MétaCan
Menu
Back to cohort
Record W4285211988 · doi:10.5267/j.esm.2022.4.006

Solar dome integration as technical new in water desalination: case study Morocco region Rabat-Kenitra

2022· article· en· W4285211988 on OpenAlexvenueno aff
Saad Eddin Lachhab, A. Bliya, E. Al Ibrahmi, L. Dlim

Bibliographic record

VenueEngineering Solid Mechanics · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDome (geology)HumiditySaturation (graph theory)Environmental scienceDew pointHydrology (agriculture)EvaporationLow-temperature thermal desalinationThermalDesalinationEnvironmental engineeringMaterials scienceGeotechnical engineeringMeteorologyGeologyGeomorphologyGeographyChemistryMathematics

Abstract

fetched live from OpenAlex

This paper presents a study of the solar dome system that is considered as one of the most important economical solutions in the domain of drinking water production. For this, a mathematical model was built from equations describing the optical and thermal phenomenon involved in this process. The concentration of radiation and the heat flow were simulated in each 0.1m² of the dome using the metrological parameters of Morocco’s region Rabat-Salé-Kenitra. The results can follow the evolution of the temperature of glass, salt water, point dew temperature, saturation pressure and evaporation rate as well as humidity reached in August month for the study zone. For this, a numerical implementation on Matlab and Ansys are compared with measurements for the same parameters shown in the domain study of single slope. The comparison results are more significant by the rise of the ray concentration rate reached to 99% if the inclination angle of the heliostat is divided into three intervals. The saturation pressure increases as long as with the rate of evaporation and the humidity that produces ordinary daily fresh water.

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)0.001
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 score0.106
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.017
GPT teacher head0.240
Teacher spread0.224 · 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

Explore more

Same venueEngineering Solid MechanicsSame topicSolar Thermal and Photovoltaic SystemsFrench-language works237,207