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Record W2106507373 · doi:10.13182/nse02-a2265

Adjoint and Generalized Adjoint Flux Calculations Using the Collision Probability Technique

2002· article· en· W2106507373 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNuclear Science and Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdjoint equationFlux (metallurgy)ComputationNeutron fluxSimple (philosophy)Applied mathematicsCollisionMathematicsIterative methodPhysicsMathematical optimizationMathematical analysisComputer scienceAlgorithmNeutronPartial differential equationQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

Computation of adjoint and generalized adjoint fluxes may present some difficulties, especially when relying on the collision probability technique in transport theory. This paper proposes a simple method to compute those adjoint flux and generalized adjoint fluxes associated with homogenized and condensed cross sections. By defining a pseudo adjoint flux, one can apply an algorithm, similar to that required for the evaluation of the direct neutron flux, to adjoint flux calculations. Because of the presence of the scattering source, a multigroup iterative procedure is used in DRAGON for the direct flux solution. We show that this procedure can be easily modified in such a way that the performance of the solution algorithm is preserved for the adjoint problem. Finally, a generic adjoint algorithm is presented to deal with generalized adjoint fluxes’ computation.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.510

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 head0.022
GPT teacher head0.198
Teacher spread0.176 · 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