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Record W2071790473 · doi:10.5539/mas.v3n11p24

Study on the Growth of the Fatigue Crack under Flexural Moment

2009· article· en· W2071790473 on OpenAlexvenueno aff
Wen Zhong, Jiajie Hu, Jun Guo, Zibin Li, Quyue Liu

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
FundersSouthwest Jiaotong University
KeywordsStructural engineeringFlexural strengthMoment (physics)Materials scienceBending momentEngineering

Abstract

fetched live from OpenAlex

Vehicles will suffer various loads in the moving process, and under the functions of various factors such as the ovality of the wheel, and the impacts to the rails when the vehicle passes the curve rail, the rail slot and the turnoff, the wheels will inevitably add normal loads to the rails, and the flexural moment will influence the generating and growth of the fatigue crack. To study the influences of flexural moment on the fatigue performances of rails with various materials, the experiments about the growth performances of the fatigue cracks of two kinds of materials including U71Mn and PD3 under the flexural moment are made on the NENE-2 fretting test machine, and according to the experiment results, the measures to reduce the fatigue of rails are proposed. The research analysis and experiment results indicate that the fatigue crack generates from the focal point of stress at first, and the main shearing stress is very important, and when the static load functions, the direction of crack growth will change by a large angle, but when the dynamic load functions, the direction of crack growth will be stable, and under same loading speed, the fatigue crack of PD3 rail more easily generates and grows than U71Mn rail. To prevent and reduce the fatigue of rails, the selection of rail should accord with the type of the route and the actual working environment of the rail, and in the heavy freight route, the PD3 rail with high mechanical strength should be selected, and in the high-speed route, the U71Mn rail should be selected.

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: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.253

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 science0.0010.000
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 head0.023
GPT teacher head0.233
Teacher spread0.210 · 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
Published2009
Admission routes1
Has abstractyes

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