{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121993"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121993","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Parametric model order reduction development for Navier-Stokes equations from 2D chaotic to 3D turbulent flow problems","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_has_math":false,"creators":["Tsai, Ping-Hsuan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Fischer, Paul","Olson, Luke","Solomonik, Edgar","Patera, Anthony"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Reduced Order Model","Parametric Model Order Reduction","Model Order Reduction","Turbulence","Error Indicator","Pod","Stabilization Method","Regularization","Tensor Decomposition","Cp Decomposition"],"languages":["en","eng"],"rights":["Copyright 2023 Ping-Hsuan Tsai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121993","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Fischer, Paul","Olson, Luke","Solomonik, Edgar","Patera, Anthony"]},{"key":"dc:creator","label":"Author","values":["Tsai, Ping-Hsuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-11-17"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Reduced Order Model","Parametric Model Order Reduction","Model Order Reduction","Turbulence","Error Indicator","Pod","Stabilization Method","Regularization","Tensor Decomposition","Cp Decomposition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Ping-Hsuan Tsai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121993"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Ping-Hsuan Tsai, accepted the attached license on 2023-11-15 at 23:55.","The student, Ping-Hsuan Tsai, submitted this Dissertation for approval on 2023-11-16 at 00:39.","This Dissertation was approved for publication on 2023-11-17 at 15:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19934 on 2024-03-01 at 13:14:35","This work presents new developments for the application of parametric model-order reduction (pMOR) for engineering thermal-fluid applications. The pMOR technique is built on a reduced order model (ROM), in which the governing thermal-fluid transport equations are approximated by a low-dimensional system of ordinary differential equations involving relatively few (N ≈ 20-200) time-dependent unknowns. Basis functions for the ROMs are derived from high-fidelity, full-order models (FOMs) typified by large-eddy simulations (LES) or direct numerical simulations (DNS) of turbulence that involve N ≈ =10^6-10^11 unknowns. The goal of pMOR is to track quantities of interest as a function of input parameters, such as Reynolds or Rayleigh number, without rerunning the FOM. This dissertation addresses several outstanding challenges in the application of pMOR to engineering problems, including: developing a time-averaged error indicator for thermal-fluids systems; improved stabilization strategies for ROM-based simulations of turbulence; and an efficient low-rank, symmetry preserving, tensor decomposition for the ROM advection operator that alleviates the leading order, O(N^3), computational complexity in time-advancement of ROMs."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Parametric model order reduction development for Navier-Stokes equations from 2D chaotic to 3D turbulent flow problems"]}]}],"canonical_facts":{"dc:contributor":["Fischer, Paul","Olson, Luke","Solomonik, Edgar","Patera, Anthony"],"dc:creator":["Tsai, Ping-Hsuan"],"dc:date":["2023-12","2023-11-17"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Ping-Hsuan Tsai, accepted the attached license on 2023-11-15 at 23:55.","The student, Ping-Hsuan Tsai, submitted this Dissertation for approval on 2023-11-16 at 00:39.","This Dissertation was approved for publication on 2023-11-17 at 15:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19934 on 2024-03-01 at 13:14:35","This work presents new developments for the application of parametric model-order reduction (pMOR) for engineering thermal-fluid applications. The pMOR technique is built on a reduced order model (ROM), in which the governing thermal-fluid transport equations are approximated by a low-dimensional system of ordinary differential equations involving relatively few (N ≈ 20-200) time-dependent unknowns. Basis functions for the ROMs are derived from high-fidelity, full-order models (FOMs) typified by large-eddy simulations (LES) or direct numerical simulations (DNS) of turbulence that involve N ≈ =10^6-10^11 unknowns. The goal of pMOR is to track quantities of interest as a function of input parameters, such as Reynolds or Rayleigh number, without rerunning the FOM. This dissertation addresses several outstanding challenges in the application of pMOR to engineering problems, including: developing a time-averaged error indicator for thermal-fluids systems; improved stabilization strategies for ROM-based simulations of turbulence; and an efficient low-rank, symmetry preserving, tensor decomposition for the ROM advection operator that alleviates the leading order, O(N^3), computational complexity in time-advancement of ROMs."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121993"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Ping-Hsuan Tsai"],"dc:subject":["Reduced Order Model","Parametric Model Order Reduction","Model Order Reduction","Turbulence","Error Indicator","Pod","Stabilization Method","Regularization","Tensor Decomposition","Cp Decomposition"],"dc:title":["Parametric model order reduction development for Navier-Stokes equations from 2D chaotic to 3D turbulent flow problems"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}