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Record W4386071150 · doi:10.11159/htff23.201

The Effect of Solar Sphere Thickness on the Fluid to Generate Power

2023· article· en· W4386071150 on OpenAlexvenueno aff
Hassan Abdulmouti, Fady Alnajjar

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Computer scienceMaterials sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

The high demand for renewable energy sources has given rise to many innovative systems that utilize natural resources to generate energy and supply electricity.The solar sphere system is an innovative system that collects the solar radiation incoming from the sun and concentrates it at a focal point on a multijunction device.The multijunction device consists of a high-efficiency solar cell that converts solar radiation into electricity.Many parameters in the solar sphere system affect the amount of power output and the associated efficiency leading to the improved overall performance of the system thus the aim of this paper is to examine and investigate the thickness of the sphere on the performance of the solar sphere filled with sunflower oil.The results obtained from the experiments showed that the thickness of the sphere significantly changes the value of power output and the associated efficiency of the system.From this result, it is found that decreasing the thickness of the sphere did increase the power output.Hence, the efficiency of the sphere increases when using a lower acrylic sphere thickness.The results can be interpreted as the thickness of the acrylic layer of the sphere getting lower, the more the sunlight is absorbed by the acrylic photons which subsequently leads to higher output power generation and higher system efficiency as compared to the conventional solar panel.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.452

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 head0.195
Teacher spread0.192 · 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 designBench or experimental
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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicSolar and Space Plasma DynamicsFrench-language works237,207