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Record W2918077276 · doi:10.5539/ijsp.v8n2p172

Cavitation and Negative Pressure: A Flexible Water Model Molecular Dynamics Simulation

2019· article· en· W2918077276 on OpenAlexvenueno aff
Gang Liu, Qiang Fu, Junjun Kang

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCavitationMolecular dynamicsHydrogen bondWater modelBubbleNucleationIntermolecular forceChemistryThermodynamicsChemical physicsMaterials scienceMoleculeMechanicsComputational chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The critical negative pressure for cavitation in water has been theoretically predicted to be in the range of -100 to -200 MPa at room temperature, whereas values around -30 MPa have been obtained by many experiments. The discrepancy has yet to be resolved. Molecular dynamics (MD) is an effective method of observing bubble nucleation, however, most MD simulations use a rigid water model and do not take the effects of intermolecular vibrations into account. In this manuscript we perform MD simulations to study cavitation in water by using a TIP4P/2005f model under volumecontrolled stretching. It is found that the critical negative pressure of water was -168 MPa in the simulation and the critical negative pressure of water containing 50 oxygen molecules was -150 MPa. Hydrogen bonds played a major role in the cavitation process: the breaking of hydrogen bonds promoted bubble generation and growth. The O-H bond could release energy to increase the amount of potential energy in the system, so that cavitation was more likely to occur. When cavitation occurred, the O-H bond could absorb energy to reduce the amount of potential energy in the system, which will promote the growth of bubbles, and stabilise the cavitation bubbles.

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: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.314

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 head0.012
GPT teacher head0.262
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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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