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Record W4212865853 · doi:10.1016/j.cpc.2022.108318

LAMMPS lb/fluid fix version 2: Improved hydrodynamic forces implemented into LAMMPS through a lattice-Boltzmann fluid

2022· article· en· W4212865853 on OpenAlexafffund
Colin Denniston, Navid Afrasiabian, Maxximuss Cole-André, Frances Mackay, Santtu T. T. Ollila, Tyson Whitehead

Bibliographic record

VenueComputer Physics Communications · 2022
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsComputer scienceLattice Boltzmann methodsInterpolation (computer graphics)Fluid simulationComputational sciencePython (programming language)Fluid dynamicsAlgorithmProgramming languageComputer graphics (images)PhysicsMechanics

Abstract

fetched live from OpenAlex

The first version of this code (Mackay et al., 2013) [10] implemented long-range hydrodynamic interactions into the open-source molecular dynamics package LAMMPS. This was done through the creation of a fix, lb/fluid which was subsequently included as a user-package in the main LAMMPS distribution. Here we substantially update this package by making improvements to its accuracy, adding significant new features, and by simplifying the use of the package. A new two-pass interpolation and spreading scheme is introduced which results in the improved accuracy and numerical stability. New features include new output options, several added computes, and mesh geometry option suitable for micro- and nano-fluidic device simulations. The original package could require fairly careful calibration to obtain accurate thermostating and accurate reproduction of properties related to the hydrodynamic size of objects such as colloids. This process has now been largely automated so that the default settings should suffice for most applications. Program title: fix lb/fluid CPC Library link to program files: https://doi.org/10.17632/2289cnrdtz.1 Licensing provisions: GPLv3 Programming language: C++ Journal reference of previous version: Comput. Phys. Commun. 184 (2013) 2021–2031. Does the new version supersede the previous version?: Yes Reasons for the new version: The new version improves accuracy, adds new features, and simplifies the use of the package. Summary of revisions: A new two-pass interpolation and spreading scheme is introduced to relate properties on the fluid mesh to off-lattice particle properties. New features include output options, several added computes, and mesh geometry suitable for micro- and nano-fluidic device simulations. Calibration processes have been largely automated so that the default settings should suffice for most applications. Nature of problem: The inclusion of long-range hydrodynamic effects into molecular dynamics simulations requires the presence of an explicit solvent. Prior to the implementation of this fix, the only option for incorporating such a solvent into a LAMMPS [1] simulation is the explicit inclusion of each of the individual solvent molecules. This is obviously quite computationally intensive, and for large system sizes can quickly become impractical. Solution method: As an alternative, we have implemented a coarse-grained model for the fluid, simplifying the problem, while retaining the solvent degrees of freedom. We use a thermal lattice-Boltzmann model for the fluid, which is coupled to the molecular dynamics particles at each fluid time step. S. Plimpton, J. Comput. Phys. 117 (1995) 1–19.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0040.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0450.022

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 head0.274
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 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
GenreMethods

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

Citations12
Published2022
Admission routes2
Has abstractyes

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