Radial Decomposition of Blade Vibration to Identify a Stall Flutter Source in a Transonic Fan

Rendu, Quentin; Vahdati, Mehdi; Salles, Loïc

doi:10.1115/1.4044484

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…In practice, the combination of reduced frequency and modeshape is not arbitrary: the same blade will be characterised by very different reduced frequencies whether it is vibrating in a pure twist or in a flap mode. Moreover, this combination also depends on the machine being considered: the flap mode of a fan blade will have a lower reduced frequency [17,21] compared to an embedded compressor blade [22]. Nevertheless, to cover the whole spectrum of combinations, modeshape and frequency in this study are assumed to be independent from each other during training of the surrogate model and, therefore, take large intervals.…”

Section: Bladementioning

confidence: 99%

Machine Learning Based Sensitivity Analysis of Aeroelastic Stability Parameters in a Compressor Cascade

Rauseo

Vahdati

Zhao

2021

IJTPP

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Aeroelastic instabilities such as flutter have a crucial role in limiting the operating range and reliability of turbomachinery. This paper offers an alternative approach to aeroelastic analysis, where the sensitivity of aerodynamic damping with respect to main flow and structural parameters is quantified through a surrogate-model-based investigation. The parameters are chosen based on previous studies and are represented by a uniform distribution within applicable intervals. The surrogate model is an artificial neural network, trained and tested to achieve an error within 1% of the test data. The quantity of interest is aerodynamic damping and the datasets are obtained from a linearised aeroelastic solver. The sensitivity of aerodynamic damping with respect to the input variables is obtained by calculating normalised gradients from the surrogate model at specific operating conditions. The results show a quantitative comparison of sensitivity across the different input parameters. The outcome of the sensitivity analysis is then used to decide the most appropriate action to take in order to induce stability in unstable operating conditions. The work is a preliminary study, carried out on a simplified two dimensional compressor cascade and it is aimed at proving the validity of a data-driven approach in studying the aeroelastic behaviour of turbomachinery. To the best of the authors’ knowledge, this is the first time a data-driven flutter model has been investigated. The initial results are encouraging, indicating that this approach is worth pursuing in the future. The presented framework can be used as a redesign tool to enhance the flutter stability of an existing blade.

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Section: Bladementioning

confidence: 99%

Machine Learning Based Sensitivity Analysis of Aeroelastic Stability Parameters in a Compressor Cascade

Rauseo

Vahdati

Zhao

2021

IJTPP

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“…4). In this plot, the Mach number is shown on the suction surface of the blade when operating at a constant speed, both for a [26] that, the instability outboard of 80% blade span is caused by the vibration and the flow at 65% height. The above finding indicates that 2D models (at representative heights) will not be appropriate for stall flutter computations as this phenomenon is driven by 3D flow features.…”

Section: Fig 2: Demonstration Of Flutter Bitementioning

confidence: 99%

A Review of Computational Aeroelasticity Of Civil Fan Blades

Vahdati

Lee²,

Sureshkumar

2020

JGPP

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This paper presents a review of aeroelasticity research concerning fan blades in modern civil aircraft engines. It summarises the research carried out at the Rolls-Royce Vibration University Technology Centre (VUTC) at Imperial College over the past 25 years. The purpose of this paper is to gather information on all the aeroelastic issues observed for civil aero-engine fan blades into one document and provide a useful synopsis for other researchers in the field. The results presented here are based on numerical methods but wherever possible data from experiments are used to verify the numerical findings. For cases where such datasets do not exist fundamental principles, engine observations and engineering judgement are used to support the numerical results. Numerical methods offer a cheaper alternative to rig tests, especially in cases of blade failure, and can also provide more information about the nature of instabilities, which can be useful in the design of future civil aircraft engines. In fact, in cases such as crosswind testing that use smaller rig-scale blades, such results can even be more representative of real engine flows.

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“…In the lower flow rates of fluid machinery, ‘stall’ is one of the most detrimental phenomena that has various instabilities due to an increase in incidence angle. Based on the theoretical and empirical discussion as well known in our field, unfavorable factors that can be contained in the stalling flow rates are as follows: positive gradients (degradation) on performance curve ( – or – ) 1 , 2 ; backflow and rotating stall inside inlet passage 3 , 4 ; blade fluctuating stress 5 ; pressure fluctuation 6 ; vibration 7 , 8 ; noise 9 , 10 . Here, the backflow should be developed from the blade (rotor) leading edge (LE) and gradually increases in the spanwise and streamwise direction as the flow rate decreases, whereas the intensity for the other factors such as pressure fluctuation, vibration, and noise may not be inversely proportional to the flow rate.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation on functional limitations of the anti-stall fin for an axial fan: one-factor analyses

Kim

Yang

Lee

et al. 2022

Sci Rep

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The stall in an axial fan is directly related to detrimental phenomena such as performance degradation, vibration, noise, and flow instability at low flow rates. As a kind of passive control method to handle the stall, two-dimensional plates so-named anti-stall fin (ASF) were suggested by ourselves and were attached inside the casing. In this study, the ASF's effect on the internal flow pattern was visually investigated in the flow passage, and its tendency was discussed with the performance curve. Subsequently, the ASF's functional limitations for various design parameters, which the ASF can derive aerodynamically, were presented as the primary focus of this study. Each one-factor analysis was performed, and the internal flow pattern was observed in parallel at the point where the ASF lost its function. For the radial length, axial length, number of fins, and positive-tangential angle, the ASF almost retained its function up to the limitation to prevent instability but radically lost its function at a certain flow rate. For the axial gap and negative-tangential angle, the ASF gradually lost its function. Mostly, this study was based on numerical analysis, and the performance was validated through experimental tests.

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Radial Decomposition of Blade Vibration to Identify a Stall Flutter Source in a Transonic Fan

Cited by 7 publications

References 6 publications

Machine Learning Based Sensitivity Analysis of Aeroelastic Stability Parameters in a Compressor Cascade

Machine Learning Based Sensitivity Analysis of Aeroelastic Stability Parameters in a Compressor Cascade

A Review of Computational Aeroelasticity Of Civil Fan Blades

Numerical investigation on functional limitations of the anti-stall fin for an axial fan: one-factor analyses

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