scholarly journals Reduced-order modeling based on POD of a parabolized Navier-Stokes equation model I: forward model

2011 ◽  
Vol 69 (3) ◽  
pp. 710-730 ◽  
Author(s):  
J. Du ◽  
I.M. Navon ◽  
J.L. Steward ◽  
A.K. Alekseev ◽  
Z. Luo
Keyword(s):  
Stokes Equation ◽  
Equation Model ◽  
Reduced Order Modeling ◽  
Forward Model ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Reduced Order

Numerical simulations using conserved wave absorption applied to Navier–Stokes equation model

2015 ◽  
Vol 99 ◽  
pp. 15-25 ◽  
Author(s):  
Zhe Hu ◽  
Wenyong Tang ◽  
Hongxiang Xue ◽  
Xiaoying Zhang ◽  
Jinting Guo
Keyword(s):  
Numerical Simulations ◽  
Stokes Equation ◽  
Equation Model ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Wave Absorption

Two-Dimensional "Poor Man's Navier-Stokes Equation" Model of Turbulent Flows

AIAA Journal ◽  
2003 ◽  
Vol 41 (9) ◽  
pp. 1690-1696 ◽  
Author(s):  
Tianliang Yang ◽  
J. M. McDonough ◽  
J. D. Jacob
Keyword(s):  
Turbulent Flows ◽  
Stokes Equation ◽  
Equation Model ◽  
Navier Stokes ◽  
Two Dimensional ◽  
Navier Stokes Equation

Wavelet-distributed approximating functional method for solving the Navier-Stokes equation

1998 ◽  
Vol 115 (1) ◽  
pp. 18-24 ◽  
Author(s):  
G.W. Wei ◽  
D.S. Zhang ◽  
S.C. Althorpe ◽  
D.J. Kouri ◽  
D.K. Hoffman
Keyword(s):  
Stokes Equation ◽  
Functional Method ◽  
Navier Stokes ◽  
Navier Stokes Equation

scholarly journals Generalized Navier–Stokes Equations and Dynamics of Plane Molecular Media

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 288
Author(s):  
Alexei Kushner ◽  
Valentin Lychagin
Keyword(s):  
Carbon Dioxide ◽  
Internal Structure ◽  
Stokes Equation ◽  
Hydrogen Cyanide ◽  
Stokes Equations ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Navier Stokes Equations

The first analysis of media with internal structure were done by the Cosserat brothers. Birkhoff noted that the classical Navier–Stokes equation does not fully describe the motion of water. In this article, we propose an approach to the dynamics of media formed by chiral, planar and rigid molecules and propose some kind of Navier–Stokes equations for their description. Examples of such media are water, ozone, carbon dioxide and hydrogen cyanide.

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

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.

Sign in / Sign up

Export Citation Format

Share Document