Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows

Dabaghian, Pedram H.; Ahmed, Shady E.; San, Omer

doi:10.1080/10618562.2022.2152014

Title: Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows

Journal Article · 09 August 2022 · International Journal of Computational Fluid Dynamics

DOI:https://doi.org/10.1080/10618562.2022.2152014· OSTI ID:1979229

Dabaghian, Pedram H. ^[1]; Ahmed, Shady E. ^[1];

^[1]

School of Mechanical & Aerospace Engineering, Oklahoma State University, Stillwater, OK, USA

Not provided.

Cite

Export

Save

Research Organization:: Oklahoma State Univ., Stillwater, OK (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0019290

OSTI ID:: 1979229

Journal Information:: International Journal of Computational Fluid Dynamics, Vol. 36, Issue 7; ISSN 1061-8562

Publisher:: Taylor & Francis

Country of Publication:: United States

Language:: English

References (57)

Dynamic mode decomposition with core sketch Ahmed, Shady E.; Dabaghian, Pedram H.; San, Omer Physics of Fluids, Vol. 34, Issue 6 https://doi.org/10.1063/5.0095163	journal	June 2022
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues Ahmed, Shady E.; Pawar, Suraj; San, Omer Physics of Fluids, Vol. 33, Issue 9 https://doi.org/10.1063/5.0061577	journal	September 2021
Breaking the Kolmogorov Barrier in Model Reduction of Fluid Flows Ahmed, Shady E.; San, Omer Fluids, Vol. 5, Issue 1 https://doi.org/10.3390/fluids5010026	journal	February 2020
Sketching Methods for Dynamic Mode Decomposition in Spherical Shallow Water Equations Ahmed, Shady E.; San, Omer; Bistrian, Diana AIAA SCITECH 2022 Forum https://doi.org/10.2514/6.2022-2325	conference	December 2022
Multifidelity computing for coupling full and reduced order models Ahmed, Shady E.; San, Omer; Kara, Kursat PLOS ONE, Vol. 16, Issue 2 https://doi.org/10.1371/journal.pone.0246092	journal	February 2021
Nonlinear proper orthogonal decomposition for convection-dominated flows Ahmed, Shady E.; San, Omer; Rasheed, Adil Physics of Fluids, Vol. 33, Issue 12 https://doi.org/10.1063/5.0074310	journal	December 2021
Missing Point Estimation in Models Described by Proper Orthogonal Decomposition Astrid, Patricia; Weiland, Siep; Willcox, Karen IEEE Transactions on Automatic Control, Vol. 53, Issue 10 https://doi.org/10.1109/TAC.2008.2006102	journal	November 2008
Application of reduced-order models based on PCA & Kriging for the development of digital twins of reacting flow applications Aversano, Gianmarco; Bellemans, Aurélie; Li, Zhiyi Computers & Chemical Engineering, Vol. 121 https://doi.org/10.1016/j.compchemeng.2018.09.022	journal	February 2019
An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations Barrault, Maxime; Maday, Yvon; Nguyen, Ngoc Cuong Comptes Rendus Mathematique, Vol. 339, Issue 9 https://doi.org/10.1016/j.crma.2004.08.006	journal	November 2004
A perspective on machine learning methods in turbulence modeling Beck, Andrea; Kurz, Marius GAMM-Mitteilungen, Vol. 44, Issue 1 https://doi.org/10.1002/gamm.202100002	journal	March 2021
Randomized dynamic mode decomposition for nonintrusive reduced order modelling: RANDOMIZED DYNAMIC MODE DECOMPOSITION FOR NIROM Bistrian, Diana Alina; Navon, Ionel Michael International Journal for Numerical Methods in Engineering, Vol. 112, Issue 1 https://doi.org/10.1002/nme.5499	journal	February 2017
Machine Learning for Fluid Mechanics Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos, Petros Annual Review of Fluid Mechanics, Vol. 52, Issue 1 https://doi.org/10.1146/annurev-fluid-010719-060214	journal	September 2019
