Vibration Reduction of Mistuned Bladed Disks via Piezoelectric-Based Resonance Frequency Detuning

Recent trends in turbomachinery blade technology have led to increased use of monolithically constructed bladed disks (blisks). Although offering a wealth of performance benefits, this construction removes the blade-attachment interface present in the conventional design, thus unintentionally removing a source of friction-based damping needed to counteract large vibrations during resonance passages. This issue is further exacerbated in the presence of blade mistuning that arises from small imperfections from otherwise identical blades and are unavoidable as they originate from manufacturing tolerances and operational wear over the lifespan of the engine. Mistuning is known to induce vibration localization with large vibration amplitudes that render blades susceptible to failure induced by high-cycle fatigue. The resonance frequency detuning (RFD) method reduces vibration associated with resonance crossings by selectively altering the blades\u27 structural response. This method utilizes the variable stiffness properties of piezoelectric materials to switch between available stiffness states at some optimal time as the excitation frequency sweeps through a resonance. For a single-degree-of-freedom (SDOF) system, RFD performance is well defined. This research provides the framework to extend RFD to more realistic applications when the SDOF assumption breaks down, such as in cases of blade mistuning. Mistuning is inherently random; thus, a Monte Carlo analysis performed on a computationally cheap lumped-parameter model provides insight into RFD performance for various test parameters. Application of a genetic algorithm reduces the computational expense required to identify the optimal set of stiffness-state switches. This research also develops a low-order blisk model with blade-mounted piezoelectric patches as a tractable first step to apply RFD to more realistic systems. Application of a multi-objective optimization algorithm produces Pareto fronts that aid in the selection of the optimized patch parameters. Experimental tests utilizing the academic blisk with the optimized patches provides validation

Aeronautical engineering: A continuing bibliography with indexes (supplement 255)

This bibliography lists 529 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Data-driven modelling of compressor stall flutter

Modern aircraft engines need to meet ever more stringent requirements that greatly increase the complexity of design, which strives for enhanced performance, reduced operating costs, emissions and noise simultaneously. The drive for performance leads to the development of thin, lightweight, highly loaded fan and compressor blades which are increasingly more prone to incur high, sustained vibratory stresses and aeroelastic problems such as flutter. The current practice employs preliminary design tools for flutter that are often based on empiricism or simplified analytical models, requiring extensive use of computational fluid dynamics to verify aeroelastic stability. As the industry moves to new designs, fast and accurate prediction tools are needed. In this thesis, data-driven techniques are employed to model the aeroelastic response of compressor blades. Machine learning has been applied to a plethora of engineering problems, with particular success in the field of turbulence modelling. However, conventional, black-box data- driven methods based on simple input parameters require large databases and are unable to generalise. In this work a combination of machine learning techniques and reduced order models is proposed to address both limitations at the same time. Previous knowledge of flutter is introduced in the physics guided framework by formulating relevant, steady state input features, and by injecting results from low-fidelity analytical models. The models are tested on several unseen cascades and it is found that training on even a single geometry yields accurate results. The models developed here allow flutter prediction of fan and compressor flutter stability based on the steady state flow only without a need for any CPU intensive unsteady simulations. Hence, one can predict flutter stability of a given blade for different mechanical properties (mode shape, frequency) at near zero additional cost once the mean flow is known. Moreover, for fan flutter, the model developed here can be integrated with available analytical models of intake to analyse the consequences of intake properties, such as length and acoustic liners location, on the stability of fan blades. The EU goal of climate-neutrality by 2050 requires novel design concepts in aviation which is unachievable without complimentary novel prediction and design tools. The research presented in this thesis will allow one to explore the design space for flutter stability based on steady flow only, and hence offers such an alternative. To the best of the author’s knowledge, no previous research is available on modelling of compressor stall flutter with data-driven techniques.Open Acces