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Record W3164850868 · doi:10.53063/synsint.2021.128

Numerical investigation of solar collectors as a potential source for sintering of ZrB2

2021· article· en· W3164850868 on OpenAlexaffvenue
Mohammad Vajdi, Saeed Mohammad Bagheri, Farhad Sadegh Moghanlou, Amin Shams Khorrami

Bibliographic record

VenueSynthesis and Sintering · 2021
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsYork University
Fundersnot available
KeywordsSinteringMaterials scienceConcentratorSolar energyWork (physics)CeramicMelting pointThermal conductionComposite materialNuclear engineeringMechanical engineeringOpticsElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Sintering of ceramics is an energy-consuming process that needs high temperatures, therefore, in the present work; solar energy is used to produce high temperatures for the sintering aim of different materials. Solar energy concentrators increase the intensity of incident energy to the receiver provides high temperatures. Ultrahigh-temperature ceramics (UHTCs) due to their high melting point can also be a good alternative for receiver materials. In the present work, ZrB2 is introduced as an alternative material for solar receivers which can withstand high temperatures of sintering. The governing equations, including heat radiation and conduction ones are solved numerically using the finite element method. Transient heat transfer in the concentrator-collector system is investigated to check the feasibility of high temperatures needs for sintering at the receiver. The highest temperature of 1680 °C was achieved after 15 minutes at the focal point of the concentrator when the solar heat flux of 6.86 w/mm2 used for the location of the city of Ardabil in Iran. The obtained temperature can be used to sintering of some groups of materials.

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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