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Record W4411102604 · doi:10.5267/j.esm.2025.5.002

Nonlinear support effects on the aeroelastic stability of multi-stage turbine rotors

2025· article· en· W4411102604 on OpenAlexvenueno aff
Masoud Yousefi, R.D. Firouz-Abadi, Hassan Haddadpou

Bibliographic record

VenueEngineering Solid Mechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAeroelasticityNonlinear systemStability (learning theory)Materials scienceTurbineStructural engineeringStage (stratigraphy)Computer scienceEngineeringMechanical engineeringPhysicsAerospace engineeringAerodynamicsGeology

Abstract

fetched live from OpenAlex

This study presents a comprehensive analysis of aeroelastic stability in multi-stage turbine rotors mounted on nonlinear supports. A high-fidelity dynamic model is developed by coupling the structural behavior of a rotating shaft–disk–blade assembly with quasi-steady aerodynamic forces. The system incorporates nonlinear stiffness and damping in the bearing supports, and the governing equations of motion are derived using the Lagrangian method. Aerodynamic forces are modeled using cascade theory for incompressible subsonic flow and integrated with structural dynamics through coordinate transformation. The resulting nonlinear system is solved using the Runge-Kutta method, and its stability characteristics are investigated via bifurcation diagrams and Poincaré maps. A detailed parametric study is conducted to examine the influence of aerodynamic parameters, structural parameters and support characteristics on rotor response. Results show that nonlinear supports significantly alter stability boundaries, reduce critical flutter speeds, and introduce multi-periodic dynamic behavior. These findings provide valuable insights into the design and tuning of support systems to enhance the dynamic robustness of turbomachinery.

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.

How this classification was reachedexpand

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 score0.514
Threshold uncertainty score0.722

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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