Neurofuzzy-Model-Based Unsteady Aerodynamic Computations Across Varying Freestream Conditions

Winter, Maximilian; Breitsamter, Christian

doi:10.2514/1.j054892

Cited by 48 publications

(19 citation statements)

References 37 publications

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“…limit cycle oscillation under the nonlinearity of a large-amplitude structural motion or flow separation, practical nonlinear ROMs have been developed, such as the Volterra series model [80], the neural network model [81], and the Winer model [82]. To improve the generalization ability of these nonlinear ROMs and construct linear/non-linear combination models, Kou [83][84] and Winter [85][86][87] have carried out extensive original researches. In recent years, increasing attention has been paid to deep learning and machine learning methods in this field [88].…”

Section: Reduced-order Model For the Unsteady Flowmentioning

confidence: 99%

Transonic aeroelasticity: A new perspective from the fluid mode

Gao

Zhang

2020

Progress in Aerospace Sciences

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Within the transonic regime, the aeroelastic problems exhibit many unique characteristics compared with subsonic and supersonic cases. Although a lot of research has been carried out in this field, the underlying mechanisms of these complex phenomena are not clearly understood yet, resulting in a challenge in the design and use of modern aircraft. This review summarizes the recent investigations on nonclassical transonic aeroelastic problems, including transonic buzz, reduction of transonic buffet onset, transonic buffeting response and frequency lock-in phenomenon in transonic buffet flow. After introducing the research methods in unsteady aerodynamics and aeroelastic problems, the dynamical characteristics as well as the physical mechanisms of these phenomena are discussed from the perspective of the fluid mode. In the framework of the ROM (reduced order model) -based model, the dominant fluid mode (or the eigenvalue) and its coupling process with the structural model can be clearly captured. The flow nonlinearity was believed to be the cause of the complexity of transonic aeroelasticity. In fact, this review indicates that the complexity lies in the decrease of the flow stability in the transonic regime. In this condition, the fluid mode becomes a principal part of the coupling process, which results in the instability of the fluid mode itself or the structural mode, and thus, it is the root cause of different transonic aeroelastic phenomena.

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Section: Reduced-order Model For the Unsteady Flowmentioning

confidence: 99%

Transonic aeroelasticity: A new perspective from the fluid mode

Gao

Zhang

2020

Progress in Aerospace Sciences

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“…Lieu (Lieu and Lesoinne, 2004;Farhat, 2006, 2007) demonstrated the use of ROMs to predict the transonic aeroelastic response and take into account variations in Mach number and angle of attack. Winter (Winter and Breitsamter, 2016b) presented a novel aerodynamic ROM approach for predicting generalized aerodynamic forces with variations in Mach number using local linear neuro-fuzzy models. Winter (Winter and Breitsamter, 2016a) demonstrated an unsteady aerodynamic surrogate modeling approach for the prediction of surface pressure fluctuations and integral force and moment coefficients undergoing a pitching motion at high subsonic and transonic flow conditions, based on the POD method combined with a local linear neuro-fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

An efficient implementation of aeroelastic tailoring based on efficient computational fluid dynamics-based reduced order model

Gong

Ronch

et al. 2019

Journal of Fluids and Structures

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Current and future trends in the aerospace industry leverage on the potential benefits provided by lightweight materials that can be tailored to realize desired mechanical characteristics when loaded. For aircraft design, the deployment of aeroelastic tailoring is hindered by the need to re-compute, for any possible modification of the structure, the dependence of the aerodynamic field on the underlying structural properties. To make progress in this direction, the work presents a rapid computational fluid dynamics based aeroelastic tool which is built around a reduced order model for the aerodynamics that is updated for any modification of the structure by using the structural dynamics reanalysis method. The aeroelastic tailoring tool is demonstrated in transonic flow for the AGARD 445.6 wing, suitably modified with composite materials. It was found that the proposed method provides accurate engineering predictions for the aeroelastic response and stability when the structure is modified from the baseline model.

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“…However, the computational cost for Kriging model increased significantly in order to take the Mach number into consideration. Recently, a reduced-order modeling approach based on recurrent local linear neuro-fuzzy models was developed to model the generalized aerodynamic forces over a range of Mach numbers [19]. To guarantee the accuracy of the local linear neurofuzzy model, the flight parameters for training data should be selected carefully in the flight range of concern.…”

Section: Introductionmentioning

confidence: 99%

Efficient Training Data Generation for Reduced-Order Modeling in a Transonic Flight Regime

Liu

Zhao

2018

International Journal of Aerospace Engineering

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In this study, a time-dependent surrogate approach is presented to generate the training data for identifying the reduced-order model of an unsteady aerodynamic system with the variation of mean angle of attack and Mach number in a transonic flight regime. For such a purpose, a finite set of flight samples are selected to cover the flight range of concern at first. Subsequently, the unsteady aerodynamic outputs of the system under given inputs of filtered white Gaussian noise at these flight samples are simulated via CFD technique which solves Euler equations. The unsteady aerodynamic outputs, which are viewed as a timedependent function of flight parameters, can be approximated via the Kriging technique at each time step. By this way, the training data for any combination of flight parameters in the range of concern can be obtained without performing any further CFD simulations. To illustrate the accuracy and validity of the training data generated via the proposed approach, the constructed data are used to identify the reduced-order aerodynamic models of a NACA 64A010 airfoil via a robust subspace identification algorithm. The unsteady aerodynamics and aeroelastic responses under various flight conditions in a transonic flight regime are computed. The results agree well with those obtained by using the training data of CFD technique.

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Neurofuzzy-Model-Based Unsteady Aerodynamic Computations Across Varying Freestream Conditions

Cited by 48 publications

References 37 publications

Transonic aeroelasticity: A new perspective from the fluid mode

Transonic aeroelasticity: A new perspective from the fluid mode

An efficient implementation of aeroelastic tailoring based on efficient computational fluid dynamics-based reduced order model

Efficient Training Data Generation for Reduced-Order Modeling in a Transonic Flight Regime

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