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space Bui-Thanh, T.; Willcox, K.; Ghattas, O. SIAM Journal on Scientific Computing, Vol. 30, Issue 6 https://doi.org/10.1137/070694855	journal	January 2008
Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations Carlberg, Kevin; Bou-Mosleh, Charbel; Farhat, Charbel International Journal for Numerical Methods in Engineering, Vol. 86, Issue 2 https://doi.org/10.1002/nme.3050	journal	October 2010
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows Carlberg, Kevin; Farhat, Charbel; Cortial, Julien Journal of Computational Physics, Vol. 242 https://doi.org/10.1016/j.jcp.2013.02.028	journal	June 2013
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning Carlberg, Kevin T.; Jameson, Antony; Kochenderfer, Mykel J. Journal of Computational Physics, Vol. 395 https://doi.org/10.1016/j.jcp.2019.05.041	journal	October 2019
Nonlinear Model Reduction via Discrete Empirical Interpolation Chaturantabut, Saifon; Sorensen, Danny C. SIAM Journal on Scientific Computing, Vol. 32, Issue 5 https://doi.org/10.1137/090766498	journal	January 2010
Greedy Nonintrusive Reduced Order Model for Fluid Dynamics Chen, Wang; Hesthaven, Jan S.; Junqiang, Bai AIAA Journal, Vol. 56, Issue 12 https://doi.org/10.2514/1.J056161	journal	December 2018
pyNIROM—A suite of python modules for non-intrusive reduced order modeling of time-dependent problems Dutta, Sourav; Rivera-Casillas, Peter; Cecil, Orie M. Software Impacts, Vol. 10 https://doi.org/10.1016/j.simpa.2021.100129	journal	November 2021
Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations Grepl, Martin A.; Maday, Yvon; Nguyen, Ngoc C. ESAIM: Mathematical Modelling and Numerical Analysis, Vol. 41, Issue 3 https://doi.org/10.1051/m2an:2007031	journal	May 2007
On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows Grimberg, Sebastian; Farhat, Charbel; Youkilis, Noah Journal of Computational Physics, Vol. 419 https://doi.org/10.1016/j.jcp.2020.109681	journal	October 2020
Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction Hampton, Jerrad; Fairbanks, Hillary R.; Narayan, Akil Journal of Computational Physics, Vol. 368 https://doi.org/10.1016/j.jcp.2018.04.015	journal	September 2018
An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes Heaney, Claire E.; Wolffs, Zef; Tómasson, Jón Atli Physics of Fluids, Vol. 34, Issue 5 https://doi.org/10.1063/5.0088070	journal	May 2022
Certified Reduced Basis Methods for Parametrized Partial Differential Equations Hesthaven, Jan S.; Rozza, Gianluigi; Stamm, Benjamin SpringerBriefs in Mathematics https://doi.org/10.1007/978-3-319-22470-1	book	January 2016
Nonlinear principal component analysis of noisy data Hsieh, William W. Neural Networks, Vol. 20, Issue 4 https://doi.org/10.1016/j.neunet.2007.04.018	journal	May 2007
Sparsity-promoting dynamic mode decomposition Jovanović, Mihailo R.; Schmid, Peter J.; Nichols, Joseph W. Physics of Fluids, Vol. 26, Issue 2 https://doi.org/10.1063/1.4863670	journal	February 2014
A probabilistic graphical model foundation for enabling predictive digital twins at scale Kapteyn, Michael G.; Pretorius, Jacob V. R.; Willcox, Karen E. Nature Computational Science, Vol. 1, Issue 5 https://doi.org/10.1038/s43588-021-00069-0	journal	May 2021
Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction Katrutsa, Aleksandr; Utyuzhnikov, Sergey; Oseledets, Ivan Journal of Computational Physics, Vol. 476 https://doi.org/10.1016/j.jcp.2023.111913	journal	March 2023
Nonlinear principal component analysis using autoassociative neural networks Kramer, Mark A. AIChE Journal, Vol. 37, Issue 2 https://doi.org/10.1002/aic.690370209	journal	February 1991
