{"id":"W3118888785","doi":"10.2514/6.2021-0620","title":"Topology Optimization of a Section of a Morphing Serpentine Aircraft Inlet","year":2021,"lang":"en","type":"article","venue":"AIAA Scitech 2021 Forum","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Queen's University","funders":"","keywords":"Morphing; Topology optimization; Airframe; Aerodynamics; Aerospace engineering; Inlet; Computer science; Shape optimization; Mechanical engineering; Propulsion; Engineering; Topology (electrical circuits); Structural engineering; Finite element method","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":[0.000264494,0.0004556769,0.0003091151,0.0004899486,0.0002594356,0.0005764263,0.000305676,0.0006430629,0.004112356],"category_scores_gemma":[0.000442296,0.0002436339,0.0004626045,0.0001914789,0.0003086863,0.0003007132,0.0003455053,0.0003288767,0.0004456643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003889559,"about_ca_system_score_gemma":0.0003826981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009448693,"about_ca_topic_score_gemma":0.001892331,"domain_scores_codex":[0.9999192,0.0000154227,0.000003402035,0.00001874901,0.00002544812,0.00001779235],"domain_scores_gemma":[0.9998394,0.00006282269,0.00002491715,0.00001713096,0.00003543026,0.00002027343],"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":[0.0001213272,0.00006301911,0.001331228,0.0001348078,0.00002245071,0.0003695646,0.00007002715,0.9288485,0.03620408,0.003359497,0.0007241129,0.02875142],"study_design_scores_gemma":[0.00002319449,0.00058362,0.002287195,0.00003711602,0.00003255155,0.000192212,0.0001517185,0.9641131,0.02541591,0.001760668,0.005381171,0.00002152942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7533435,0.0003135393,0.1973983,0.0001923047,0.00008335754,0.0001654962,0.0005336116,0.000334956,0.04763493],"genre_scores_gemma":[0.9417216,0.0001229176,0.05201907,0.0000229226,0.000005890846,0.00006539121,0.0002350452,0.00006612656,0.00574111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004112356,"threshold_uncertainty_score":0.01375723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004969409271118003,"score_gpt":0.2046069779594935,"score_spread":0.1996375686883755,"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."}}