Application of Artificial Neural Network for the Prediction of Pressure Distribution of a Plunging Airfoil
Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
[1] J., Leishman, "Principles of helicopter aerodynamic," Cambridge Press,
2000.
[2] Jeppe, Johansen, "Unsteady airfoil flows with application to aero elastic
stability," Riso laboratory, Roskilde, Denmark, October 1999.
[3] Joseph C., Tayler, and , J. Gordon, Leishman, "An analysis of pitch and
plunge effects on unsteady airfoil behavior," Presented at the 47th
Annual Forum of the American Helicopter Society, May1991.
[4] S. J., Schreck, and , W. E., Faller, "Encoding of three dimensional
unsteady separated flow field dynamics in neural network architectures,"
AIAA 95-0103, 33rd Aerospace Science Meeting and Exhibit, 1995.
[5] R.L., McMillen, J.E., Steck, and K., Rokhsaz, "Application of an
artificial neural network as a flight test data estimator," AIAA Paper 95-
0561, presented at AIAA 33rd Aerospace Sciences Meeting and Exhibit,
Reno, Nev., Jan. 1995.
[6] K., Rokhsaz, and J.E., Steck, "Application of artificial neural networks
in nonlinear aerodynamics and aircraft design," SAE Paper 932533, SAE
Transactions, pp. 1790- 1798, 1993.
[7] M.R., Soltani, F., Rasi Marzabadi, and M., Seddighi, "Surface pressure
variation on an airfoil in plunging and pitching motions," 25th ICAS
Congress, Hamburg, Germany, September, 2006.
[8] F. A., Carta, "A comparison of the pitching and plunging response of an
oscillating airfoil," NASA CR-3172, 1979.
[9] Donald, F. Specht, "A General Regression Neural Network," IEEE,
Vol.2 No.6, November, 1991.
[10] W.Mciscl, Brichman, and E., Pursell, "Variable kernel estimates of
multivariate densities," Technometries, Vol. 19 No. 2, May, 1977.
[1] J., Leishman, "Principles of helicopter aerodynamic," Cambridge Press,
2000.
[2] Jeppe, Johansen, "Unsteady airfoil flows with application to aero elastic
stability," Riso laboratory, Roskilde, Denmark, October 1999.
[3] Joseph C., Tayler, and , J. Gordon, Leishman, "An analysis of pitch and
plunge effects on unsteady airfoil behavior," Presented at the 47th
Annual Forum of the American Helicopter Society, May1991.
[4] S. J., Schreck, and , W. E., Faller, "Encoding of three dimensional
unsteady separated flow field dynamics in neural network architectures,"
AIAA 95-0103, 33rd Aerospace Science Meeting and Exhibit, 1995.
[5] R.L., McMillen, J.E., Steck, and K., Rokhsaz, "Application of an
artificial neural network as a flight test data estimator," AIAA Paper 95-
0561, presented at AIAA 33rd Aerospace Sciences Meeting and Exhibit,
Reno, Nev., Jan. 1995.
[6] K., Rokhsaz, and J.E., Steck, "Application of artificial neural networks
in nonlinear aerodynamics and aircraft design," SAE Paper 932533, SAE
Transactions, pp. 1790- 1798, 1993.
[7] M.R., Soltani, F., Rasi Marzabadi, and M., Seddighi, "Surface pressure
variation on an airfoil in plunging and pitching motions," 25th ICAS
Congress, Hamburg, Germany, September, 2006.
[8] F. A., Carta, "A comparison of the pitching and plunging response of an
oscillating airfoil," NASA CR-3172, 1979.
[9] Donald, F. Specht, "A General Regression Neural Network," IEEE,
Vol.2 No.6, November, 1991.
[10] W.Mciscl, Brichman, and E., Pursell, "Variable kernel estimates of
multivariate densities," Technometries, Vol. 19 No. 2, May, 1977.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:64068", author = "F. Rasi Maezabadi and M. Masdari and M. R. Soltani", title = "Application of Artificial Neural Network for the Prediction of Pressure Distribution of a Plunging Airfoil", abstract = "Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.", keywords = "Airfoil, experimental, GRNN, Neural Network,Plunging.", volume = "2", number = "4", pages = "544-6", }