Active/Passive Optimization of Helicopter Rotor Blades for Improved Vibration, Noise, and Performance Characteristics.

This dissertation describes an active/passive approach to optimum design of helicopter rotor blades for reduced vibration and noise levels, as well as reduced power consumption. In the active/passive approach, structurally optimized rotor blade designs obtained from surrogate based optimization (SBO) methods were augmented with active control flaps (ACF’s). Multi-objective function optimization techniques were employed to obtain active/passive configurations corresponding to the best trade-offs between vibration, noise, and performance characteristics of the rotor blades in forward flight. The focus of the initial portion of the work was on the effectiveness of SBO for vibration reduction in forward flight. It was determined that SBO methods could be used to conduct global searches of the design space for reduced vibration designs, even though the surrogates were not accurate everywhere in the design space. Subsequently, it was demonstrated that the Efficient Global Optimization (EGO) algorithm was superior to conventional SBO techniques for vibration reduction at low speed forward flight where blade-vortex interaction (BVI) induces high vibration levels, and at high speeds where dynamic stall is the dominant source of vibration. Since the best design for low speed forward flight differed from the best design for high speed flight, multi-objective function optimization techniques were necessary to find the best trade-off designs for vibration reduction over the entire flight envelope. To this end, the EGO algorithm was extended for surrogate based multi-objective function optimization and the results demonstrate that the modified EGO algorithm located a single trade-off design with vibration characteristics similar to the best designs for both flight conditions. Finally, ACF’s were used to enhance vibration, noise, and performance characteristics of structurally optimized blades. Using a closed-loop control algorithm and multi-objective function optimization based on EGO, a versatile active/passive design for reduced vibration and noise levels due to BVI was obtained. The design corresponds to $68 - 91$ vibration reduction and a $2.3 - 2.7$ db decrease in the maximum noise level. In addition, the active/passive approach was used for vibration reduction over the entire flight envelope, while enhancing performance at high speed flight and constraining noise levels at low speed forward flight from increasing.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60833/1/bglaz_1.pd

Reduced-Order Nonlinear Unsteady Aerodynamic Modeling Using a Surrogate-Based Recurrence Framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83597/1/AIAA-52068-600.pd

Vibration Reduction and Performance Enhancement of Helicopter Rotors Using an Active/Passive Approach

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76405/1/AIAA-2008-2178-413.pd

Surrogate Based Optimization of Helicopter Rotor Blades for Vibration Reduction in Forward Flight

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76201/1/AIAA-2006-1821-974.pd

A moderate deflection composite helicopter rotor blade model with an improved cross-sectional analysis

AbstractThe compatibility between a composite beam cross-sectional analysis based on the variational asymptotic approach, and a helicopter rotor blade model which is part of a comprehensive rotorcraft analysis code is examined. It was found that the finite element cross-sectional analysis code VABS can be combined with a moderate deflection rotor blade model in spite of the differences between the formulations. The new YF/VABS rotor blade model accounts for arbitrary cross-sectional warping, in-plane stresses, and moderate deflections. The YF/VABS composite rotor blade model was validated against experimental data and various rotor blade analyses by examining displacements and stresses under static loads, as well as aeroelastic stability of a composite rotor blade in hover, and forward flight vibratory hubloads of a four bladed composite rotor

Efficient Global Optimization of Helicopter Rotor Blades for Vibration Reduction in Forward Flight

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77207/1/AIAA-2006-6997-436.pd

A Surrogate Based Approach to Reduced-Order Dynamic Stall Modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83591/1/AIAA-2010-3042-203.pd

Approximate Modeling of Unsteady Aerodynamics for Hypersonic Aeroelasticity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83554/1/AIAA-52860-446.pd

Machine-learning parameter tracking with partial state observation

Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and control. Existing machine-learning methods require full state observation of the underlying system and tacitly assume adiabatic changes in the parameter. Formulating an inverse problem and exploiting reservoir computing, we develop a model-free and fully data-driven framework to accurately track time-varying parameters from partial state observation in real time. In particular, with training data from a subset of the dynamical variables of the system for a small number of known parameter values, the framework is able to accurately predict the parameter variations in time. Low- and high-dimensional, Markovian and non-Markovian nonlinear dynamical systems are used to demonstrate the power of the machine-learning based parameter-tracking framework. Pertinent issues affecting the tracking performance are addressed.Comment: 5 pages, 4 figure