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Record W4415625360 · doi:10.3390/pr13113436

Study on the Fracture Characteristics and Mechanisms of Iron Ore Under Dynamic Loading

2025· article· en· W4415625360 on OpenAlexaff
Yilin Tian, Peng Xu, Hua Li, Junjie Li, Shiqing Zhou, Yanting Chen, Zhibo Lin

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsIron Ore Company (Canada)
FundersNational Natural Science Foundation of China
KeywordsSplit-Hopkinson pressure barStrain rateDynamic loadingIron oreToughnessFracture (geology)Dynamic compactionShear (geology)

Abstract

fetched live from OpenAlex

The dynamic fracture process of iron ore under blast loading is an important manifestation of ore fragmentation. To investigate the dynamic fracturing process of iron ore, Hopkinson bar experiments were conducted under different impact loads. The results indicate that under low strain rates, the dynamic stress–strain curve of iron ore exhibits compaction, elastic, and failure stages. However, as the strain rate increases, the compaction stage becomes less distinct, while the elastic modulus decreases and the failure strength increases, indicating the material toughness was enhanced at high strain rate. Moreover, under high strain rates, a significant increase in shear strain promotes the formation of tensile–shear cracks in the ore. In addition, based on the fragmentation of iron ore at different impact pressure, there exists a certain impact pressure, at which the proportion of large fragments decreases only slightly, while the amount of small fragments increases markedly. These findings provide important insights for optimizing fragmentation and improving blasting effectiveness.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.296

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 head0.019
GPT teacher head0.298
Teacher spread0.278 · 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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