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Record W2957402803 · doi:10.1139/tcsme-2019-0016

A computational study on airflow balancing in a horticultural drying chamber

2019· article· en· W2957402803 on OpenAlexaffvenue
Mahmoud Elhalwagy, Anthony G. Straatman, Bernard Goyette, Gideon Avigad

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsAgriculture and Agri-Food CanadaVineland Research and Innovation CentreWestern University
Fundersnot available
KeywordsAirflowComputational fluid dynamicsStack (abstract data type)Flow (mathematics)MechanicsEnvironmental scienceMechanical engineeringSimulationEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Simulations were conducted to study the airflow across skids of grapes in a horticultural grape drying chamber for the purpose of balancing the airflow to produce a uniform drying environment. The focus of the study was on the approach taken to provide balanced airflow using a computational fluid dynamics (CFD) tool combined with experimental data. The process was to first characterize the crate stacks by comparison of airflow simulations across a single crate stack to experimental data to establish resistance coefficients. The next step was to use these coefficients to simulate a row of stacked skids to establish corrections in terms of additional (variable) resistance that would result in balanced airflow. The corrected model was then used to simulate flow through the entire horticultural chamber to confirm that under the conditions of fan operation, the balance of airflow persists. The study shows that while the unmodified stacks had nearly 20% imbalance from the first to the last stack, the stack with resistance modifiers corrected this imbalance to within 5%, which is considered suitable for operation of the chamber.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.651

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

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