Development of Digital Flight Motion Methodology Based on Aerodynamic Derivatives Approximation

Norazila Othman; Masahiro Kanazaki

doi:10.20965/jrm.2016.p0215

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JRM Vol.28 No.2 pp. 215-225

doi: 10.20965/jrm.2016.p0215

(2016)

Paper:

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Development of Digital Flight Motion Methodology Based on Aerodynamic Derivatives Approximation

Norazila Othman and Masahiro Kanazaki

Tokyo Metropolitan University
6-6 Asahigaoka, Hino City, Tokyo 191-0065, Japan

Received:

April 3, 2015

Accepted:

February 10, 2016

Published:

April 20, 2016

Keywords:

digital flight, flight simulation, surrogate model, equation of motion

Abstract

The accuracy of efficient flight simulation depends on the quality of the aerodynamic data used to simulate aircraft dynamic motion. The accuracy of such data prediction depends strongly on motion variables, aerodynamic derivatives, and the coefficients used when the complete global aerodynamic database is being building. A surrogate model applied as a prediction method based on several measured points (exact function) used to predict unknown points of interest helps reduce time taken by the experiment or computation. Latin hypercube sampling searches the solution space for aerodynamic data to optimize the experimental design, so the key objective is to develop an aircraft's efficient digital flight motion by solving equations of motion and predicting aerodynamic data using a surrogate model. To realize these goals, we use sample surrogate model data, acquired from empirical model USAF Stability and Control DATCOM. The database was built for two main variables, the angle of attack and the Mach number, along the longitudinal and lateral axes. Exact and predicted functions were compared by calculating the mean squared error (MSE). The digital flight was validated through mode motion analysis and a flight quality scale to prove flight mission capabilities. A comparison between results predicted by the surrogate model and the exact function showed that flight simulation analysis and prediction ability of the surrogate model are useful in future analyses.

3D contour views of Cz [-0.4—1.05], <br>Mach:0.6-1.4, Alpha:0°-30°

3D contour views of Cz [-0.4—1.05],
Mach:0.6-1.4, Alpha:0°-30°

Cite this article as:

N. Othman and M. Kanazaki, “Development of Digital Flight Motion Methodology Based on Aerodynamic Derivatives Approximation,” J. Robot. Mechatron., Vol.28 No.2, pp. 215-225, 2016.

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References

[1] R. C. Nelson, “Flight stability and automatic control,” McGRAW-Hill Int. Editions Inc., 1998.
[2] A. D. Ronch, “On the calculation of dynamic derivatives using computational fluid dynamics,” Thesis Doctor of Philosophy, University of Liverpool, pp. 1-214, 2012.
[3] A. Rizzi, “Modeling and simulating aircraft stability and control-The SIMSAC project,” Progress in Aerospace Sciences, Vol.47, pp. 573-588, 2011.
[4] Y. Narita, A. Hashimoto, and M. Kanazaki, “Numerical simulation: Flight dynamics stability analysis using unstructured based Navier-Stokes solver,” Asia-Pacific Int. Symposium on Aerospace Technology (APISAT), 2012.
[5] M. A. Park and L. L. Green, “Steady-state computation of constant rotational rate dynamic stability derivatives,” 18^th Applied Aerodynamics Conf., AIAA 2000-4321, 2000.
[6] K. Takii, M. Kanazaki, K. Ishiko, K. Hayashi, A. Hashimoto, and T. Aoyama, “Unsteady numerical simulations of HTV-R Reentry Capsule by using Detached-Eddy simulation,” 52^nd Aerospace Sciences Meeting, 2014.
[7] N. Othman and M. Kanazaki, “Prediction of Aircraft's Longitudinal Motion Based Aerodynamic Coefficients and Derivatives by Surrogate Model Approach,” J. of Mechanics Engineering and Automation (JMEA), Vol.4, No.7, pp. 584-594, 2014.
[8] E. Schmidt, “Standard dynamics model experiments with the DFVLR/AVA transonic derivative balance,” Report of Institut Fuer Aerelastik, DFVLR-AVA Goettingen, Bunsenstr, West Germany, 1985.
[9] “USAF Stability and Control DATCOM,” Flight Control Division, Air Force Flight Dynamics Laboratory, Wright-Patterson Air Force Base, Fairborn, OH, 1976.
[10] S. Jeong, M. Murayama, and K. Yamamoto, “Efficient optimization design method using Kriging model,” J. of aircraft, Vol.42, No.2, pp. 413-420, 2005.
[11] M. Kanazaki, K. Tanaka, S. Jeong, and K. Yamamoto, “Design Exploration of High-Lift Airfoil Using Kriging Model and Data Mining Technique,” European Conf. on Computational Fluid Dynamics, ECCOMAS CFD, 2006.
[12] K. H. Lee and F. J. Park, “A global robust optimization using Kriging based approximation model,” JSME Int. J. Series C, Vol.49, No.3, 2006.
[13] J. L. Deutsch and C. V. Deutsch, “Latin hypercube sampling with multidimensional uniformity,” J. of Statistical Planning and Inference, Vol.142, pp. 763-772, 2012.
[14] W. Durham, “Aircraft flight dynamics and control,” Virginia Polytechnic Institute and State University, USA, WILEY, 2013.
[15] MIL-F-8785C, “Military specification-Flying qualities of piloted airplanes,” 1980.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. C. Nelson, “Flight stability and automatic control,” McGRAW-Hill Int. Editions Inc., 1998.

