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Record W4407176215 · doi:10.3390/app15031597

Crack Propagation in Axial-Flow Fan Blades Under Complex Loading Conditions: A FRANC3D and ABAQUS Co-Simulation Approach

2025· article· en· W4407176215 on OpenAlexafffund
Mariem Ben Hassen, Slim Ben-Elechi, Hatem Mrad

Bibliographic record

VenueApplied Sciences · 2025
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceStructural engineeringMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Since fan blades are exposed to fatigue, and in some cases harsh loading conditions, they may exhibit fracture failures due to crack propagation, resulting in significant losses. Previous studies of crack propagation in blades are mainly confined to either simplified blade geometry or loads, resulting in a significant discrepancy between the simulated crack propagation and the real blade propagation behavior, while it is lacking for challenging shapes and loads. A co-simulation approach of FRANC3D and ABAQUS was developed to study the crack propagation of an axial-flow fan blade subjected to centrifugal, aerodynamic, and combined loads. The projected approach is validated with results obtained from analytical calculations and experiments. Meanwhile, making use of benchmarks, the Stress Intensity Factor (SIF) and the prediction of mixed-mode crack growth path are validated. Considering various loads, the crack propagation path response for the fan blade is computed for different growth steps. The results pinpoint that the crack propagation length of the crack tip center is maximum under centrifugal loading. However, the aerodynamic load led to a maximum propagation length of the crack tip endpoints. In addition, the combined force of centrifugal and aerodynamic loads limits the crack from growing.

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 score0.626
Threshold uncertainty score0.370

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 head0.030
GPT teacher head0.284
Teacher spread0.255 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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