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Record W4292409181 · doi:10.3390/su141610231

Experimental Evaluation of the Thermoelectrical Performance of Photovoltaic-Thermal Systems with a Water-Cooled Heat Sink

2022· article· en· W4292409181 on OpenAlexaff
Husam Abdulrasool Hasan, Jenan S. Sherza, Jasim M. Mahdi, Hussein Togun, Azher M. Abed, Raed Khalid Ibrahim, Wahiba Yaïci

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsHeat sinkPhotovoltaic systemEnvironmental scienceWater coolingThermalDaytimeMaterials scienceWater flowIrradiationWind speedSink (geography)Solar cellAtmospheric sciencesMass flow rateNuclear engineeringMeteorologyElectrical engineeringOptoelectronicsEnvironmental engineeringMechanicsThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

A design for a photovoltaic-thermal (PVT) assembly with a water-cooled heat sink was planned, constructed, and experimentally evaluated in the climatic conditions of the southern region of Iraq during the summertime. The water-cooled heat sink was applied to thermally manage the PV cells, in order to boost the electrical output of the PVT system. A set of temperature sensors was installed to monitor the water intake, exit, and cell temperatures. The climatic parameters including the wind velocity, atmospheric pressure, and solar irradiation were also monitored on a daily basis. The effects of solar irradiation on the average PV temperature, electrical power, and overall electrical-thermal efficiency were investigated. The findings indicate that the PV temperature would increase from 65 to 73 °C, when the solar irradiation increases from 500 to 960 W/m2, with and without cooling, respectively. Meanwhile, the output power increased from 35 to 55 W when the solar irradiation increased from 500 to 960 W/m2 during the daytime. The impact of varying the mass flow rate of cooling water in the range of 4 to 16 L/min was also examined, and it was found that the cell temperature declines as the water flow increases in intensity throughout the daytime. The maximum cell temperature recorded for PV modules without cooling was in the middle of the day. The lowest cell temperature was also recorded in the middle of the day for a PVT solar system with 16 L/min of cooling 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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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 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

Citations41
Published2022
Admission routes1
Has abstractyes

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