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Record W4401277213 · doi:10.1080/01430750.2024.2384944

Performance evaluation of a solar thermoelectric generator with optical concentration under real operating conditions

2024· article· en· W4401277213 on OpenAlexaff
Alkhadher Khalil, Bouchaib Zohal, Houssam Amiry, Ahmed Elhassnaoui, Said Yadir, Abdellatif Obbadi, Youssef Errami, Smail Sahnoun

Bibliographic record

VenueInternational Journal of Ambient Energy · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsThermoelectric generatorThermoelectric effectVoltageMaterials scienceNuclear engineeringSolar simulatorMaximum power principleGenerator (circuit theory)Thermoelectric coolingSolar energyElectric powerElectricity generationRadiationOptoelectronicsEnvironmental scienceElectricityPower (physics)Electrical engineeringOpticsSolar cellPhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Every day, the sun emits a large amount of light energy. This energy is converted into heat and then electricity using solar thermoelectric generators (STEG). In this article, we have simulated a solar thermoelectric generator (TEC-12706) with real conditions in our laboratory. We have calculated the electric current, the voltage and the output power. To validate the simulation results, we carried out experimental studies to assess the effects of solar radiation concentration (optics) on the performance of the STEG. We measured the temperatures of the hot and cold sides, the electric current and the output voltage using an appropriate electronic circuit. We found that for optical concentration ratios 40, 30 and 20, the maximum temperatures on the hot side are 66.5, 54 and 44.75°C, respectively. Under these conditions the maximum powers obtained are 0.04147, 0.01884 and 0.00832 W with external load resistances of 2.42342, 2.14555 and 2.16696 Ω . Simulation of the STEG considered with the values of those obtained from experience gave the following maximum output powers 0.130088, 0.061466 and 0.031803 W for the same external load resistances. The differences between the experimental and simulation results may be due to the unknown properties of the materials used in the module.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.493

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 communication0.0000.001
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 head0.016
GPT teacher head0.291
Teacher spread0.275 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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