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Record W4378471566 · doi:10.18488/13.v12i1.3379

Weather influence on the performance of solar collector

2023· article· en· W4378471566 on OpenAlexaff
Hakizabera Olivier, Uwayezu Emmanuel, Hirwa Gloria

Bibliographic record

VenueInternational Journal of Sustainable Energy and Environmental Research · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsSkyEnvironmental scienceAtmospheric sciencesMeteorologyRadiationPhysicsOptics

Abstract

fetched live from OpenAlex

A laboratory-scale evacuated tube solar collector (ETSCs) has been developed and tested for unloading conditions in Lanzhou, China. clear sky and cloudy conditions have been tested. Heat is generally considered instantaneous because it is a function of multiple instantaneous factors, like ambient temperature and solar radiation. The results show a clear sky day with a maximum and average value of ambient temperature, outlet, inlet, and solar radiation of 12 and -60C, 56 and 310C, 69 and 340C, 931 and 576W/m2, respectively, while a cloudy day was eight and -30C and 861.2 and 449W/m2 respectively. Clear sky day maximum and the average mass flow rate was 0.25 and 0.21 Kg/sec, while the cloudy flow rate was 0.27 and 0.03 Kg/sec, respectively. A clear sky has a heat loss of -0.307 and -0.05kW for a cloudy day. R square were 0.76 and 0.4 for clear sky and cloudy day, respectively. For the heat and radiation correlation relationship, a clear sky has a slope of 0.0029 and 0.0539 for a cloudy day. In other conditions, the solar radiation per unit area increases by 1 W/m2, and the daily heat is collected. Increasing A W/m2, the temperature difference between the average temperature of the hot water storage tank and the average temperature of the environment increases by one °C, and the daily heat collection decreases A W/m2. It concludes that a clear sky has much better efficiency than a cloudy day.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.312

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 integrity0.0000.000
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 head0.020
GPT teacher head0.271
Teacher spread0.251 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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