Dynamic Mode Decomposition Kutz, J. Nathan; Brunton, Steven L.; Brunton, Bingni W. Society for Industrial and Applied Mathematics https://doi.org/10.1137/1.9781611974508	book	January 2016
A parametric and feasibility study for data sampling of the dynamic mode decomposition: Spectral insights and further explorations Li, Cruz Y.; Chen, Zengshun; Tse, Tim K. T. Physics of Fluids, Vol. 34, Issue 3 https://doi.org/10.1063/5.0082640	journal	March 2022
Nonlinear Principal Component Analysis by Neural Networks: Theory and Application to the Lorenz System Monahan, Adam H. Journal of Climate, Vol. 13, Issue 4 https://doi.org/10.1175/1520-0442(2000)013<0821:NPCABN>2.0.CO;2	journal	February 2000
Automating turbulence modelling by multi-agent reinforcement learning Novati, Guido; de Laroussilhe, Hugues Lascombes; Koumoutsakos, Petros Nature Machine Intelligence, Vol. 3, Issue 1 https://doi.org/10.1038/s42256-020-00272-0	journal	January 2021
Linearly Recurrent Autoencoder Networks for Learning Dynamics Otto, Samuel E.; Rowley, Clarence W. SIAM Journal on Applied Dynamical Systems, Vol. 18, Issue 1 https://doi.org/10.1137/18M1177846	journal	January 2019
A deep learning enabler for nonintrusive reduced order modeling of fluid flows Pawar, S.; Rahman, S. M.; Vaddireddy, H. Physics of Fluids, Vol. 31, Issue 8 https://doi.org/10.1063/1.5113494	journal	August 2019
Data-driven operator inference for nonintrusive projection-based model reduction Peherstorfer, Benjamin; Willcox, Karen Computer Methods in Applied Mechanics and Engineering, Vol. 306 https://doi.org/10.1016/j.cma.2016.03.025	journal	July 2016
An autoencoder‐based reduced‐order model for eigenvalue problems with application to neutron diffusion Phillips, Toby R. F.; Heaney, Claire E.; Smith, Paul N. International Journal for Numerical Methods in Engineering, Vol. 122, Issue 15 https://doi.org/10.1002/nme.6681	journal	May 2021
Deep Multilayer Convolution Frameworks for Data-Driven Learning of Fluid flow Dynamics Puligilla, Shivakanth Chary; Jayaraman, Balaji 2018 Fluid Dynamics Conference https://doi.org/10.2514/6.2018-3091	conference	June 2018
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective Rasheed, Adil; San, Omer; Kvamsdal, Trond IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2970143	journal	January 2020
Model order reduction for nonlinear dynamical systems based on trajectory piecewise-linear approximations Rewieński, Michał; White, Jacob Linear Algebra and its Applications, Vol. 415, Issue 2-3 https://doi.org/10.1016/j.laa.2003.11.034	journal	June 2006
Dynamic mode decomposition as an analysis tool for time-dependent partial differential equations Rot, Miha; Horvat, Martin; Kosec, Gregor 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) https://doi.org/10.23919/SpliTech55088.2022.9854243	conference	July 2022
Principal interval decomposition framework for POD reduced-order modeling of convective Boussinesq flows: PRINCIPAL INTERVAL DECOMPOSITION MODEL REDUCTION FRAMEWORK San, O.; Borggaard, J. International Journal for Numerical Methods in Fluids, Vol. 78, Issue 1 https://doi.org/10.1002/fld.4006	journal	February 2015
A stabilized proper orthogonal decomposition reduced-order model for large scale quasigeostrophic ocean circulation San, Omer; Iliescu, Traian Advances in Computational Mathematics, Vol. 41, Issue 5 https://doi.org/10.1007/s10444-015-9417-0	journal	May 2015
POD/DEIM nonlinear model order reduction of an ADI implicit shallow water equations model Ştefănescu, R.; Navon, I. M. Journal of Computational Physics, Vol. 237 https://doi.org/10.1016/j.jcp.2012.11.035	journal	March 2013
