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Record W2315429721 · doi:10.1061/40996(330)345

Structure Optimal Design of Maglev Train Car Body

2009· article· en· W2315429721 on OpenAlex
Hong-qi Tian, Ping Xu

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

VenueLogistics · 2009
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsMaglevFinite element methodStiffnessDisplacement (psychology)Optimal designStructural engineeringAdinaMechanical engineeringComputer scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The sidewall stiffness of the Chinese Maglev train is so weak that it disagrees with the situation of passing each other in open air at high speed. The first problem of the structure optimal design to settle down is to enhance the sidewall stiffness. The car body weight was defined as the object variables, and the deflections and stresses in the sidewall were defined as state variables, and the board thickness of profiled extrusion material and composite plate were defined as design variables. Combined with finite element analysis technology, the optimal design of the maglev train body structure was carried out by mathematical programming approach method. The transversal displacement in the sidewall from load case 6 reduced from 3.8241mm to 3.2mm and from load case 7 reduced from 2.9153mm to 2.3531mm after 18 times iteration calculation optimized. The displacement value was decreased by 20%. The proposed method can be used to optimize the existing and new car body structure of domestic maglev train.

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: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.339

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 head0.014
GPT teacher head0.212
Teacher spread0.199 · 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