Reduced order framework for convection dominant and pure diffusive problems based on combination of deep long short‐term memory and proper orthogonal decomposition/dynamic mode decomposition methods

2020 ◽  
Author(s):  
Mahdi Kherad ◽  
Mohammad Kazem Moayyedi ◽  
Faranak Fotouhi
Keyword(s):  
Short Term Memory ◽  
Decomposition Methods ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Short Term ◽  
Term Memory ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Long Short Term Memory ◽  
Proper Orthogonal

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.

Analysis of dynamic stall flow field of high bypass fan rotor based on airworthiness certification

2020 ◽  
Vol 234 (11) ◽  
pp. 1757-1769
Author(s):  
Kai Zhang ◽  
AJ Wang
Keyword(s):  
Flow Field ◽  
Unsteady Flow ◽  
Field Data ◽  
Decomposition Methods ◽  
Modal Identification ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Complex Flow ◽  
Mode Decomposition ◽  
Proper Orthogonal

In order to ensure flight safety, the stall test is one of the most important steps in the airworthiness certification phase of civil aircraft. The twisted-swept fan is one of the most important components of the high bypass ratio engine. The unsteady flow field of the fan rotor stall condition is obtained by numerical simulation. At the same time, the time series flow field data of the stall condition flow field is acquired. The modal analysis of the unsteady flow field at stall condition was performed using the dynamic mode decomposition and proper orthogonal decomposition methods. Through modal identification of a large number of unsteady flow field data, the eigenvalues and corresponding modal information about the unsteady flow field change process are obtained. Finally, the evolution process of the unsteady flow field of the fan rotor under stall condition is visually demonstrated, and the coherent structures of different scales in the complex flow field under stall condition are revealed.

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