587202295,"score_gpt":0.2291873288128993,"score_spread":0.220274936225697,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}