Reduced Order Gust Response Simulation using Computational Fluid Dynamics

2016 ◽  
Author(s):  
Philipp Bekemeyer ◽  
Sebastian Timme
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Reduced Order ◽  
Gust Response

A methodological exploration for efficient prediction of airfoil response to gusts in wind engineering

2019 ◽  
Vol 233 (6) ◽  
pp. 738-750 ◽  
Author(s):  
Zhenlong Wu ◽  
Qiang Wang ◽  
Hao Huang
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Least Square ◽  
Efficient Estimation ◽  
Sharp Edge ◽  
Reduced Order ◽  
Efficient Prediction ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations ◽  
Gust Response

This paper presents several approaches for efficient estimation of airfoil response to gust via computational fluid dynamics and reduced-order modeling. A computational fluid dynamics code enabling simulation of aerodynamics under an arbitrary-shaped discrete gust is adopted. Convolution models using baseline sharp-edge gust response either obtained by the closed-form Küssner functions or computational fluid dynamics methods are established. A parametric approximation function model for gust response is identified via the least square optimization of the computational fluid dynamics-obtained sharp-edge responses. Finally, an example taking advantage of the aerodynamic response by the above methods to simulate the aeroelastics of an airfoil performing a plunging-twisting coupled motion under various gusts is presented. The present practice indicates that the reduced-order modelings are not only more efficient compared to direct computational fluid dynamics simulations, but also have a satisfactory accuracy in gust response predictions.

scholarly journals Updating of aerodynamic reduced order models generated using computational fluid dynamics

2017 ◽  
Vol 232 (9) ◽  
pp. 1739-1763
Author(s):  
LM Griffiths ◽  
AL Gaitonde ◽  
DP Jones ◽  
MI Friswell
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Computational Fluid Dynamics ◽  
Model Updating ◽  
Limited Range ◽  
Reduced Order Model ◽  
Reduced Order Models ◽  
Viscous Effects ◽  
Order Model ◽  
Reduced Order

Reduced order models of computational fluid dynamics codes have been developed to decrease computational costs; however, each reduced order model has a limited range of validity based on the data used in its construction. Further, like the computational fluid dynamics from which it is derived, such models exhibit differences from experimental data due to uncertainty in boundary conditions and numerical accuracy. Model updating provides the opportunity to use small amounts of additional data to modify the behaviour of a reduced order model, which means that the range of validity of the reduced order model can be extended. Whilst here computational fluid dynamics data have been used for updating, the approach offers the possibility that experimental data can be used in future. In this work, the baseline reduced order models are constructed using the Eigensystem realisation algorithm and the steps used to update these models are given in detail. The methods developed are then applied to remove the effects of wind tunnel walls and to include viscous effects.

scholarly journals A Reduced Order Kalman Filter for Computational Fluid-Dynamics Applications

2018 ◽  
Author(s):  
Carolina Introini ◽  
Stefano Lorenzi ◽  
Antonio Cammi ◽  
Davide Baroli
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Computational Fluid Dynamics ◽  
Kalman Filter ◽  
Numerical Model ◽  
Data Assimilation ◽  
Computational Cost ◽  
High Fidelity ◽  
Dimensional Manifold ◽  
Reduced Order

In the last decade, the importance of numerical simulations for the analysis of complex engineering systems, such as thermo-fluid dynamics in nuclear reactors, has grown exponentially. In spite of the large experimental databases available for validation of mathematical models, in order to identify the most suitable one for the system under investigation, the inverse integration of such data into the CFD model is nowadays an ongoing challenge. In addition, such integration could tackle the problem of propagation of epistemic uncertainties, both in the numerical model and in the experimental data. In this framework, the data-assimilation method allows for the dynamic incorporation of observations within the computational model. Perhaps the most famous among these methods, due to its simple implementation and yet robust nature, is the Kalman filter. Although this approach has found success in fields such as weather forecast and geoscience, its application in Computational Fluid-Dynamics (CFD) is still in its first stages. In this setting, a new algorithm based on the integration between the segregated approach, which is the most common method adopted by CFD applications for the solution of the incompressible Navier-Stokes equations, and a Kalman filter modified for fluid-dynamics problems, while preserving mass conservation of the solution, has already been developed and tested in a previous work. Whereas such method is able to robustly integrate experimental data within the numerical model, its computational cost increases with model complexity. In particular, in high-fidelity realistic scenarios the error covariance matrix for the model, which represents the uncertainties associated with it, becomes dense, thus affecting the efficiency and computational cost of the method. For this reason, due to the promised reduction of computational requirements recently investigated, which combines model reduction and data-assimilation, in this work a combination of reduced order model and mass-conservative Kalman filter within a segregated approach for CFD analysis is proposed. The novelty lies in the peculiar formulation of the Kalman filter and how to construct a low-dimensional manifold to approximate, with sufficient accuracy, the high fidelity model. With respect to literature, in which the full-order Kalman filter is applied to a reduced model, the reduction is performed directly on the integrated model in order to obtain a reduced-order Kalman filter already optimised for fluid-dynamics applications. In order to verify the capabilities of this approach, this reduced-order algorithm has been tested against the lid-driven cavity test case.

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