{"id":"W3108566704","doi":"10.1016/j.ijsolstr.2020.11.012","title":"An efficient multi-scale computation of the macroscopic coefficient of thermal expansion: Application to the Resin Transfer Molding manufactured 3D woven composites","year":2020,"lang":"en","type":"article","venue":"International Journal of Solids and Structures","topic":"Composite Material Mechanics","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal expansion; Homogenization (climate); Materials science; Composite material; Thermoelastic damping; Composite number; Thermal; Transfer molding; Computation; Isothermal process; Molding (decorative); Thermomechanical analysis; Mathematics; Thermodynamics; Mold; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0000779472,0.00008575658,0.0001393883,0.00004460969,0.00003793107,0.00003700054,0.000306121,0.00003196163,0.000007568426],"category_scores_gemma":[0.000006154139,0.00005242249,0.00004799879,0.00006535371,0.00002775208,0.0000337963,0.0000346843,0.00009489112,1.579844e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002207721,"about_ca_system_score_gemma":0.000009524947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007797262,"about_ca_topic_score_gemma":0.000004327915,"domain_scores_codex":[0.9992127,0.00003487716,0.0003217025,0.00007263216,0.0002928256,0.00006529575],"domain_scores_gemma":[0.999639,0.000024279,0.00008590853,0.00007215747,0.0001338641,0.00004483105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005441277,0.000009894498,0.0000682896,0.00001346885,0.00002128738,4.485029e-7,0.001558047,0.5194001,0.4759887,0.00006189936,0.000004516005,0.002818937],"study_design_scores_gemma":[0.0003964333,0.00008816706,0.02257504,0.00005584821,0.00001987925,0.00001399282,0.0001330334,0.4876799,0.4888874,0.000045276,0.00004853384,0.0000564636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8333791,0.0001079587,0.1656268,0.0003407696,0.0003828213,0.0001244256,0.00002719781,0.000006952086,0.000003927268],"genre_scores_gemma":[0.9975398,0.000005910734,0.002218886,0.00009995059,0.0001218765,0.000001228819,0.000002454002,0.000009685914,2.28989e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1641607,"threshold_uncertainty_score":0.2137727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008070159743469986,"score_gpt":0.2459855551398034,"score_spread":0.2379153953963334,"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."}}