Comparison of CAMRAD II and RCAS Predictions of Tiltrotor Aeroelastic Stability

Yeo, Hyeonsoo; Bosworth, Jeffrey; Acree, C. W.; Kreshock, Andrew R.

doi:10.4050/jahs.63.022001

Cited by 21 publications

(4 citation statements)

References 16 publications

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“…Analytical modeling and design of the Apache helicopter were performed by Jones and Kunz [70,71] using CAMRAD II, which uses LLT for blade modeling. Yeo et al [72,73] studied tail rotor flutter, exploring a wide range of design parameters and examining their effects on whirl flutter speed. Jain et al [74] studied rotor performance in hover and forward flight and compared their results with experimental data.…”

Section: Lifting Line Methodsmentioning

confidence: 99%

Review of vortex methods for rotor aerodynamics and wake dynamics

et al. 2022

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Electric vertical take-off and landing (eVTOL) aircraft with multiple lifting rotors or prop-rotors have received significant attention in recent years due to their great potential for next-generation urban air mobility (UAM). Numerical models have been developed and validated as predictive tools to analyze rotor aerodynamics and wake dynamics. Among various numerical approaches, the vortex method is one of the most suitable because it can provide accurate solutions with an affordable computational cost and can represent vorticity fields downstream without numerical dissipation error. This paper presents a brief review of the progress of vortex methods, along with their principles, advantages, and shortcomings. Applications of the vortex methods for modeling the rotor aerodynamics and wake dynamics are also described. However, the vortex methods suffer from the problem that it cannot deal with the nonlinear aerodynamic characteristics associated with the viscous effects and the flow behaviors in the post-stall regime. To overcome the intrinsic drawbacks of the vortex methods, recent progress in a numerical method proposed by the authors is introduced, and model validation against experimental data is discussed in detail. The validation works show that nonlinear vortex lattice method (NVLM) coupled with vortex particle method (VPM) can predict the unsteady aerodynamic forces and complex evolution of the rotor wake.

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Section: Lifting Line Methodsmentioning

confidence: 99%

Review of vortex methods for rotor aerodynamics and wake dynamics

et al. 2022

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“…Additionally, modeling software such as Fatigue, Aerodynamics, Structures, and Turbulence (FAST) (Jonkman and Buhl, 2005); BLADED (DNV, 2018); and HAWC2 (Larsen and Hansen, 2007) have been developed for wind turbine design applications. Through leveraging these analysis tools, numerous subtopics of interest have been investigated, ranging from multi-rotor performance prediction (Liew et al, 2020;Conley and Shirazi, 2021) to aeroelasticity (Kecskemety and McNamara, 2016;Yeo et al, 2018). When applied to the early stages of rotor optimization, typically mid-fidelity tools provide an excellent path to identifying a limited design space from which an optimal solution can be obtained.…”

Section: Introductionmentioning

confidence: 99%

A data-driven reduced-order model for rotor optimization

Peters¹,

Silva²,

Ekaterinaris³

2023

Wind Energ. Sci.

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Abstract. For rotor design applications, such as wind turbine rotors or urban air mobility (UAM) rotorcraft and flying-car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving reasonably accurate but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed-pressure predictions on rotor blades subjected to topology change due to variations in the twist and taper ratio. Rotor twist, θ, was varied between 0, 10, 20, and 30∘, while the taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases: a high-pitch or a high-thrust rotor in hover, a low-pitch or a low-thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed that reasonably accurate distributed-load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 h on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotors in forward flight, cost was reduced from 20 h on 440 cores to less than a second on a single core. The POD ROM was used to carry out a design optimization of the rotor such that the figure of merit was maximized for hovering-rotor cases and the lift-to-drag effective ratio was maximized in forward flight.

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“…Additionally, modeling softwares such as Fatigue, Aerodynamics, Structures, and Turbulence (FAST) (Jonkman and Buhl Jr, 2005), BLADED (DNV, 2018), and HAWC2 (Larsen and Hansen, 2007) have been developed for wind turbine design applications. Through leveraging these analysis tools numerous sub-topics of interest have been investigated ranging from multi-rotor performance prediction (Liew et al, 2020;Conley and Shirazi, 2021) to aeroelasticity (Kecskemety and McNamara, 2016;Yeo et al, 2018). When applied to the early stages of rotor optimization, typically mid-fidelity tools provide an excellent path to identifying a limited design space from which an optimal solution can be obtained.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Reduced Order Model for Rotor Optimization

Peters

Silva²,

Ekaterinaris³

2022

Preprint

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Abstract. For rotor design applications, such as wind turbine rotor or Urban Air Mobility (UAM) rotorcraft and flying car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex turbulent flow fields. One potential path for deriving high-fidelity but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD) based reduced order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed pressure predictions on rotor blades subjected to topology change due to variations in twist and taper ratio. Rotor twist, θ, was varied between 0°, 10°, 20°, and 30° while taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases; a high pitch or a high thrust rotor in hover, a low pitch or a low thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed highly accurate distributed load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 hours on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotor in forward flight cost was reduced from 20 hours on 440 cores to less than a second on a single core. The POD ROM was used to undergo a design optimization of the rotor such that figure of merit was maximized for hovering rotor cases and the lift to drag effective ratio was maximized in forward flight.

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Comparison of CAMRAD II and RCAS Predictions of Tiltrotor Aeroelastic Stability

Cited by 21 publications

References 16 publications

Review of vortex methods for rotor aerodynamics and wake dynamics

Review of vortex methods for rotor aerodynamics and wake dynamics

A data-driven reduced-order model for rotor optimization

A Data-Driven Reduced Order Model for Rotor Optimization

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