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Record W7046467039

Dynamic Failure of Advanced Ceramics using Molecular Dynamics Simulations

2020· dissertation· en· W7046467039 on OpenAlexfundno aff

Bibliographic record

VenueUPCommons institutional repository (Universitat Politècnica de Catalunya) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersArmy Research LaboratoryUniversity of Alberta
KeywordsMolecular dynamicsCeramicBallistic impactShock (circulatory)Boundary value problemMATLABBallisticsPeriodic boundary conditions
DOInot available

Abstract

fetched live from OpenAlex

This project has been mainly focused on the validation of the model of Al2O3 that will be used to study the mechanical behavior of alumina under shock loading conditions through the application of Molecular Dynamics simulations using LAMMPS software. The simulations were used to estimate the parameters that are commonly used to validate the potential fields and the Molecular Dynamics models before running complex simulations to study the behavior of materials (e.g., elastic constants, cohesive energy, lattice constants, and the radial distribution function). In addition, a methodology for shock loading simulations with alumina was proposed. In this methodology, the equilibration part, the boundary conditions, and the shock loading conditions were described.\n\t\t\t\t The project also contains a study of some B4C specimens that were analyzed by image processing algorithms using MATLAB to understand the usual behavior of ceramics under ballistic impacts and provides a basis on the topic. Altogether, this study presents a thorough understanding of the dynamic failure of Al2O3 under ballistic impacts with regard to mechanical properties and failure mechanisms, and provides insights for simulating with this type of material and with advanced ceramics in general.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
Teacher spread0.250 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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