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Record W4367317664 · doi:10.18280/mmep.100223

Performance of Photovoltaic Thermal Technology Using V-Absorber with Exergy, Improvement Potential and Sustainability Index Analysis

2023· article· en· W4367317664 on OpenAlexvenueno aff
Muhammad Zohri, Yuliadi Yuliadi, Muḥammad Ghazālī, Idham Idham

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemExergySustainabilityIndex (typography)ThermalEnvironmental scienceMaterials scienceEnvironmental economicsEngineering physicsComputer scienceProcess engineeringEngineeringEconomicsThermodynamicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This research aims to test the PVT system's performance using V-Absorber model.The PVT technology using V-Absorber is conducted using a mathematical approach.This mathematical or theoretical approach aims to analyse suitable variables in predicting the system's optimal performance.The analysis of this system is the analysis of exergy and the most important is the analysis of the sustainability index and improvement potential.The variable values of sunlight intensity are 385 W/m 2 , 575 W/m 2 and 815 W/m 2 .At the same time, the flow air is from 0.01 kg/s to 0.10 kg/s.The analysis results show that the position of the flow air of 0.06 kg/s is the maximum value for exergy using V-Absorber.The optimum sustainability index's value was 2.012 at flow air of 0.06 kg/s with sunlight intensity of 815 W/m 2 .For the improvement potential, the optimum yield was 329.24 Watt for flow air of 0.10 kg/s.The results of the sustainability index and improvement potential analysis show that the effect of the flow air and sunlight intensity was very significant.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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