Flutter Analysis of a Transonic Steam Turbine Blade with Frequency and Time-Domain Solvers

Frey, Christian; Ashcroft, Graham; Kersken, Hans-Peter; Schlüß, Daniel

doi:10.3390/ijtpp4020015

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…The higher pressure harmonics are one order of magnitude below the first one, but they are present around the shock impingement region although the vibration amplitude already reported is quite low. To further confirm the excellent agreement between TRAF and TRACE, the authors also include in the plot the single value of damping obtained with the non linear version of TRACE for the most unstable nodal diameter (see Frey et al (2019)): TRAF and TRACE practically predict the same value. An additional qualitative comparison can be done by looking at the local work distribution on the blade surface for the most unstable nodal diameter nd=+13 (see Fig.…”

Section: Unsteady Flutter Resultsmentioning

confidence: 81%

“…Starting from this geometry, the open flutter test case has been established by Qi et al (2017) by the definition of flow conditions (compatible with steam turbine at design conditions) and blade structural characteristics. The numerical flutter test-case is already fully described in previous publications (Qi et al (2017); Frey et al (2019)) and only the main information are reported hereafter. The stage depicted in Fig.…”

Section: Numerical Test Casementioning

confidence: 99%

“…The fist part of the paper describes the flutter assessment results of the open flutter test case obtained with an uncoupled non-linear flutter solver in time domain and compares them with numerical results coming from different numerical solvers already presented in the literature (Qi et al (2017); Frey et al (2019)). The results shows a good agreement among different codes and confirm that blade area subjected to non linear phenomena (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Aeroelastic investigation of an open 3D steam turbine test case and blade shape optimization with improved flutter behavior

Pinelli

Castelli

Vanti

et al. 2021

European Conference on Turbomachinery Fluid Dynamics and Thermodynamics

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The paper describes the flutter stability assessment of a steam turbine rotor and the application of a multi-disciplinary shape optimization procedure to obtain a new blade geometry with higher efficiency and an improved flutter stability. The investigated test case consists of a steam turbine last stage representative of modern industrial steam turbine blading, firstly presented by Durham University and intensively investigated during last years. The aerodamping results on the original geometry, obtained by a non-linear method, are compared with those already published by other researches with different flutter approaches. The present results are in complete agreement with numerical investigations already present in the literature and show a high blade instability for the first bending mode. Starting from this unstable geometry, a shape optimization procedure with aeromechanical constraints has been applied in order to obtain a blade with improved performances from both the aerodynamic and the flutter point of view. The optimum profile shows a higher efficiency and a flutter instability reduction. Those aspects are exhaustively discussed and comparisons with the baseline geometry are reported. KEYWORDSCFD, FLUTTER, OPTIMIZATION, STEAM TURBINE NOMENCLATURE ṁ mass flow p pressure Greek: η T T total-to-total stage efficiency ω * reduced frequency, = ω * c/v out σ static stress ξ logarithmic decrement (log.dec.

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Section: Unsteady Flutter Resultsmentioning

confidence: 81%

Section: Numerical Test Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aeroelastic investigation of an open 3D steam turbine test case and blade shape optimization with improved flutter behavior

Pinelli

Castelli

Vanti

et al. 2021

European Conference on Turbomachinery Fluid Dynamics and Thermodynamics

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“…The current state-of-the-art technique relies on solving the steady Reynolds-averaged Navier-Stokes (RANS) equations with second-order accurate finite volume schemes in combination with appropriate RANS models, analyzing the steady-state flow field. Unsteady effects are assessed using unsteady RANS approaches or, to save computation time, frequency domain methods such as harmonic balance [1,2]. Yet, all the mentioned steady and unsteady approaches include RANS models, which are generally well tuned and mature at and close to the aerodynamic design points.…”

Section: Introductionmentioning

confidence: 99%

Statistical Error Estimation Methods for Engineering-Relevant Quantities From Scale-Resolving Simulations

Bergmann

Morsbach

Ashcroft

et al. 2021

Journal of Turbomachinery

Self Cite

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Scale-resolving simulations, such as large eddy simulations, have become affordable tools to investigate the flow in turbomachinery components. The resulting time-resolved flow field is typically analyzed using first- and second-order statistical moments. However, two sources of uncertainty are present when recording statistical moments from scale-resolving simulations: the influence of initial transients and statistical errors due to the finite number of samples. In this paper, both are systematically analyzed for several quantities of engineering interest using time series from a long-time large eddy simulation of the low-pressure turbine cascade T106C. A set of statistical tools to either remove or quantify these sources of uncertainty is assessed. First, the Marginal Standard Error Rule is used to detect the end of the initial transient. The method is validated for integral and local quantities and guidelines on how to handle spatially varying initial transients are formulated. With the initial transient reliably removed, the statistical error is estimated based on standard error relations considering correlations in the time series. The resulting confidence intervals are carefully verified for quantities of engineering interest utilizing cumulative and simple moving averages. Furthermore, the influence of periodic content from large scale vortex shedding on the error estimation is studied. Based on the confidence intervals, the required averaging interval to reduce the statistical uncertainty to a specific level is indicated for each considered quantity.

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“…Two-dimensional non-reflecting boundary conditions are applied throughout the study. The solver has been validated against other established codes [19,20].…”

Section: Flow Solvermentioning

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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Flutter Analysis of a Transonic Steam Turbine Blade with Frequency and Time-Domain Solvers

Cited by 10 publications

References 19 publications

Aeroelastic investigation of an open 3D steam turbine test case and blade shape optimization with improved flutter behavior

Aeroelastic investigation of an open 3D steam turbine test case and blade shape optimization with improved flutter behavior

Statistical Error Estimation Methods for Engineering-Relevant Quantities From Scale-Resolving Simulations

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

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