Numerical Simulation and Reduced-Order Modeling of Airfoil Gust Response

17th AIAA Computational Fluid Dynamics Conference ◽

10.2514/6.2005-5128 ◽

2005 ◽

Cited By ~ 6

Author(s):

Avi Zaide ◽

Daniella Raveh

Keyword(s):

Numerical Simulation ◽

Reduced Order Modeling ◽

Reduced Order ◽

Gust Response

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Numerical Simulation and Reduced-Order Modeling of Airfoil Gust Response

AIAA Journal ◽

10.2514/1.16995 ◽

2006 ◽

Vol 44 (8) ◽

pp. 1826-1834 ◽

Cited By ~ 56

Author(s):

Avi Zaide ◽

Daniella Raveh

Keyword(s):

Numerical Simulation ◽

Reduced Order Modeling ◽

Reduced Order ◽

Gust Response

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REDUCED-ORDER MODELING OF TURBULENT FLOWS IN MULTISCALE DOMAINS

Equipment ◽

10.1615/ihtc13.trb.100 ◽

2006 ◽

Author(s):

J. Rambo ◽

Y. Joshi

Keyword(s):

Turbulent Flows ◽

Reduced Order Modeling ◽

Reduced Order

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Integrated Toolkit for Closed-Loop Flow Control: Flow Simulation, Reduced-Order Modeling, and Control Design (Invited)

38th Fluid Dynamics Conference and Exhibit ◽

10.2514/6.2008-3980 ◽

2008 ◽

Author(s):

Tim Colonius ◽

Kunihiko Taira ◽

Won Joe ◽

Clancy Rowley ◽

Sunil Ahuja

Keyword(s):

Flow Control ◽

Closed Loop ◽

Control Design ◽

Flow Simulation ◽

Reduced Order Modeling ◽

Control Flow ◽

Modeling And Control ◽

Reduced Order ◽

And Control

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Lattice Boltzmann Flow Simulations With Applications of Reduced Order Modeling Techniques.

10.2523/17457-ms ◽

2014 ◽

Author(s):

Donald L. Brown ◽

Jun Li ◽

Victor M. Calo ◽

Mehdi Ghommem ◽

Yalchin Efendiev

Keyword(s):

Lattice Boltzmann ◽

Reduced Order Modeling ◽

Reduced Order ◽

Flow Simulations ◽

Modeling Techniques

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Reduced Order Modeling for Rapid Simulations of Blast and Rollover Events of a Ground Vehicle and its Occupants Using Rigid Body Dynamic Models

10.21236/ada585048 ◽

2013 ◽

Cited By ~ 4

Author(s):

Jaisankar Ramalingam ◽

Sherri Chandra ◽

Ravi Thyagarajan

Keyword(s):

Rigid Body ◽

Dynamic Models ◽

Reduced Order Modeling ◽

Ground Vehicle ◽

Reduced Order ◽

Rigid Body Dynamic ◽

Body Dynamic

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REDUCED-ORDER MODELING FOR ESTIMATING CO2 STORAGE AND ENHANCED COALBED METHANE OF UNCONVENTIONAL COAL SEAM RESERVOIRS

10.1130/abs/2018am-323999 ◽

2018 ◽

Author(s):

Ryan Kammer ◽

◽

Kevin Ellett ◽

Richard S. Middleton ◽

Chris Korose

Keyword(s):

Coal Seam ◽

Coalbed Methane ◽

Co2 Storage ◽

Reduced Order Modeling ◽

Reduced Order ◽

Enhanced Coalbed Methane

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Research on Reduced Order Modeling Method of Power Electronic Interface Based on Hybrid System Theory

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon43393.2020.9254684 ◽

2020 ◽

Author(s):

Yijia Heng ◽

Qunying Liu ◽

Xinli Huo ◽

Shuheng Chen ◽

Fanzhang ◽

...

Keyword(s):

System Theory ◽

Hybrid System ◽

Reduced Order Modeling ◽

Modeling Method ◽

Power Electronic ◽

Reduced Order ◽

Electronic Interface

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Reduced-order Modeling and Stability Analysis of MTDC System Under Droop Control in DC Voltage Control Timescale

2020 IEEE Power & Energy Society General Meeting (PESGM) ◽

10.1109/pesgm41954.2020.9282157 ◽

2020 ◽

Author(s):

Pengfei Li ◽

Xialin Li ◽

Li Guo ◽

Zhiwang Li ◽

Chao Qin ◽

...

Keyword(s):

Stability Analysis ◽

Voltage Control ◽

Reduced Order Modeling ◽

Droop Control ◽

Reduced Order ◽

Dc Voltage ◽

Dc Voltage Control

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Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning

International Journal for Numerical Methods in Biomedical Engineering ◽

10.1002/cnm.3450 ◽

2021 ◽

Author(s):

Stefano Pagani ◽

Andrea Manzoni

Keyword(s):

Machine Learning ◽

Sensitivity Analysis ◽

Uncertainty Quantification ◽

Cardiac Electrophysiology ◽

Reduced Order Modeling ◽

Reduced Order

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Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410021999864 ◽

2021 ◽

pp. 095441002199986

Author(s):

Hassan F Ahmed ◽

Hamayun Farooq ◽

Imran Akhtar ◽

Zafar Bangash

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Stokes Equations ◽

Decomposition Analysis ◽

Hydrodynamic Forces ◽

Reduced Order Modeling ◽

Navier Stokes ◽

Reduced Order ◽

Mode Decomposition ◽

Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

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