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Space-time accurate finite-state dynamic inflow modeling for aeromechanics of rotorcraft
Wake inflow modeling is a crucial issue in the development of efficient and high-fidelity simulation tools for rotorcraft flight dynamics and aeroelasticity. This paper proposes a space-time accurate, finite-state, dynamic wake inflow modeling suitable for conventional and innovative rotor configura...
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Published in: | Aerospace science and technology 2019-12, Vol.95, p.105454, Article 105454 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Wake inflow modeling is a crucial issue in the development of efficient and high-fidelity simulation tools for rotorcraft flight dynamics and aeroelasticity. This paper proposes a space-time accurate, finite-state, dynamic wake inflow modeling suitable for conventional and innovative rotor configurations, based on simulations provided by high-fidelity aerodynamic solvers. It relates the coefficients of a rotor-disc, radial-azimuthal wake inflow distribution to the rotor kinematic variables, and is capable to take into account the intrinsic periodicity of aerodynamic responses of rotors in steady forward flight. The proposed inflow modeling consists of a three-step process: (i) numerical evaluation of wake inflow due to perturbations of rotor kinematic variables, (ii) determination of transfer functions of multi-harmonic components of a suitable set of inflow coordinates, followed by (iii) their rational approximation and transformation into time domain to derive the differential operators governing multi-harmonic dynamics. The numerical investigation concerns the derivation of finite-state inflow models for single and coaxial rotors, through application of an aerodynamic boundary-element-method solver for potential flows. These are successfully validated by comparison with inflows directly calculated by the aerodynamic tool for arbitrary rotor perturbations. |
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ISSN: | 1270-9638 1626-3219 |
DOI: | 10.1016/j.ast.2019.105454 |