Aerodynamic Coefficient Prediction of Transport Aircraft Using Neural Network

Wallach, Ricardo; Mattos, Bento Silva de; Girardi, R.; Curvo, Marcelo

doi:10.2514/6.2006-658

Cited by 22 publications

(8 citation statements)

References 4 publications

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“…Therefore, the resolution of the airfoil image could not be set too high. If the MLP network were selected as a three-layer network whose number of layers was [80, 10,3], the resolution of the airfoil image had to be within 20 × 20. Otherwise, an out-of-memory error would occur.…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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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Section: Discussionmentioning

confidence: 99%

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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“…[32][33][34] It is also known that such algorithms are used to extend the database of a specified aircraft. [35][36][37][38][39] The existing methods within the literature [40][41][42][43][44][45] such as fuzzy inference systems and neural networks are designed for estimating the performance of a single configuration under a single flight condition and does not contain the complete the aircraft configuration. In addition, these predictions generally focus on a single target coefficient.…”

Section: Literature Review -Ai Based Aerodynamic Modelmentioning

confidence: 99%

A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

Karali

Tsourdos

Demirezen

et al. 2021

International Journal of Micro Air Vehicles

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In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

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“…However, in Rajkumar’s work, the methodology is used for a single configuration. Wallach and Mattos (2006) studied multilayer feed-forward ANN to predict drag coefficient of transport airplanes. In his work, all wing planforms are composed of the same baseline airfoils.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks to predict aerodynamic coefficients of transport airplanes

Secco

Mattos

2017

AEAT

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Purpose Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity tools to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, structural analysis and others. Surrogate models are a good alternative to address properly and elegantly this issue. With regard to this issue, the purpose of this paper is the design and application of an artificial neural network to predict aerodynamic coefficients of transport airplanes. The neural network must be fed with calculations from computational fluid dynamic codes. The artificial neural network system that was then developed can predict lift and drag coefficients for wing-fuselage configurations with high accuracy. The input parameters for the neural network are the wing planform, airfoil geometry and flight condition. An aerodynamic database consisting of approximately 100,000 cases calculated with a full-potential code with computation of viscous effects was used for the neural network training, which is carried out with the back-propagation algorithm, the scaled gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. The neural network featuring the lowest regression error is able to reduce the computation time of the aerodynamic coefficients 4,000 times when compared with the computing time required by the full potential code. Regarding the drag coefficient, the average error of the neural network is of five drag counts only. The computation of the gradients of the neural network outputs in a scalable manner is possible by an adaptation of back-propagation algorithm. This enabled its use in an adjoint method, elaborated by the authors and used for an airplane optimization task. The results from that optimization were compared with similar tasks performed by calling the full potential code in another optimization application. The resulting geometry obtained with the aerodynamic coefficient predicted by the neural network is practically the same of that designed directly by the call of the full potential code. Design/methodology/approach The aerodynamic database required for the neural network training was generated with a full-potential multiblock-structured code. The training process used the back-propagation algorithm, the scaled-conjugate gradient algorithm and the Nguyen–Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. Findings A suitable and efficient methodology to model aerodynamic coefficients based on artificial neural networks was obtained. This work also suggests appropriate sizes of artificial neural networks for this specific application. We demonstrated that these metamodels for airplane optimization tasks can be used without loss of fidelity and with great accuracy, as their local minima might be relatively close to the minima of the original design space defined by the call of computational fluid dynamics codes. Research limitations/implications The present work demonstrated the ability of a metamodel with artificial neural networks to capture the physics of transonic and subsonic flow over a wing-fuselage combination. The formulation that was used was the full potential equation. However, the present methodology can be extended to model more complex formulations such as the Euler and Navier–Stokes ones. Practical implications Optimum networks reduced the computation time for aerodynamic coefficient calculations by 4,000 times when compared with the full-potential code. The average absolute errors obtained were of 0.004 and 0.0005 for lift and drag coefficient prediction, respectively. Airplane configurations can be evaluated more quickly. Social implications If multidisciplinary optimization tasks for airplane design become more efficient, this means that more efficient airplanes (for instance less polluting airplanes) can be designed. This leads to a more sustainable aviation. Originality/value This research started in 2005 with a master thesis. It was steadily improved with more efficient artificial neural networks able to handle more complex airplane geometries. There is a single work using similar techniques found in a conference paper published in 2007. However, that paper focused on the application, i.e. providing very few details of the methodology to model aerodynamic coefficients.

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Aerodynamic Coefficient Prediction of Transport Aircraft Using Neural Network

Cited by 22 publications

References 4 publications

Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

Artificial neural networks to predict aerodynamic coefficients of transport airplanes

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