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Record W1964448338 · doi:10.1080/15502280601149593

An Algorithm for Solving Reactive Advection-Dispersion Problems

2007· article· en· W1964448338 on OpenAlexaff
Jun Cao, Peter K. Kitanidis

Bibliographic record

VenueInternational Journal for Computational Methods in Engineering Science and Mechanics · 2007
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDiscretizationSolverAdvectionPartial differential equationNonlinear systemApplied mathematicsMathematicsDispersion (optics)Finite element methodMathematical optimizationMathematical analysisPhysics

Abstract

fetched live from OpenAlex

The physical problem of advection, dispersion, and reaction of a suite of chemicals is formulated mathematically as a set of coupled time-dependent nonlinear partial differential equations. Partially based on the Gear scheme for time discretization, the algorithm developed in this paper decouples equations into separate linearized problems. Then, using finite element approximation, these resulting linear advection-dispersion equations are further transformed, via a re-assembling of variational formulation, into an elliptic Stokes-like problem, which can be solved by an existing Stokes solver previously employed when solving the flow problem to produce a velocity background. The methodology is implemented in the simulation of transport and transformations of an electron donor, an electron acceptor, and active biomass. This application provides insights into biofilm evolution and pore clogging, and demonstrates mesh self-adjustment in the process of biofilm growth.

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)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.210
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.383
Teacher spread0.355 · 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
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

Citations4
Published2007
Admission routes1
Has abstractyes

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