A Study on the Use of Reduced Order Models for Unsteady Fluid Dynamic Analysis of Flexible Structures

2012 ◽  
Author(s):  
L. Ebrahimnejad ◽  
H. Yadollahi Farsani ◽  
D. T. Valentine ◽  
K. D. Janoyan ◽  
P. Marzocca
Keyword(s):  
Dynamic Analysis ◽  
Cross Sections ◽  
Fluid Dynamic ◽  
Flexible Structures ◽  
Reduced Order Models ◽  
Computationally Efficient ◽  
Reduced Order ◽  
Long Span ◽  
Long Span Bridge ◽  
Unsteady Fluid

Reduced order models (ROMs) are computationally efficient techniques, which have been widely used for predicting unsteady aerodynamic response of airfoils and wings. However, they have not been applied extensively to perform unsteady fluid dynamic analysis of flexible structures in civil engineering. This paper discusses the application of reduced order computational fluid dynamics (CFD) model based on the eigensystem realization algorithm (ERA) in the aerodynamic analysis of flexible structures with arbitrary shaped cross sections. As an example of a civil structure we examine the GBB long-span bridge for which there are published experimental data. The aerodynamic impulse responses of the GBB Bridge are used to construct the ROM, and then the aerodynamic forces due to arbitrary inputs are evaluated and compared to those of the model coupled with an advanced CFD code. Results demonstrate reasonable prediction power and high computational efficiency of the technique that can serve for preliminary design, optimization and control purposes. The methodology described in this paper has wide application in many offshore engineering problems where flexible structures interact with unsteady fluid mechanical phenomena.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

scholarly journals Reduced Order Models for the Nonlinear Dynamic Analysis of Shells

2016 ◽  
Vol 19 ◽  
pp. 118-125 ◽  
Author(s):  
Paulo B. Gonçalves ◽  
Frederico M.A. Silva ◽  
Zenón J.G.N. Del Prado
Keyword(s):  
Dynamic Analysis ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Analysis ◽  
Reduced Order Models ◽  
Reduced Order

scholarly journals Comparative Study of Blades Reduced Order Models With Geometrical Nonlinearities and Contact Interfaces

2020 ◽  
Author(s):  
Elise Delhez ◽  
Florence Nyssen ◽  
Jean-Claude Golinval ◽  
Alain Batailly
Keyword(s):  
Finite Element ◽  
Degrees Of Freedom ◽  
Performance Indicator ◽  
Element Model ◽  
Reduced Order Models ◽  
Computationally Efficient ◽  
Reduced Order ◽  
Reduction Methods ◽  
Blade Tip ◽  
Reduced Space

Abstract This paper investigates the use of different model reduction methods accounting for geometric nonlinearities. These methods are adapted to retain physical degrees-of-freedom in the reduced space in order to ease contact treatment. These reduction methods are applied to a 3D finite element model of an industrial compressor blade (NASA rotor 37). In order to compare the different reduction methods, a scalar indicator is defined. This performance indicator allows to quantify the accuracy of the predicted displacement both locally (at the blade tip) and globally. The robustness of each method with respect to variations of the external excitation is also assessed. The performances of the reduction methods are then compared in the case of frictional contact between the blade tip and the surrounding casing. This work brings evidence that reduced order models provide a computationally efficient alternative to full order finite element models for the accurate prediction of the time response of structures with both distributed and localized nonlinearities.

Efficient Predictions of Unsteady Viscous Flows Around Bluff Bodies by Aerodynamic Reduced Order Models

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
L. Ebrahimnejad ◽  
K. D. Janoyan ◽  
H. Yadollahi Farsani ◽  
D. T. Valentine ◽  
P. Marzocca
Keyword(s):  
Flexible Structures ◽  
Bluff Bodies ◽  
Aerodynamic Coefficients ◽  
Reduced Order ◽  
Offshore Engineering ◽  
Unsteady Fluid Flow ◽  
Sinusoidal Input ◽  
Computational Fluid Dynamics Cfd ◽  
Unsteady Fluid ◽  
Great Belt

This paper describes an efficient reduced order model (ROM) applied in the aerodynamic analysis of bluff bodies. The proposed method, which is based on eigensystem realization algorithm (ERA), uses the impulse response of the system obtained by computational fluid dynamics (CFD) analysis to construct a ROM that can accurately predict the response of the system to any arbitrary input. In order to study the performance of the proposed technique, three different geometries including elliptical and rectangular sections as well as the deck cross section of Great Belt Bridge (GBB) were considered. The aerodynamic coefficients of the impulse responses of the three sections are used to construct the corresponding ROM for each section. Then, the aerodynamic coefficients from an arbitrary sinusoidal input obtained by CFD are compared with the predicted one using the ROM. The results presented illustrate the ability of the proposed technique to predict responses of the systems to arbitrary sinusoidal and other generic inputs, with significant savings in terms of CPU time when compared with most CFD codes. The methodology described in this paper has wide application in many offshore engineering problems where flexible structures interact with unsteady fluid flow, and should be useful in preliminary design, in design optimization, and in control algorithm development.

