{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/88070"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/88070","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Accelerating induction machine finite-element simulation with parallel processing","abstract":"Finite element analysis used for detailed electromagnetic analysis and design of electric machines is computationally intensive. A means of accelerating two-dimensional transient finite element analysis, required for induction machine modeling, is explored using graphical processing units (GPUs) for parallel processing. The graphical processing units, widely used for image processing, can provide faster computation times than CPUs alone due to the thousands of small processors that comprise the GPUs. Computations that are suitable for parallel processing using GPUs are calculations that can be decomposed into subsections that are independent and can be computed in parallel and reassembled. The steps and components of the transient finite element simulation are analyzed to determine if using GPUs for calculations can speed up the simulation. The dominant steps of the finite element simulation are preconditioner formation, computation of the sparse iterative solution, and matrix-vector multiplication for magnetic flux density calculation. Due to the sparsity of the finite element problem, GPU-implementation of the sparse iterative solution did not result in faster computation times. The dominant speed-up achieved using the GPUs resulted from matrix-vector multiplication. Simulation results for a benchmark nonlinear magnetic material transient eddy current problem and linear magnetic material transient linear induction machine problem are presented. The finite element analysis program is implemented with MATLAB R2014a to compare sparse matrix format computations to readily available GPU matrix and vector formats and Compute Unified Device Architecture (CUDA) functions linked to MATLAB. Overall speed-up achieved for the simulations resulted in 1.2-3.5 times faster computation of the finite element solution using a hybrid CPU/GPU implementation over the CPU-only implementation. The variation in speed-up is dependent on the sparsity and number of unknowns of the problem.","abstract_html":"Finite element analysis used for detailed electromagnetic analysis and design of electric machines is computationally intensive. A means of accelerating two-dimensional transient finite element analysis, required for induction machine modeling, is explored using graphical processing units (GPUs) for parallel processing. The graphical processing units, widely used for image processing, can provide faster computation times than CPUs alone due to the thousands of small processors that comprise the GPUs. Computations that are suitable for parallel processing using GPUs are calculations that can be decomposed into subsections that are independent and can be computed in parallel and reassembled. The steps and components of the transient finite element simulation are analyzed to determine if using GPUs for calculations can speed up the simulation. The dominant steps of the finite element simulation are preconditioner formation, computation of the sparse iterative solution, and matrix-vector multiplication for magnetic flux density calculation. Due to the sparsity of the finite element problem, GPU-implementation of the sparse iterative solution did not result in faster computation times. The dominant speed-up achieved using the GPUs resulted from matrix-vector multiplication. Simulation results for a benchmark nonlinear magnetic material transient eddy current problem and linear magnetic material transient linear induction machine problem are presented. The finite element analysis program is implemented with MATLAB R2014a to compare sparse matrix format computations to readily available GPU matrix and vector formats and Compute Unified Device Architecture (CUDA) functions linked to MATLAB. Overall speed-up achieved for the simulations resulted in 1.2-3.5 times faster computation of the finite element solution using a hybrid CPU/GPU implementation over the CPU-only implementation. The variation in speed-up is dependent on the sparsity and number of unknowns of the problem.","abstract_has_math":false,"creators":["Ross, Christine Anne Haines"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Krein, Philip T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-29T20:38:34Z","date_published":"2015-09-29T20:38:34Z","updated_at":"2026-07-22T22:26:31Z","subjects":["finite element","simulation","finite","element","MATLAB","Graphics Processing Unit (GPU)","parallel","processing","linear","nonlinear","transient","eddy current","eddy","induction","Machine","induction machine","electrical machine","speedup","electromagnetic","Compute Unified Device Architecture (CUDA)","sparse matrix-vector multiplication","Sparse Matrix-vector Multiply (SpMV)","Krylov","iterative solver","Finite Element Method (FEM)","Finite Element Analysis (FEA)","Galerkin"],"languages":["en"],"rights":["Copyright 2015 Christine Ross"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/88070","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Krein, Philip T."]},{"key":"dc:creator","label":"Author","values":["Ross, Christine Anne Haines"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-29T20:38:34Z","2015-08","2015-07-17","2015-8"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["finite element","simulation","finite","element","MATLAB","Graphics Processing Unit (GPU)","parallel","processing","linear","nonlinear","transient","eddy current","eddy","induction","Machine","induction machine","electrical machine","speedup","electromagnetic","Compute Unified Device Architecture (CUDA)","sparse matrix-vector multiplication","Sparse Matrix-vector Multiply (SpMV)","Krylov","iterative solver","Finite Element Method (FEM)","Finite Element Analysis (FEA)","Galerkin"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Christine Ross"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/88070"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Finite element analysis used for detailed electromagnetic analysis and design of electric machines is computationally intensive. A means of accelerating