Aerodynamic Coefficient Prediction of Airfoils Using Neural Networks

Santos, Mailema; Mattos, Bento Silva de; Girardi, R.

doi:10.2514/6.2008-887

Cited by 32 publications

(19 citation statements)

References 3 publications

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“…The multilayer perceptron (MLP) [ 33 ] has the ability to extract the deep hidden features of information from data efficiently and accurately. The MLP is composed of several neurons, which are connected together in a complex manner to form a network [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method

Qian

Zhao

et al. 2020

Entropy

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To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data fusion model. The architecture consists of three fully-connected neural networks that are employed to approximate low-fidelity data, and the linear part and nonlinear part of correlation for the low- and high-fidelity data, respectively. To test the proposed multi-fidelity aerodynamic data fusion method, we calculated Euler and Navier–Stokes simulations for a typical airfoil at various Mach numbers and angles of attack to obtain the aerodynamic coefficients as low- and high-fidelity data. A fusion model of the longitudinal coefficients of lift CL and drag CD was constructed with the proposed method. For comparisons, variable complexity modeling and cokriging models were also built. The accuracy spread between the predicted value and true value was discussed for both the training and test data of the three different methods. We calculated the root mean square error and average relative deviation to demonstrate the performance of the three different methods. The fusion result of the proposed method was satisfactory on the test case, and showed a better performance compared with the other two traditional methods presented. The results provide evidence that the method proposed in this paper can be useful in dealing with the multi-fidelity aerodynamic data fusion problem.

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Section: Methodsmentioning

confidence: 99%

Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method

Qian

Zhao

et al. 2020

Entropy

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“…Liu [2] established a radial basis function (RBF) neural-network model to predict the airfoil lift and drag coefficients within a given parameter range. Wallach, Santos, Mattos, et al [3,4] employed MLP and functional link networks to predict the lift and drag coefficients of NACA23012, the drag coefficients of a regional twinjet, and the drag coefficients of a wing-fuselage combination. Andrés et al [5] hybridized an evolutionary programming algorithm with an SVR algorithm as the metamodel for the aerodynamic optimization of aeronautical wing profiles.…”

Section: Introductionmentioning

confidence: 99%

“…Andrés et al [5] hybridized an evolutionary programming algorithm with an SVR algorithm as the metamodel for the aerodynamic optimization of aeronautical wing profiles. Secco et al [6] improved Wallach's work [4]. There, wing-body combinations were composed of generic airfoils, and different artificial-neural-network (ANN) architectures were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

Hai

Qian

et al. 2020

Symmetry

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Both symmetric and asymmetric airfoils are widely used in aircraft design and manufacture, and they have different aerodynamic characteristics. In order to improve flight performance and ensure flight safety, the aerodynamic coefficients of these airfoils must be obtained. Various methods are used to generate aerodynamic coefficients. The prediction model is a promising method that can effectively reduce cost and time. In this paper, a graphical prediction method for multiple aerodynamic coefficients of airfoils based on a convolutional neural network (CNN) is proposed. First, a transformed airfoil image (TAI) was constructed by using the flow-condition convolution with the airfoil image. Next, TAI was combined with the original airfoil image to form a composite airfoil image (CAI) that is used as the input of the CNN prediction model. Then, the structure and parameters of the prediction model were designed according to CAI features. Finally, a sample set that was generated on the basis of the deformation of symmetrical airfoil NACA 0012 was used to train and test the prediction model. Simulation results showed that the proposed method based on CNN could simultaneously predict the pitch-moment, drag, and lift coefficients, and prediction accuracy was high.

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“…Prediction of aerodynamic coefficients with ANN models, through the interaction of airfoil geometry variables as inputs and equating force coefficients as outputs was investigated. 14,[17][18][19][20] Santos et. al.…”

Section: Introductionmentioning

confidence: 99%

“…reported a training database of 10,000 airfoils, with a multi-layered network consisting of 50 neurons within the two hidden layers, for acceptable lift and drag simulations. 17 Ross et. al.…”

Section: Introductionmentioning

confidence: 99%

Application of Swarm Approach and Artificial Neural Networks for Airfoil Shape Optimization

Khurana¹,

Winarto²,

Sinha³

2008

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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The Direct Numerical Optimization (DNO) approach for airfoil shape design requires the integration of modules: a) A geometrical shape function; b) Computational flow solver and; c) Search model for shape optimization. These modules operate iteratively until convergence based on defined objectives and constraints. The DNO architecture is to be validated to ensure efficient optimization simulations and is the focus of this paper. The PARSEC airfoil shape function is first validated by observing the effect of design coefficients on airfoil geometry and aerodynamics. The design variables provide independent one-to-one control over airfoil geometry, for imposing shape constraints. The aerodynamic performance of PARSEC airfoils through variable perturbations, conform to established aerodynamic principles. It confirms the design flexibility of the shape function in providing direct control over airfoil geometry. The Particle Swarm Optimization (PSO) algorithm is introduced as the search agent. A PSO simulation requires userinputs to define the search pattern. A methodology is presented to validate these parameters on pre-defined benchmark mathematical functions. Self Organizing Maps (SOM) are applied to illustrate trade-offs between PSO search variables. An Adaptive Inertia Weight (APSO) scheme that dynamically alters the search path of the swarm by monitoring the position of the particles, provides an acceptable convergence. Validation tests indicated the maximum velocity of the particles is less than 1% of computational domain size for convergence. The DNO approach is computationally inefficient, thus a surrogate model to address this issue is presented. An Artificial Neural Network (ANN) model with a training dataset of 3000 airfoils is applied to develop a model that applies the PARSEC airfoil geometry variables as inputs and the equating aerodynamic coefficient as output. System validation with 1000 randomly generated airfoils indicated 70% of the simulated solutions were within 10% of actual solver run. Future research will involve reducing the percentage error of the surrogate model against the theoretical solution.

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Aerodynamic Coefficient Prediction of Airfoils Using Neural Networks

Cited by 32 publications

References 3 publications

Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method

Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method

Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

Application of Swarm Approach and Artificial Neural Networks for Airfoil Shape Optimization

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