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Record W4320494241 · doi:10.1080/02286203.2023.2176674

Numerical examination of water production by underground condensation system

2023· article· en· W4320494241 on OpenAlexaff
S. Alireza Zarabadi, Mostafa Mafi, P. Jalali Farahani, M. Soltani, Jatin Nathwani

Bibliographic record

VenueInternational Journal of Modelling and Simulation · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCondensationHumidityEnvironmental scienceWater vaporHydrology (agriculture)GroundwaterRelative humidityInletAir temperatureEnvironmental engineeringGeotechnical engineeringMeteorologyGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Water production by underground condensation is a low-capacity water-gathering technology for hot, humid climates. Hot, humid air is routed to subterranean pipes where it is progressively cooled and the vapor within the pipes appears as water droplets on the pipe surface. The goal of this paper is to quantify the amount of water extracted in the condensation system of humid and hot air. The water produced from humid air in buried pipes in the ground at a 0.5 m depth with different lengths is evaluated using MATLAB software, and optimal pipe length is established. Numerical findings show that water production is about 1 kilogram per day. It has been investigated how air temperature, pipe material, soil temperature, air humidity, and input speed influence underground condensation water production. It has been determined that 20 meters is the optimal length of the pipe. According to studies, Sandstone soil can produce 86%more water than other types of soil. It is also revealed that copper pipes could improve efficiency by 31%. The impact of effective factors on the efficiency of the condensation system, such as intake air temperature and humidity, inlet air velocity, and soil temperature, has also been assessed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.312
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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