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Record W4407398067 · doi:10.1016/j.dt.2025.02.004

Impact safety of CL-20-based explosive charge using detonation driving high velocity fragments

2025· article· en· W4407398067 on OpenAlex

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

VenueDefence Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsDetonationExplosive materialDetonation velocityCharge (physics)Materials scienceAutomotive engineeringEngineeringChemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The impact safety of explosive charges has been focused in these decades. The fragment impact is widely used to evaluate the response of explosive charges. In our work, the explosive detonation driving technique was used to generate a high velocity fragment with large mass. When the fragment masses are 10 g, 16 g, 25 g and 50 g, the highest velocity of fragments can reach 2400 m/s, 2100 m/s, 1900 m/s and 1400 m/s, respectively. The high velocity fragment with large mass was used to evaluate the safety of two kinds of CL-20 based explosive charges. The effects of the fragment mass and velocity were analyzed. Especially, the reaction extent was obtained based on visible phenomenon. The CL-20-based explosive charge containing Al had a higher safety level than that without Al. It was because Al had good ductility, and further improved the mechanical property of the material. Also, the numerical simulation was conducted to understand the reaction characteristics of the CL-20-based explosive charge. The results showed that as the fragment mass and velocity increased, the reaction became more violent.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.467

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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