[2] [2] A. D. Ronch, “On the calculation of dynamic derivatives using computational fluid dynamics,” Thesis Doctor of Philosophy, University of Liverpool, pp. 1-214, 2012.

[3] [3] A. Rizzi, “Modeling and simulating aircraft stability and control-The SIMSAC project,” Progress in Aerospace Sciences, Vol.47, pp. 573-588, 2011.

[4] [4] Y. Narita, A. Hashimoto, and M. Kanazaki, “Numerical simulation: Flight dynamics stability analysis using unstructured based Navier-Stokes solver,” Asia-Pacific Int. Symposium on Aerospace Technology (APISAT), 2012.

[5] [5] M. A. Park and L. L. Green, “Steady-state computation of constant rotational rate dynamic stability derivatives,” 18^th Applied Aerodynamics Conf., AIAA 2000-4321, 2000.

[6] [6] K. Takii, M. Kanazaki, K. Ishiko, K. Hayashi, A. Hashimoto, and T. Aoyama, “Unsteady numerical simulations of HTV-R Reentry Capsule by using Detached-Eddy simulation,” 52^nd Aerospace Sciences Meeting, 2014.

[7] [7] N. Othman and M. Kanazaki, “Prediction of Aircraft's Longitudinal Motion Based Aerodynamic Coefficients and Derivatives by Surrogate Model Approach,” J. of Mechanics Engineering and Automation (JMEA), Vol.4, No.7, pp. 584-594, 2014.

[8] [8] E. Schmidt, “Standard dynamics model experiments with the DFVLR/AVA transonic derivative balance,” Report of Institut Fuer Aerelastik, DFVLR-AVA Goettingen, Bunsenstr, West Germany, 1985.

[9] [9] “USAF Stability and Control DATCOM,” Flight Control Division, Air Force Flight Dynamics Laboratory, Wright-Patterson Air Force Base, Fairborn, OH, 1976.

[10] [10] S. Jeong, M. Murayama, and K. Yamamoto, “Efficient optimization design method using Kriging model,” J. of aircraft, Vol.42, No.2, pp. 413-420, 2005.

[11] [11] M. Kanazaki, K. Tanaka, S. Jeong, and K. Yamamoto, “Design Exploration of High-Lift Airfoil Using Kriging Model and Data Mining Technique,” European Conf. on Computational Fluid Dynamics, ECCOMAS CFD, 2006.

[12] [12] K. H. Lee and F. J. Park, “A global robust optimization using Kriging based approximation model,” JSME Int. J. Series C, Vol.49, No.3, 2006.

[13] [13] J. L. Deutsch and C. V. Deutsch, “Latin hypercube sampling with multidimensional uniformity,” J. of Statistical Planning and Inference, Vol.142, pp. 763-772, 2012.

[14] [14] W. Durham, “Aircraft flight dynamics and control,” Virginia Polytechnic Institute and State University, USA, WILEY, 2013.

[15] [15] MIL-F-8785C, “Military specification-Flying qualities of piloted airplanes,” 1980.