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Record W2951090412 · doi:10.18483/ijsci.2019

Molecular Dynamics approach for Fracture Simulation along a Weakly Bonded Interface

2019· article· en· W2951090412 on OpenAlexaff
SeyedBijan Mahbaz, Milad Mosharafi, Maurice B. Dusseault

Bibliographic record

VenueInternational Journal of Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMolecular dynamicsMaterials scienceFracture (geology)Ultimate tensile strengthFinite element methodInterface (matter)Stress (linguistics)Oil shaleComposite materialPorosityScale (ratio)Hydraulic fracturingMechanicsStructural engineeringGeotechnical engineeringGeologyPhysicsChemistryComputational chemistryEngineering

Abstract

fetched live from OpenAlex

Opening mechanisms for fractures (joints) characterized by weak bonding (nano-micro scale cracks) in low-porosity rock play a key role in shale oil and shale gas development through staged hydraulic fracturing. We explore the cohesive interface strength of two weakly bonded slabs of Polymethylmethacrylate (PMMA) with nano-scale Molecular Dynamics (MD) methods, calibrated to experimental data and simulated by the Finite Element Method (FEM). The proper stress/strain state at the weakly bonded interface is determined because it is required for MD simulation. Then, we develop a representative PMMA structure as an input for LAMMPS© software to simulate a tensile strength test. Results including per-atom stress values and pressures in different components are extracted, and we note that the fracture location and its behavior deduced from MD simulations follows experimental results. The total force for this simulation is close to 80 × 106 N, and we can use this parameter to reflect the fracture behavior of the weakly bonded PMMA slabs with an acceptable accuracy, considering the levels of uncertainty.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score0.256

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 head0.255
Teacher spread0.248 · 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 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
Published2019
Admission routes1
Has abstractyes

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