In this paper, we propose a filter-based stabilization of reduced order models (ROMs) for uncertainty quantification (UQ) of the time-dependent Navier--Stokes equations in convection-dominated regimes. Here we propose a novel high-order ROM differential filter and use it in conjunction with an evolve-filter-relax (EFR) algorithm to attenuate the numerical oscillations of standard ROMs. We also examine how stochastic collocation methods can be combined with the EFR algorithm for efficient UQ of fluid flows. We test the new framework in the numerical simulation of a two-dimensional flow past a circular cylinder with a random viscosity that yields a random Reynolds number with mean Re = 100.
Gunzburger, M., et al. "An Evolve-Filter-Relax Stabilized Reduced Order Stochastic Collocation Method for the Time-Dependent Navier--Stokes Equations." SIAM/ASA Journal on Uncertainty Quantification, vol. 7, no. 4, Oct. 2019. https://doi.org/10.1137/18m1221618
Gunzburger, M., Iliescu, T., Mohebujjaman, M., & Schneier, M. (2019). An Evolve-Filter-Relax Stabilized Reduced Order Stochastic Collocation Method for the Time-Dependent Navier--Stokes Equations. SIAM/ASA Journal on Uncertainty Quantification, 7(4). https://doi.org/10.1137/18m1221618
Gunzburger, M., Iliescu, T., Mohebujjaman, M., et al., "An Evolve-Filter-Relax Stabilized Reduced Order Stochastic Collocation Method for the Time-Dependent Navier--Stokes Equations," SIAM/ASA Journal on Uncertainty Quantification 7, no. 4 (2019), https://doi.org/10.1137/18m1221618
@article{osti_1896786,
author = {Gunzburger, M. and Iliescu, T. and Mohebujjaman, M. and Schneier, M.},
title = {An Evolve-Filter-Relax Stabilized Reduced Order Stochastic Collocation Method for the Time-Dependent Navier--Stokes Equations},
annote = {In this paper, we propose a filter-based stabilization of reduced order models (ROMs) for uncertainty quantification (UQ) of the time-dependent Navier--Stokes equations in convection-dominated regimes. Here we propose a novel high-order ROM differential filter and use it in conjunction with an evolve-filter-relax (EFR) algorithm to attenuate the numerical oscillations of standard ROMs. We also examine how stochastic collocation methods can be combined with the EFR algorithm for efficient UQ of fluid flows. We test the new framework in the numerical simulation of a two-dimensional flow past a circular cylinder with a random viscosity that yields a random Reynolds number with mean Re = 100.},
doi = {10.1137/18m1221618},
url = {https://www.osti.gov/biblio/1896786},
journal = {SIAM/ASA Journal on Uncertainty Quantification},
issn = {ISSN 2166-2525},
number = {4},
volume = {7},
place = {United States},
publisher = {Society for Industrial and Applied Mathematics (SIAM)},
year = {2019},
month = {10}}
Florida State Univ., Tallahassee, FL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); US Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF); Commonwealth Fusion Systems
Grant/Contract Number:
SC0009324
OSTI ID:
1896786
Journal Information:
SIAM/ASA Journal on Uncertainty Quantification, Journal Name: SIAM/ASA Journal on Uncertainty Quantification Journal Issue: 4 Vol. 7; ISSN 2166-2525