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Record W4220936697 · doi:10.1002/mma.8190

On the convergence of solving a nonlinear Volterra‐type integral equation for surface divergence based on surface thermal information

2022· article· en· W4220936697 on OpenAlexaff
Tianyi Li, Andrew J. Szeri, Lian Shen

Bibliographic record

VenueMathematical Methods in the Applied Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematicsIntegral equationDivergence (linguistics)Mathematical analysisVolterra integral equationNonlinear systemUniquenessConvergence (economics)Integro-differential equationSummation equationPartial differential equationSurface (topology)Heat equationHeat fluxFirst-order partial differential equationHeat transferGeometryPhysics

Abstract

fetched live from OpenAlex

We analyze a nonlinear integral equation for calculating free‐surface divergence that was proposed by Szeri (2017, https://doi.org/10.1002/2016JC012312 ). When given the temperature and heat flux at a free surface, the surface divergence can be calculated through a nonlinear singular Volterra‐type integral equation. The two given functions in the integral equation satisfy auxiliary conditions through a higher dimensional partial differential equation. We prove the existence and uniqueness of the solution of the integral equation. We also prove the local linear convergence of the corresponding Picard iteration method for solving the integral equation when the surface heat flux is a real‐analytic function of time. The rate of convergence is derived explicitly, which depends on the function of surface heat flux. Numerical examples are provided to validate the convergence performance.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.281
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.334
Teacher spread0.270 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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