{"id":"W2048427570","doi":"10.1016/j.commatsci.2005.05.010","title":"Non-isothermal finite element modeling of a shape memory alloy actuator using ANSYS","year":2005,"lang":"en","type":"article","venue":"Computational Materials Science","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shape-memory alloy; Finite element method; Actuator; Materials science; Bilinear interpolation; Isothermal process; Transformation (genetics); Hysteresis; Morphing; Smart material; Computer science; Mechanical engineering; Stress (linguistics); Structural engineering; Representation (politics); Engineering; Physics; Composite material","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001530728,0.0002061197,0.0002896546,0.0002629673,0.0004067446,0.0002076414,0.0007129766,0.00004792816,0.002162646],"category_scores_gemma":[0.00008375045,0.0001987885,0.00005313793,0.000421212,0.0004243603,0.00137653,0.0001441086,0.00005397425,0.0002202625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001517606,"about_ca_system_score_gemma":0.0005521031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003318289,"about_ca_topic_score_gemma":0.000003386716,"domain_scores_codex":[0.9973344,0.00005865702,0.0007802018,0.0004340965,0.0009349835,0.0004577042],"domain_scores_gemma":[0.9988549,0.0001056596,0.0002514292,0.0002574535,0.0003934428,0.0001370521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001430654,0.00003649166,0.000006657003,0.00001991967,0.000001961139,5.607185e-7,0.0004807696,0.4910127,0.5080786,0.0002401313,0.000003193052,0.0001047147],"study_design_scores_gemma":[0.0002857388,0.00002671044,0.0002052068,0.000043215,0.000009887922,0.00001033006,0.00008013728,0.6514459,0.3475176,0.0002177399,0.000008219953,0.000149356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9387828,0.00001383363,0.05988555,0.000132746,0.0004694407,0.0003187423,0.00009413547,0.00006720787,0.0002355819],"genre_scores_gemma":[0.9037655,0.000001562529,0.09579198,0.0002261896,0.0001567602,0.00001868291,0.00001274724,0.00001800824,0.000008577275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.160561,"threshold_uncertainty_score":0.9987495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03559931882984704,"score_gpt":0.288910522118429,"score_spread":0.2533112032885819,"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."}}