two-dimensional transient finite element analysis, required for induction machine modeling, is explored using graphical processing units (GPUs) for parallel processing. The graphical processing units, widely used for image processing, can provide faster computation times than CPUs alone due to the thousands of small processors that comprise the GPUs. Computations that are suitable for parallel processing using GPUs are calculations that can be decomposed into subsections that are independent and can be computed in parallel and reassembled. The steps and components of the transient finite element simulation are analyzed to determine if using GPUs for calculations can speed up the simulation. The dominant steps of the finite element simulation are preconditioner formation, computation of the sparse iterative solution, and matrix-vector multiplication for magnetic flux density calculation. Due to the sparsity of the finite element problem, GPU-implementation of the sparse iterative solution did not result in faster computation times. The dominant speed-up achieved using the GPUs resulted from matrix-vector multiplication. Simulation results for a benchmark nonlinear magnetic material transient eddy current problem and linear magnetic material transient linear induction machine problem are presented. The finite element analysis program is implemented with MATLAB R2014a to compare sparse matrix format computations to readily available GPU matrix and vector formats and Compute Unified Device Architecture (CUDA) functions linked to MATLAB. Overall speed-up achieved for the simulations resulted in 1.2-3.5 times faster computation of the finite element solution using a hybrid CPU/GPU implementation over the CPU-only implementation. The variation in speed-up is dependent on the sparsity and number of unknowns of the problem.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-09-29 without embargo terms","The student, Christine Ross, accepted the attached license on 2015-07-17 at 10:55.","The student, Christine Ross, submitted this Thesis for approval on 2015-07-17 at 11:12.","This Thesis was approved for publication on 2015-07-17 at 12:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8523 on 2015-09-29 at 13:23:01","Made available in DSpace on 2015-09-29T20:38:34Z (GMT). No. of bitstreams: 2 ROSS-THESIS-2015.pdf: 5742570 bytes, checksum: f59bfbce6a5700935810995367009188 (MD5) LICENSE.txt: 4211 bytes, checksum: 0018d9915134416dcfcf1bd831a0e6e9 (MD5) Previous issue date: 2015-07-17"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Accelerating induction machine finite-element simulation with parallel processing"]}]}],"canonical_facts":{"dc:contributor":["Krein, Philip T."],"dc:creator":["Ross, Christine Anne Haines"],"dc:date":["2015-09-29T20:38:34Z","2015-08","2015-07-17","2015-8"],"dc:description":["Finite element analysis used for detailed electromagnetic analysis and design of electric machines is computationally intensive. A means of accelerating two-dimensional transient finite element analysis, required for induction machine modeling, is explored using graphical processing units (GPUs) for parallel processing. The graphical processing units, widely used for image processing, can provide faster computation times than CPUs alone due to the thousands of small processors that comprise the GPUs. Computations that are suitable for parallel processing using GPUs are calculations that can be decomposed into subsections that are independent and can be computed in parallel and reassembled. The steps and components of the transient finite element simulation are analyzed to determine if using GPUs for calculations can speed up the simulation. The dominant steps of the finite element simulation are preconditioner formation, computation of the sparse iterative solution, and matrix-vector multiplication for magnetic flux density calculation. Due to the sparsity of the finite element problem, GPU-implementation of the sparse iterative solution did not result in faster computation times. The dominant speed-up achieved using the GPUs resulted from matrix-vector multiplication. Simulation results for a benchmark nonlinear magnetic material transient eddy current problem and linear magnetic material transient linear induction machine problem are presented. The finite element analysis program is implemented with MATLAB R2014a to compare sparse matrix format computations to readily available GPU matrix and vector formats and Compute Unified Device Architecture (CUDA) functions linked to MATLAB. Overall speed-up achieved for the simulations resulted in 1.2-3.5 times faster computation of the finite element solution using a hybrid CPU/GPU implementation over the CPU-only implementation. The variation in speed-up is dependent on the sparsity and number of unknowns of the problem.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-09-29 without embargo terms","The student, Christine Ross, accepted the attached license on 2015-07-17 at 10:55.","The student, Christine Ross, submitted this Thesis for approval on 2015-07-17 at 11:12.","This Thesis was approved for publication on 2015-07-17 at 12:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8523 on 2015-09-29 at 13:23:01","Made available in DSpace on 2015-09-29T20:38:34Z (GMT). No. of bitstreams: 2 ROSS-THESIS-2015.pdf: 5742570 bytes, checksum: f59bfbce6a5700935810995367009188 (MD5) LICENSE.txt: 4211 bytes, checksum: 0018d9915134416dcfcf1bd831a0e6e9 (MD5) Previous issue date: 2015-07-17"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/88070"],"dc:language":["en"],"dc:rights":["Copyright 2015 Christine Ross"],"dc:subject":["finite element","simulation","finite","element","MATLAB","Graphics Processing Unit (GPU)","parallel","processing","linear","nonlinear","transient","eddy current","eddy","induction","Machine","induction machine","electrical machine","speedup","electromagnetic","Compute Unified Device Architecture (CUDA)","sparse matrix-vector multiplication","Sparse Matrix-vector Multiply (SpMV)","Krylov","iterative solver","Finite Element Method (FEM)","Finite Element Analysis (FEA)","Galerkin"],"dc:title":["Accelerating induction machine finite-element simulation with parallel processing"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:31Z"}