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Record W2047155601 · doi:10.1115/gt2013-95005

Investigation of Efficient CFD Methods for the Prediction of Blade Damping

2013· article· en· W2047155601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsAnsys (Canada)
Fundersnot available
KeywordsComputational fluid dynamicsAerodynamicsBlade (archaeology)Blade element momentum theoryTransonicStructural engineeringAxial compressorRotor (electric)Blade element theoryMach numberComputer scienceAeroelasticityEngineeringMechanical engineeringGas compressorMechanicsTurbine bladeAerospace engineeringTurbinePhysics

Abstract

fetched live from OpenAlex

Accurate and efficient prediction of blade damping is one essential element in the engineering of durable and reliable compressors and turbines. Over the years, a variety of empirical and linearized methods have been developed and used, and have served well. Recently, the development of efficient unsteady CFD methods combined with an expansion in available and affordable computing power has enabled CFD analysis of blade damping. This paper looks at the prediction of aerodynamic blade damping using some recently developed CFD methods. Unsteady CFD methods are used to predict the fluid flow in a transonic fan rotor, with tip Mach number of about 1.4. Deformation of the blade is determined from a mechanical pre-stressed modal analysis. In this investigation, blade motion for the first bending moments is prescribed in the CFD code, for a range of nodal diameters. After periodic unsteady solutions are obtained, damping coefficients are calculated based on the predicted blade forces and the specified blade motion. Traditional unsteady CFD methods require the simulation of many blades in a given row, depending on the nodal diameter. For instance, for a nodal diameter of four, a wheel with 22 blades would require simulation of eleven blades. Computational methods have been developed which now enable simulation of only a few (1 or 2) blades per row yet yield the full sector solution, thus providing considerable savings in computing time and machine resources. The properties of the available methods vary, but one method, the Fourier Transformation method, has the property that it is frequency preserving, and hence suitable for the present task. Fourier Transformation predictions, for a variety of nodal diameters, are compared with full sector predictions. Positive damping was predicted for this range of nodal diameters at design speed near peak efficiency operating condition indicating a stable system. The Fourier Transformation predictions for blade aerodynamic damping match very closely the reference full sector solutions. The Fourier transformation methods also provide solutions 3.5 times faster than average periodic reference cases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.109

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.254
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it