scholarly journals A Spatial Euler-Bernoulli Beam Element for Rigid-Flexible Coupling Dynamic Analysis of Flexible Structures

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zhigang Zhang ◽  
Zhaohui Qi ◽  
Zhigang Wu ◽  
Huiqing Fang
Keyword(s):  
Dynamic Analysis ◽  
Cross Sections ◽  
Degrees Of Freedom ◽  
Flexible Structures ◽  
Nonlinear Dynamic Analysis ◽  
Beam Element ◽  
Small Time ◽  
Time Interval ◽  
Slender Beams ◽  
New Formulation

A two-node spatial beam element with the Euler-Bernoulli assumption is developed for the nonlinear dynamic analysis of slender beams undergoing arbitrary rigid motions and large deformations. During the analysis, the global displacement and rotation vectors with six degrees of freedom are selected as the nodal coordinates. In addition, the “shear locking” problem is avoided successfully since the beam cross-sections are always perpendicular to the current neutral axes by employing a special coupled interpolation of the centroid position and the cross-section orientation. Then a scheme is presented where the original transient strains representing the nodal forces are replaced by proposed average strains over a small time interval. Thus all the high frequencies can be filtered out and a corresponding equivalent internal damping will be produced in this new formulation, which can improve the computation performance of the proposed element for solving the stiff problem and evaluate the governing equations even by using the nonstiff ordinary differential equation solver. Finally, several numerical examples are carried out to verify the validation and efficiency of this proposed formulation by comparison with the analytical solutions and other research works.

scholarly journals Non-Intrusive Inference Reduced Order Model for Fluids Using Deep Multistep Neural Network

2019 ◽  
Author(s):  
Xuping Xie ◽  
Guannan Zhang ◽  
Clayton G. Webster
Keyword(s):  
Differential Operators ◽  
Nonlinear Dynamical Systems ◽  
Dimensional Space ◽  
Fluid Dynamic ◽  
Data Driven ◽  
Reduced Order Models ◽  
Order Model ◽  
Reduced Order ◽  
Highly Nonlinear ◽  
Low Dimensional

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams-Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches

scholarly journals Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models

2021 ◽  
Vol 9 (3) ◽  
pp. 1152-1183
Author(s):  
Valentin Resseguier ◽  
Agustin M. Picard ◽  
Etienne Memin ◽  
Bertrand Chapron
Keyword(s):  
Fluid Dynamics ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Unsteady Fluid Dynamics ◽  
Unsteady Fluid

Applications of reduced order models in the aeroelastic analysis of long-span bridges

2017 ◽  
Vol 34 (5) ◽  
pp. 1642-1657
Author(s):  
L. Ebrahimnejad ◽  
K.D. Janoyan ◽  
D.T. Valentine ◽  
P. Marzocca
Keyword(s):  
Computational Efficiency ◽  
Reduced Order Models ◽  
Discrete Vortex ◽  
Content Type ◽  
Reduced Order ◽  
Long Span Bridges ◽  
Analysis And Design ◽  
Long Span ◽  
Aeroelastic Analysis ◽  
High Computational Efficiency

Purpose The application of reduced order models (ROMs) in the aerodynamic/aeroelastic analysis of long-span bridges, unlike the aeronautical structures, has not been extensively studied. ROMs are computationally efficient techniques, which have been widely used for predicting unsteady aerodynamic response of airfoils and wings. This paper aims to discuss the application of a reduced order computational fluid dynamics (CFD) model based on the eigensystem realization algorithm (ERA) in the aeroelastic analysis of the Great Belt Bridge (GBB). Design/methodology/approach The aerodynamic impulse response of the GBB section is used to construct the aerodynamic ROM, and then the aerodynamic ROM is coupled with the reduced DOF model of the system to construct the aeroelastic ROM. Aerodynamic coefficients and flutter derivatives are evaluated and compared to those of the advanced discrete vortex method-based CFD code. Findings Results demonstrate reasonable prediction power and high computational efficiency of the technique that can serve for preliminary aeroelastic analysis and design of long-span bridges, optimization and control purposes. Originality/value The application of a system identification tool like ERA into the aeroelastic analysis of long-span bridges is performed for the first time in this work. Authors have developed their earlier work on the aerodynamic analysis of long-span bridges, published in the Journal of Bridge Engineering, by coupling the aerodynamic forces with reduced DOF of structural system. The high computational efficiency of the technique enables bridge designers to perform preliminary aeroelastic analysis of long-span bridges in less than a minute.

scholarly journals Non-Intrusive Inference Reduced Order Model for Fluids Using Deep Multistep Neural Network

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 757 ◽  
Author(s):  
Xuping Xie ◽  
Guannan Zhang ◽  
Clayton G. Webster
Keyword(s):  
Differential Operators ◽  
Nonlinear Dynamical Systems ◽  
Dimensional Space ◽  
Fluid Dynamic ◽  
Data Driven ◽  
Reduced Order Models ◽  
Order Model ◽  
Reduced Order ◽  
Highly Nonlinear ◽  
Low Dimensional

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams–Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches.

Sign in / Sign up

Export Citation Format

Share Document