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Record W1991289549 · doi:10.13031/2013.42328

Modeling Ventilation Time in Forage Tower Silos

2012· article· en· W1991289549 on OpenAlexaff
Ali Bahloul, Joseph Páez Chávez, Marcelo Reggio, Brigitte Roberge, Nicole Goyer

Bibliographic record

VenueJournal of Agricultural Safety and Health · 2012
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsSiloInformation siloVentilation (architecture)TowerEngineeringEnvironmental scienceComputer simulationAirflowMarine engineeringStructural engineeringNuclear engineeringWaste managementSimulationMechanical engineering

Abstract

fetched live from OpenAlex

The fermentation process in forage tower silos produces a significant amount of gases, which can easily reach dangerous concentrations and constitute a hazard for silo operators. To maintain a non-toxic environment, silo ventilation is applied. Literature reviews show that the fermentation gases reach high concentrations in the headspace of a silo and flow down the silo from the chute door to the feed room. In this article, a detailed parametric analysis of forced ventilation scenarios built via numerical simulation was performed. The methodology is based on the solution of the Navier-Stokes equations, coupled with transport equations for the gas concentrations. Validation was achieved by comparing the numerical results with experimental data obtained from a scale model silo using the tracer gas testing method for O2 and CO2 concentrations. Good agreement was found between the experimental and numerical results. The set of numerical simulations made it possible to establish a simple analytical model to predict the minimum time required to ventilate a silo to make it safe to enter. This ventilation time takes into account the headspace above the forage, the airflow rate, and the initial concentrations of O2 and CO2. The final analytical model was validated with available results from the literature.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.182

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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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 designObservational
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

Citations3
Published2012
Admission routes1
Has abstractyes

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