Development and application of reduced-order neural network model based on proper orthogonal decomposition for BOD5monitoring in river systems: Uncertainty analysis

2013 ◽  
Vol 32 (2) ◽  
pp. 344-349 ◽  
Author(s):  
Roohollah Noori ◽  
Khosro Ashrafi ◽  
Abdolreza Karbassi ◽  
Mojtaba Ardestani ◽  
Naser Mehrdadi
Keyword(s):  
Neural Network ◽  
Uncertainty Analysis ◽  
Proper Orthogonal Decomposition ◽  
Network Model ◽  
Neural Network Model ◽  
Orthogonal Decomposition ◽  
River Systems ◽  
Reduced Order ◽  
Model Based ◽  
Proper Orthogonal

Nonlinear Aeroelasticity Modeling Using a Reduced Order Model Based on Proper Orthogonal Decomposition

2007 ◽  
Author(s):  
Zhengkun Feng ◽  
Azzeddine Soulaimani
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Reduced Order Model ◽  
Dynamic Responses ◽  
Cfd Model ◽  
Nonlinear Aeroelasticity ◽  
Order Model ◽  
Reduced Order ◽  
Model Based ◽  
Proper Orthogonal

Investigations of nonlinear aeroelasticity of flexible structures subjected to unsteady transonic flows were carried out by means of an aeroelasticity model coupled with a reduced order CFD model based on POD (proper orthogonal decomposition) method. The reduced order model is a three-dimensional with moving fluid boundaries. The CFD model order was reduced from more than 150000 of the full order model to 200 of the reduced order model and Limit Oscillation Cycle (LCO) was observed. The dynamic responses of the system were simulated with the coupled model. Qualitatively, the numerical simulations on AGARD 445.6 from the nonlinear aeroelasticity model coupled with the reduced order CFD model agree with those from the model coupled with the full order CFD model.

Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder

2020 ◽  
Author(s):  
Christian Amor ◽  
José M Pérez ◽  
Philipp Schlatter ◽  
Ricardo Vinuesa ◽  
Soledad Le Clainche
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Turbulent Wake ◽  
Orthogonal Decomposition ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Square Cylinder ◽  
Complex Flow ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Proper Orthogonal

Abstract This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.

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