MétaCan
Menu
Back to cohort
Record W2472767423 · doi:10.13182/nse07-a2691

An Improved Algebraic Collapsing Acceleration with General Boundary Conditions for the Characteristics Method

2007· article· en· W2472767423 on OpenAlexaff
R. Le Tellier, Alain Hébert

Bibliographic record

VenueNuclear Science and Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAccelerationAlgebraic numberBoundary (topology)Boundary value problemNeutron transportMathematicsPhysicsApplied mathematicsComputer scienceMathematical analysisClassical mechanicsNuclear physicsNeutron

Abstract

fetched live from OpenAlex

A detailed derivation of the algebraic collapsing acceleration (ACA), a synthetic acceleration of the characteristics method, is presented. An improvement of the synthetic hypothesis is proposed, and the corrective system is derived for general boundary conditions. Both Fourier and direct spectral analyses of the accelerated iterations for a one-dimensional slab geometry are given. The solving strategy for the corrective system along with implementation details about the method of characteristics is discussed. Numerical results for a one-group, two-dimensional benchmark are provided to illustrate the basic synthetic hypothesis and the enhancement of its robustness with the proposed two-step collapsing hypothesis. The practical performance of ACA is illustrated on a pressurized water reactor–type assembly in the context of multigroup eigenvalue calculations.

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)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.307
Teacher spread0.291 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueNuclear Science and EngineeringSame topicAdvanced Numerical Methods in Computational MathematicsFrench-language works237,207