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Record W4205371229 · doi:10.3390/en15010369

Improving Energy Efficiency by Utilizing Wetted Cellulose Pads in Passive Cooling Systems

2022· article· en· W4205371229 on OpenAlexaff
Ebrahim Morady, M. Soltani, Farshad Moradi Kashkooli, Masoud Ziabasharhagh, Armughan Al‐Haq, Jatin Nathwani

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAirflowCelluloseMaterials scienceHumidityEnvironmental scienceAir velocityPorosityComposite materialMeteorologyMechanicsMechanical engineeringChemical engineeringEngineering

Abstract

fetched live from OpenAlex

The effectiveness of using wetted cellulose pads on improving the performance of two conventional passive cooling systems has been evaluated. First, an experimental design was developed to determine the impact of using a wetted cellulose pad on the temperature and velocity of the airflow. A cellulose pad (7090 model) with a cross-sectional area of 0.5 × 0.5 m2 and three different thicknesses of 10, 15, and 30 cm were selected and tested. The results indicated that using wetted cellulose pads with thicknesses ranging from 10–30 cm decreased the outlet airflow temperature from 11.3 to 13.7 °C on average. For free airflow at velocity 3.5 m/s, the outlet airflow velocity from the wetted cellulose pad decreased to 0.9, 0.7 and 0.6 m/s, respectively, for cellulose pads with thicknesses of 10, 15, and 30 cm. By applying experimental results on a psychrometric chart, the humidity ratio of outlet airflow was obtained between 40–70%. The study established airflow velocity as the critical parameter in passive cooling systems. With the novel concept of combining wetted cellulose pads for passive cooling systems (i.e., wind catchers and induced ventilation), there is good potential to reduce the energy requirements for thermal comfort in buildings in regions with a hot and arid climate.

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 score0.038
Threshold uncertainty score0.708

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 head0.004
GPT teacher head0.169
Teacher spread0.164 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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