{"id":"W2979407507","doi":"10.3390/drones3040077","title":"Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications","year":2019,"lang":"en","type":"article","venue":"Drones","topic":"Guidance and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Propeller; Thrust; Moment (physics); Series (stratigraphy); Parametric statistics; Wind tunnel; Torque; Taylor series; Parametric model; Oblique case; Computer science; Flow (mathematics); Multinomial distribution; Applied mathematics; Control theory (sociology); Mathematics; Engineering; Artificial intelligence; Physics; Aerospace engineering; Mathematical analysis; Marine engineering; Geometry; Geology; Classical mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003029564,0.0007284786,0.0005951787,0.0006114119,0.0003958921,0.0006961302,0.001030794,0.001066197,0.002487825],"category_scores_gemma":[0.001139468,0.0005207639,0.0007312357,0.0004892955,0.0003535646,0.0007024297,0.0003319239,0.0008044051,0.0008621258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005592774,"about_ca_system_score_gemma":0.0007871403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01384047,"about_ca_topic_score_gemma":0.01536674,"domain_scores_codex":[0.9998753,0.00002102272,0.000006945163,0.00003407177,0.00004197931,0.00002072207],"domain_scores_gemma":[0.9996265,0.000213894,0.00005224614,0.00003683921,0.00005462966,0.00001597434],"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":[0.00002323175,0.00001982833,0.0004176223,0.00003604695,0.000005891297,0.00004280053,0.00001661206,0.9841405,0.001178518,0.0005178906,0.0003465733,0.01325449],"study_design_scores_gemma":[0.000002515998,0.000006838604,0.0001671622,0.000002827984,0.000001042769,0.00001068896,0.000004152585,0.9989624,0.000283506,0.0003395177,0.0002168738,0.000002589868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1282091,0.0007826991,0.8629968,0.000285431,0.00005197564,0.0001450584,0.001070487,0.001813553,0.004644916],"genre_scores_gemma":[0.8905155,0.0004880871,0.1022278,0.00006345179,0.00003886291,0.000236507,0.001482289,0.0001847837,0.004762748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01384047,"threshold_uncertainty_score":0.02751982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004942338148185106,"score_gpt":0.1856611315444989,"score_spread":0.1807187933963138,"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."}}