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Record W4380249067 · doi:10.14529/jsfi230105

Mathematical Modeling of Detonation Initiation in the Channel with a Profiled End Using Parallel Computations

2023· article· en· W4380249067 on OpenAlexaboutno aff
A. I. Lopato

Bibliographic record

VenueSupercomputing Frontiers and Innovations · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDomain decomposition methodsDetonationComputationGridShock waveComputer scienceChannel (broadcasting)Computer simulationMechanicsComputational scienceReflection (computer programming)AlgorithmParallel computingGeometryPhysicsMathematicsSimulationFinite element methodChemistryExplosive materialTelecommunicationsThermodynamics

Abstract

fetched live from OpenAlex

The paper is devoted to a numerical study of detonation initiation in the gas mixture in the channel with a profiled end. Initiation occurs as a result of the reflection of the shock wave of relatively low intensity from the end of the channel. Numerical calculations are carried out using unstructured triangular grids. The numerical algorithm is parallelized by the computational domain decomposition method using the METIS library. The exchange of grid function values between computing cores is performed using the MPI library. Numerical calculations are conducted on grids with different numbers of triangular cells. Detonation initiation patterns are obtained, which correspond to each other. The differences are mainly related to the degree of resolution of the elements of the gas mixture flow in the channel.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.223
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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