Distributed Atmospheric Turbulence Model for Helicopter Flight Simulation and Handling-Quality Analysis

Ji, Honglei; Chen, Renliang; Li, Pan

doi:10.2514/1.c033667

Cited by 12 publications

(12 citation statements)

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“…The detailed formulation and validation of the simulation model for helicopter flight in atmospheric turbulence can be obtained in [5]. Here, a brief overview to the simulation model is presented for further analysis.…”

Section: Simulation Model For Helicopter Flight In Atmospheric Tmentioning

confidence: 99%

“…(3)(4)(5), the rational approximant to the theoretical von Kármán spectra can be derived by the least-squares fitting method. According to Campbell [13], the rational approximations to the von Kármán spectra should satisfy three requirements.…”

Section: A High-order Filters With Von Kármán Spectramentioning

confidence: 99%

“…Rational filters with the approximate von Kármán spectra were also obtained by Campbell [13] with the Padé approximant to the irrational von Kármán transfer functions. However, the valid normalized frequency range of the filters is not improved compared with those proposed by Schaeffer et al Nonrecursive algorithms such as the fast Fourier transform/filter method [14], convolution integral method [15], and sum-of-sinusoids method [16] have been developed for airplane and space shuttle, but it is involved for their applications in turbulence models such as the complex rotating-frame SORBET model [1] or the current distributed turbulence model [5] for helicopter flight simulation.…”

mentioning

confidence: 99%

“…Atmospheric turbulence would result in undesired helicopter motions and high pilot workloads, sometimes even pushing the aircraft to its operational limits. For these reasons, a variety of turbulence models, from the physics-based turbulence modeling methods [1][2][3][4][5] to the flight-based control equivalent turbulence input approach [6][7][8], have been developed for the prediction of helicopter response to atmospheric turbulence. Among the various models, the current distributed turbulence model developed by Ji et al [5] is well suited for helicopter flight simulation and handling quality analysis in lowaltitude and low-speed flight conditions against the flight-test data with high computational efficiency.…”

mentioning

confidence: 99%

“…For these reasons, a variety of turbulence models, from the physics-based turbulence modeling methods [1][2][3][4][5] to the flight-based control equivalent turbulence input approach [6][7][8], have been developed for the prediction of helicopter response to atmospheric turbulence. Among the various models, the current distributed turbulence model developed by Ji et al [5] is well suited for helicopter flight simulation and handling quality analysis in lowaltitude and low-speed flight conditions against the flight-test data with high computational efficiency. It only generated turbulence velocity components at four selected points on a plane that is in front of the helicopter and perpendicular to the vehicle flight path as well as tangential to the leading edge of the rotor disc.…”

mentioning

confidence: 99%

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Analysis of Helicopter Handling Quality in Turbulence with Recursive Von Kármán Model

Chen

2017

Journal of Aircraft

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A recursive von Kármán turbulence model is developed and integrated into the current distributed turbulence model for helicopter flight simulation and handling quality analysis in atmospheric turbulence. First, high-order filters are developed with rational expressions to approximate the von Kármán spectra. Recursive algorithms for modeling the von Kármán turbulence are derived by discretizing the filters with the Tustin transform. Then, the recursive von Kármán turbulence model is used to replace the Dryden model in the current distributed turbulence model for helicopter flight simulation in atmospheric turbulence. The results show that the spectra of the recursive von Kármán turbulence have an excellent agreement with the theoretical von Kármán spectra, and they are valid over a normalized frequency range up to 400 rad, which is suitable for helicopter handling quality analysis in atmospheric turbulence with low flight speed and large turbulence scale lengths. The present turbulence model significantly extends the capability of helicopter handling quality analysis to the higher-altitude and lower-velocity flight conditions compared with the current distributed turbulence model. Nomenclature H u;v;w s = shaping filters of turbulence velocity components to random inputs L u;v;w = scale lengths for turbulence spectra, m p; q; r = angular rates about body axes, deg ∕s t = time, s u, v, w = translational velocity components along body axes, m∕s V = aircraft flight speed, m∕s x, x i = random inputs of continuous and discrete shaping filters y, y i = turbulence outputs of continuous and discrete shaping filters γ u;v;w = V∕L u;v;w , 1∕s Δt = sampling period, s Δu, Δv, Δw = turbulence velocity components, m∕s σ u;v;w = standard deviations of turbulence velocity components, m∕s Φ, Θ, Ψ = aircraft Euler angles, rad Φ X ω, Φ DX ω = power spectral densities of continuous and discrete random inputs Φ Y ω, Φ DY ω = power spectral densities of continuous and discrete turbulence outputs Φ u;v;w ω = power spectral densities of turbulence velocity components ω; ω D = continuous and discrete angular frequencies, rad∕s ω D max = Nyquist frequency, rad∕s

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Section: Simulation Model For Helicopter Flight In Atmospheric Tmentioning

confidence: 99%