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Modeling of Filtration for a Metal Foam Diesel Particulate Filter

2006· article· en· W2019853020 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.

Bibliographic record

VenueKey engineering materials · 2006
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsNickel Institute
FundersMinistry of Environment
KeywordsPressure dropMaterials scienceDiesel particulate filterFiltration (mathematics)PorositySootComposite materialParticulatesAir permeability specific surfacePermeability (electromagnetism)Drop (telecommunication)Diesel fuelFilter (signal processing)Waste managementCombustionChemistryMechanicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The filtration of soot in the metal foam DPF has been studied. INCOFOAM®HighTemp is selected for DPF material for its large specific area for filtration. The structural properties of the foam such as pore diameter, strut diameter and porosity are determined from the pictures by 3D Xray scope and SEM. The permeability and Forchheimer coefficient obtained by clean filter experiments are correlated with the structural properties. During the filtration process, the soot particles deposited inside the filter affect the local strut diameter, porosity and permeability, which determine the filtration efficiency and pressure drop. By the analytic model developed, the actual pressure drop in the engine operation can be predicted.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.661

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 head0.012
GPT teacher head0.198
Teacher spread0.186 · 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