Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Hu, Liwei; Zhang, Jun; Xiang, Yu; Wang, Wenyong

doi:10.1109/access.2020.2993562

Cited by 65 publications

(28 citation statements)

References 116 publications

(114 reference statements)

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“…In the modeling of aerodynamic data from wind tunnel tests on a variety of aircraft, a utilization of the neural network method occurred [19]. In [20,21], a rectangular wing with the NACA0015 profile, pitching from α = 0 • to α = 60 about the 1/4 chord location • , is introduced using the ANN model.…”

Section: Neural Network Modelmentioning

confidence: 99%

Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics

Alkhedher¹

2022

Computers, Materials &Amp; Continua

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Modern fighters are designed to fly at high angle of attacks reaching 90 deg as part of their routine maneuvers. These maneuvers generate complex nonlinear and unsteady aerodynamic loading. In this study, different aerodynamic prediction tools are investigated to achieve a model which is highly accurate, less computational, and provides a stable prediction of associated unsteady aerodynamics that results from high angle of attack maneuvers. These prediction tools include Artificial Neural Networks (ANN) model, Adaptive Neuro Fuzzy Logic Inference System (ANFIS), Fourier model, and Polynomial Classifier Networks (PCN). The main aim of the prediction model is to estimate the pitch moment and the normal force data obtained from forced tests of unsteady delta-winged aircrafts performing high angles of attack maneuvers. The investigation includes three delta wing models with 1, 1.5, and 2 aspect ratios with four determined variables: change rate in angle of attack (0 to 90 deg), non-dimensional pitch rate (0 to .06), and angle of attack. Following a comprehensive analysis of the proposed identification methods, it was found that the newly proposed model of PCN showed the least error in modeling and prediction results. Based on prediction capabilities, it is seen that polynomial networks modeling outperformed ANFIS and ANN for the present nonlinear problem.

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Section: Neural Network Modelmentioning

confidence: 99%

Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics

Alkhedher¹

2022

Computers, Materials &Amp; Continua

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“…Furthermore, neural networks have also been widely employed in several scientific and technological fields. Among the research studies found in the literature it is worth mentioning the application of neural networks for prediction of biogases concentration using spiking neural networks [45], feature recognition and process planning of casting dies [46], quality control in manufacturing processes [47], prediction of springback in sheet metal forming [48], aerodynamic data modeling [49], detection of skin diseases [50], automatic control of house elements [51,52] and energy forecasting in the manufacturing sector [53], among many other applications.…”

Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.

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“…The geometry shape of airfoils greatly affects the aerodynamic performance, the parameterization as well as the aircraft inverse design [1,2]. One of a traditional airfoil design method is to define airfoil geometry parameters manually, which is effective to perceive the variations in airfoil geometry structures [3].…”

Section: Introductionmentioning

confidence: 99%

An airfoil geometric-feature extraction and discrepant data fusion learning method

Xiang

Zhang

et al. 2022

Preprint

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The perception of geometric-features of airfoils is the basis in aerodynamic area for performance prediction, parameterization, aircraft inverse design, etc. There are three approaches to percept the geometric shape of airfoils, namely manual design of airfoil geometry parameters, polynomial definition and deep learning. The first two methods directly define geometric-features or polynomials of airfoil curves, but the number of extracted features is limited. Deep learning algorithms can extract a large number of potential features (called latent features). However, the features extracted by deep learning lack explicit geometrical meaning. Motivated by the advantages of polynomial definition and deep learning, we propose a geometric-feature extraction method (named Bézier-based feature extraction, BFE) for airfoils, which consists of two parts: manifold metric feature extraction and geometric-feature fusion encoder (GF encoder). Manifold metric feature extraction, with the help of the Bézier curve, captures manifold metrics (a sort of geometric-features) from tangent space of airfoil curves, and the GF-encoder combines airfoil coordinate data and manifold metrics together to form novel fused geometric-features. To validate the feasibility of the fused geometric-features, two experiments based on the public UIUC airfoil dataset are conducted. Experiment I is used to extract manifold metrics of airfoils and export the fused geometric-features. Experiment II, based on the Multi-task learning (MTL), is used to fuse the discrepant data (i.e., the fused geometric-features and the flight conditions) to predict the aerodynamic performance of airfoils. The results show that the BFE can generate more smooth and realistic airfoils than Auto-Encoder, and the fused geometric-features extracted by BFE can be used to reduce the prediction errors of C L and C D .

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Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Cited by 65 publications

References 116 publications

Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics

Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

An airfoil geometric-feature extraction and discrepant data fusion learning method

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