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Estimation of aerodynamic coefficients of a non-slender delta wing under ground effect using artificial intelligence techniques

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Abstract

This work presents machine learning techniques to estimate the aerodynamic coefficients of a 40° swept delta wing under the ground effect. For this purpose, three different approaches including feed-forward neural network (FNN), Elman neural network (ENN) and adaptive neuro-fuzzy interference system (ANFIS) have been used. The optimal configuration of these models was compared with each other, and the best accurate prediction model was determined. In the generated machine learning models, the liftCL and drag coefficientsCD of the delta wing under the ground proximity ofh/c = 0.4 were predicted by using the data of actualCL andCD of the delta wing under the ground proximities ofh/c = 1, 0.7, 0.55, 0.25 and 0.1. In FNN, ENN and ANFIS models, the angle of attackα and ground distanceh/c were utilized as input parameters,CL andCD as output parameters, separately. Although all three models estimate theCL andCD of the delta wing underh/c = 0.4 with very high accuracy, the ENN method predicts theCL andCD with much higher accuracy than the FNN and ANFIS models. For the estimation ofCL, while optimal configuration of ENN resulted in 1.0709% MAPE, 0.00595 RMSE and 0.00504 MAE, the best configurations of FNN and ANFIS end up with the results of 1.172% and 1.1028% MAPE, 0.00786 and 0.0071 RMSE, 0.00593 and 0.0054 MAE, respectively. Thus, results show that the developed FNN, ENN and ANFIS models can be accurately employed to forecast the aerodynamic coefficients of the delta wing under ground effect without the need of for many experimental measurements that causes extra time, labor and experimental costs.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Cukurova University, Adana, Turkey

    Sergen Tumse & Besir Sahin

  2. Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, Adana, Turkey

    Mehmet Bilgili

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  1. Sergen Tumse

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Tumse, S., Bilgili, M. & Sahin, B. Estimation of aerodynamic coefficients of a non-slender delta wing under ground effect using artificial intelligence techniques.Neural Comput & Applic34, 10823–10844 (2022). https://doi.org/10.1007/s00521-022-07013-x

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