2026-09-21T11:08:48Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/121993 2024-03-01 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Fischer, Paul Fischer, Paul Olson, Luke Solomonik, Edgar Patera, Anthony 2023-12 application/pdf en text 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 Parametric model order reduction development for Navier-Stokes equations from 2D chaotic to 3D turbulent flow problems Tsai, Ping-Hsuan Reduced Order Model Parametric Model Order Reduction Model Order Reduction Turbulence Error Indicator Pod Stabilization Method Regularization Tensor Decomposition Cp Decomposition 2023-11-17 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. Text eng https://hdl.handle.net/2142/121993 Copyright 2023 Ping-Hsuan Tsai Ph.D. Dissertation Computer Science University of Illinois at Urbana-Champaign Computer Science