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Record W4406903433 · doi:10.1080/02533839.2025.2453662

Double-step hydroforming mechanism of metal bipolar plates for hydrogen fuel cells

2025· article· en· W4406903433 on OpenAlexaff
Jie Zhang, Zhiqiang Chen, Yun Zeng, Han Zhang

Bibliographic record

VenueJournal of the Chinese Institute of Engineers · 2025
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsHydroformingMaterials scienceMechanism (biology)Fuel cellsHydrogenMetalComposite materialHydrogen fuelNuclear engineeringStructural engineeringMechanical engineeringMetallurgyTube (container)Chemical engineeringEngineeringChemistryPhysics

Abstract

fetched live from OpenAlex

Bipolar plate (BPP) is a key component affecting the efficiency and economy of hydrogen fuel cell, and multi-stage forming process is an effective method to improve the forming quality of metal BPP. In this thesis, a double-step hydroforming model of BPP with a serpentine flow field is established and numerical simulation is used to study the influence of die geometry and process parameters on the forming quality of BPP. The results show that the double-step hydroforming process is superior to the conventional hydroforming. The higher the forming pressure, the better the quality of the BPP, but it will result in severe local thinning; both high and low pre-forming pressures result in lower forming quality; rib fillet have less effect on forming results; the larger the flow channel fillet radius, the more uniform the forming thickness of the plate, and a noticeable second thinning area will appear at the corner when the radius is less than 0.2 mm; the draft angle and the ratio of the flow channel width have a minor impact on the double-step hydroforming effect; the thinner the plate thickness, the easier the forming, but it is prone to rupture during the forming process.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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