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Record W2315836416 · doi:10.5383/ijtee.02.01.004

Indoor Energy Analysis of Food Distribution Warehouse

2010· article· en· W2315836416 on OpenAlexvenueno aff
Rafat Al‐Waked, Nathan Groenhout, Lester Partridge

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsnot available
FundersPrince Mohammad Bin Fahd University
KeywordsWarehouseDistribution (mathematics)Environmental scienceComputer scienceBusinessMathematicsMarketing

Abstract

fetched live from OpenAlex

The current investigation involves carrying out dynamic thermal simulations and computational fluid dynamic analysis (CFD) analysis. The purpose of this analysis is to assist in designing a ventilation system to achieve an acceptable internal thermal environment of less than 28 C dry bulb temperatures within the occupied space. The thermal performance of the facility over a period of time has been simulated taking into account the effect of thermal mass within the space which affects the average internal space temperatures. Results from the dynamic thermal simulation analysis were then used to provide the boundary conditions for the CFD analysis. The investigated warehouse has been simulated using four different roofs based mechanical ventilation strategies. The amount of goods stored in the warehouse has been found to play a significant role in keeping the warehouse temperature within the acceptable range. An almost empty warehouse tends to vary with the outside ambient temperature. However, a warehouse full of goods makes use of thermal mass to provide a passive cooling strategy. It is recommended that mechanical ventilation of 350 m 3 /s of air to be adopted using the smoking fans already exist in the warehouse. These fans should operate when the ambient temperature falls below 27C at any time of the day if the temperature requirements are satisfied during the summer period.

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 score0.210
Threshold uncertainty score0.260

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 head0.003
GPT teacher head0.163
Teacher spread0.161 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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