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Rotorcraft downwash impact on ship airwake: statistics, modelling, and simulation

Published online by Cambridge University Press:  07 June 2016

K. A. Schau ,
G. Gaonkar  and
S. Polsky
K. A. Schau
Affiliation:
Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, Florida, US
G. Gaonkar*
Affiliation:
Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, Florida, US
S. Polsky
Affiliation:
Naval Air Warfare Center Aircraft Division (NAWCAD), Patuxent River, Maryland, US
*
gaonkar@fau.edu
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Abstract

Helicopter downwash impact on ship airwake is addressed from a three-pronged approach: (1) Analysis of one-point statistics of autospectrum and two-point statistics of cross-spectrum and coherence from a Computational Fluid Dynamics database of flow velocities effected by helicopter downwash and shipboard airwake; (2) Development of a mathematical framework for extracting interpretive turbulence models in closed form from these autospectral statistics; and (3) Simulation through white-noise-driven filters for the extracted models. The framework begins with an earlier-exercised perturbation-type series expansion of the autocorrelation for all three velocity components, where the first term of the series has a form of the von Karman longitudinal or lateral correlation function. After transformation into equivalent series of autospectrum, the coefficients in the series are evaluated by satisfying theoretical constraints and fitting a curve on a set of selected autospectral data points generated from the database. The framework represents a sensible combination of series expansion, exploitation of a database, and theoretical constraints to provide a foothold on airwake-downwash phenomenon for engineering analysis. It ensures that the extracted model and the autospectral data points have the same time scale, mean square value, and asymptotic decay according to the Kolmogorov –5/3 Law. The framework's strengths and weaknesses, and its major advancement over the earlier series-expansion schemes are also addressed. Finally it is shown that downwash increases airwake energy (mean square value) by one order of magnitude, and almost all of this airwake-downwash energy is concentrated within the bandwidth (0.16 < f(Hz) < 1.6) that affects flight mechanics.

Keywords

Aerodynamicsturbulenceflight controlflight simulationrotorcrafthelicopterdownwashstatisticsautospectrum
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1229 , July 2016 , pp. 1025 - 1048
Copyright
Copyright © Royal Aeronautical Society 2016 

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References

REFERENCES

1.Gaonkar, G.H.Review of turbulence modeling and related applications to some problems of helicopter flight dynamics, J. American Helicopter Society, January 2008, 53, (1), pp 87–107.Google Scholar
2.Doane, S.R. A Wind Tunnel Technique for the Identification of Ship Airwake/Rotor Downwash Coupling, August 2011, PhD Dissertation, Department of Aerospace Engineering, Old Dominion University, Norfolk, Virginia, US, Chapter 2.Google Scholar
3.Polsky, S.A. and Wilkinson, C.H. A computational study of outwash for a helicopter operating near a vertical face with comparison to experimental data, AIAA Paper 2009-5684, AIAA Modeling and Simulation Technologies Conference and Exhibit, 10-13 August 2009, Chicago, Illinois, US.Google Scholar
4.Gaonkar, G.H. On modeling and simulation of airwake and airwake downwash turbulence for helicopter shipboard operations, American Helicopter Society 69th Annual Forum Proceedings, 21-23 May 2013, Phoenix, Arizona, US.Google Scholar
5.Schau, K. Developing Interpretive Turbulence Models from a Database with Applications to Wind Farms and Shipboard Operations, MSc Thesis, October 2013, Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, Florida, US.Google Scholar
6.Harris, R.I.Some further thoughts on the spectrum of gustiness in strong winds, J. Wind Engineering and Industrial Aerodynamics, 1990, 33, pp 461–477.Google Scholar
7.Gaonkar, G.H. and Mohan, R. Extracting stochastic airwake models from a database for engineering analysis and simulation, J. American Helicopter Society, April 2012, 57, (2), pp 1–15.Google Scholar
8.Gaonkar, G.H.Extracting stochastic models of airwake-downwash turbulence from a database for simulation, J. Aircraft, 2013, 50, (4), pp 1309–1311.Google Scholar
9.Lee, R.G. and Zan, S.J.Wind tunnel testing of a helicopter fuselage and rotor in ship airwake, Journal of the American Helicopter Society, October 2005, 50, (4), pp 326–337.Google Scholar
10.Zan, S.J.On aerodynamic modelling and simulation of the dynamic interface, Proceedings of the Institution of Mechanical Engineers, Part G; J Aerospace Engineering, 1 May 2005, 219, (5), pp 393–410.Google Scholar
11.Mathieu, J. and Scott, J. Chapters 6: Spectral analysis of homogeneous turbulence and Chapter 7: Kolmogorov's and other theories based on spectral analysis, An Introduction to Turbulent Flow, 2000, Cambridge University Press, New York, New York, US, pp 239–282; 283-326.Google Scholar
12.Townsend, A.A. Chapter 3: Homogenous turbulent flows, The Structure of Turbulent Shear Flow, 2nd ed., 1976, Cambridge University Press, New York, New York, US, pp 45–104.Google Scholar
13.Bendat, J.S. and Piersol, A.G. Chapter 5: Stationary Random Processes, Random Data, Analysis and Measurement Procedures, 3rd ed., 2000, John Wiley and Sons, New York, New York, US, pp 118–188.Google Scholar
14.Simiu, E. and Scanlan, R.H. Chapter 2: The Atmospheric Boundary Layer, Wind Effects on Structures, 3rd ed., 1996, John Wiley and Sons, Inc., New York, New York, US, pp 33–90.Google Scholar