{"id":"W2067154036","doi":"10.1016/j.jcp.2014.10.058","title":"An efficient parallel immersed boundary algorithm using a pseudo-compressible fluid solver","year":2014,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Lattice Boltzmann Simulation Studies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Solver; Discretization; Convergence (economics); Projection method; Compressibility; Scaling; Algorithm; Immersed boundary method; Mathematics; Tridiagonal matrix; Boundary (topology); Computer science; Applied mathematics; Rate of convergence; Mathematical optimization; Dykstra's projection algorithm; Mathematical analysis; Geometry; Eigenvalues and eigenvectors; Physics","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":[0.0002092308,0.000155239,0.0002751055,0.00008427173,0.0001386682,0.00008388168,0.0001407972,0.00003673919,0.000009772833],"category_scores_gemma":[0.00000976574,0.0001464213,0.0001204728,0.0001582618,0.00005270049,0.0003311893,0.00002191169,0.0001716341,0.00001282709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008253118,"about_ca_system_score_gemma":0.00006205602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000159141,"about_ca_topic_score_gemma":9.954395e-8,"domain_scores_codex":[0.9987435,0.00004698585,0.0004366353,0.000097877,0.0004999111,0.0001750462],"domain_scores_gemma":[0.9990492,0.0001677431,0.0001859107,0.0001070151,0.0003955867,0.00009452511],"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":[0.00001177693,0.00007643741,0.00008120623,0.00001704913,0.0001106943,0.000003080909,0.0003302912,0.9928966,0.0008072947,0.0005282051,0.0003019453,0.004835385],"study_design_scores_gemma":[0.0008448762,0.00005350299,0.003164131,0.00003552307,0.00004592355,0.00002084014,0.00005052578,0.9850423,0.0001335998,0.01010501,0.000343827,0.0001599724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2608321,0.0001038721,0.7383878,0.00001856663,0.0003620372,0.00004646831,0.000004449695,0.00003589615,0.0002088632],"genre_scores_gemma":[0.8436731,0.000003059918,0.155532,0.00007712747,0.000675789,6.523925e-7,0.000006316343,0.00002786327,0.000004134158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5828557,"threshold_uncertainty_score":0.5970888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01846036332008204,"score_gpt":0.2736179801363382,"score_spread":0.2551576168162561,"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."}}