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Record W6962667548 · doi:10.18462/iir.pcm.2018.0007

Numerical investigation of a novel phase-change renewable energy system for underground mine heating and cooling.

2018· dataset· en· W6962667548 on OpenAlexaboutno aff

Bibliographic record

VenueInstitut International du Froid · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyCurrent (fluid)EvaporationHeat transferInletWater heatingFlow (mathematics)Evaporative coolerMixing (physics)

Abstract

fetched live from OpenAlex

This study analyzes the performance of spray freezing, a novel renewable technology, to supplement the heating and cooling load of underground mines. This opportunistic technology provides a cost-effective way to utilize the sub-arctic conditions of Northern Canada for meeting the heating needs. The current study employs a Multiphysics, multi-scale approach to model the phenomena of spray freezing. The discrete phase computational fluid dynamics model developed, can reproduce both the local effects of droplet freezing and water evaporation along with the global energy balance of the whole system with the aim of maximizing the heat transfer to the cold air. The model is validated with published experimental data and further extended to evaluate the effect of different operational (air flow rate, inlet air temperature) of the system. Preliminary results indicate that air flow rate and temperature of water impacts the mixing which consequently influences the ice formation along with the outlet temperature of the air.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.065
GPT teacher head0.307
Teacher spread0.242 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations5
Published2018
Admission routes1
Has abstractyes

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