Modal Analysis of Fluid Flows: Applications and Outlook Taira, Kunihiko; Hemati, Maziar S.; Brunton, Steven L. AIAA Journal, Vol. 58, Issue 3 https://doi.org/10.2514/1.J058462	journal	March 2020
The role of artificial intelligence in achieving the Sustainable Development Goals Vinuesa, Ricardo; Azizpour, Hossein; Leite, Iolanda Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-019-14108-y	journal	January 2020
Multiscale simulations of complex systems by learning their effective dynamics Vlachas, Pantelis R.; Arampatzis, Georgios; Uhler, Caroline Nature Machine Intelligence, Vol. 4, Issue 4 https://doi.org/10.1038/s42256-022-00464-w	journal	April 2022
Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem Wang, Qian; Hesthaven, Jan S.; Ray, Deep Journal of Computational Physics, Vol. 384 https://doi.org/10.1016/j.jcp.2019.01.031	journal	May 2019
Data-driven identification of dynamical models using adaptive parameter sets Wilson, Dan Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 32, Issue 2 https://doi.org/10.1063/5.0077447	journal	February 2022
Non-linear model reduction for the Navier–Stokes equations using residual DEIM method Xiao, D.; Fang, F.; Buchan, A. G. Journal of Computational Physics, Vol. 263 https://doi.org/10.1016/j.jcp.2014.01.011	journal	April 2014
Non-intrusive reduced order modelling of the Navier–Stokes equations Xiao, D.; Fang, F.; Buchan, A. G. Computer Methods in Applied Mechanics and Engineering, Vol. 293 https://doi.org/10.1016/j.cma.2015.05.015	journal	August 2015
Non-intrusive reduced-order modelling of the Navier-Stokes equations based on RBF interpolation: Non-intrusive reduced-order modelling of the Navier-Stokes equations based on RBF interpolation Xiao, D.; Fang, F.; Pain, C. International Journal for Numerical Methods in Fluids, Vol. 79, Issue 11 https://doi.org/10.1002/fld.4066	journal	July 2015
Towards non-intrusive reduced order 3D free surface flow modelling Xiao, D.; Fang, F.; Pain, C. C. Ocean Engineering, Vol. 140 https://doi.org/10.1016/j.oceaneng.2017.05.020	journal	August 2017
A domain decomposition non-intrusive reduced order model for turbulent flows Xiao, D.; Heaney, C. E.; Fang, F. Computers & Fluids, Vol. 182 https://doi.org/10.1016/j.compfluid.2019.02.012	journal	March 2019
Non-intrusive reduced order modelling of fluid–structure interactions Xiao, D.; Yang, P.; Fang, F. Computer Methods in Applied Mechanics and Engineering, Vol. 303 https://doi.org/10.1016/j.cma.2015.12.029	journal	May 2016
A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting Xiao, D.; Yang, P.; Fang, F. Journal of Computational Physics, Vol. 330 https://doi.org/10.1016/j.jcp.2016.10.068	journal	February 2017
Non-intrusive reduced-order modeling for fluid problems: A brief review Yu, Jian; Yan, Chao; Guo, Mengwu Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 233, Issue 16 https://doi.org/10.1177/0954410019890721	journal	December 2019

Similar Records

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

Journal Article · 2019 · Physics of Fluids · OSTI ID:1979229

Pawar, S.; Rahman, S. M.; Vaddireddy, H.; +3 more

Sampling and resolution characteristics in reduced order models of shallow water equations: Intrusive vs nonintrusive

Journal Article · 2020 · International Journal for Numerical Methods in Fluids · OSTI ID:1979229

Ahmed, Shady E.; San, Omer; Bistrian, Diana A.; +1 more

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence

Journal Article · 2019 · Physical Review E · OSTI ID:1979229

Rahman, Sk. M.; Pawar, S.; San, O.; +2 more

Related Subjects

Mechanics
Physics

Title: Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows

Citation Formats

References (57)

Similar Records

Related Subjects