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Record W2805847748

Análise termodinâmica de um refrigerador funcionando com diferentes fluidos refrigerantes sintéticos

2018· dissertation· pt· W2805847748 on OpenAlexaboutno aff
Silas Ferreira Tavares Martins Miranda

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2018
Typedissertation
Languagept
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophyChemistry
DOInot available

Abstract

fetched live from OpenAlex

O presente trabalho tem como objetivo avaliar o desempenho de um sistema de refrigeração por compressão de vapor utilizando diferentes fluidos refrigerantes sintéticos. Depois do sucesso na aplicação do Protocolo de Montreal, que culminou na eliminação dos CFCs (clorofluorcarbonetos) que tinha alto potencial de destruição da camada de ozônio, nos últimos anos foi constatado que os HCFCs que surgiram com excelente alternativa aos CFCs também prejudicam o meio ambiente, sua liberação para atmosfera contribui para o efeito estufa causando o aquecimento global. Por isso o governo brasileiro elaborou um Programa de Eliminação dos HCFCs (hidroclorofluorcarbonos), este programa está voltado principalmente para eliminação do consumo do HCFC-22 (R22), que é um dos fluidos refrigerantes mais utilizado nos sistemas de refrigeração e ar condicionado. Neste contexto, o presente trabalho pretende fazer estudo termodinâmico comparativo entre dois fluidos refrigerantes sintéticos o R-22 e o R437a. O refrigerador funcionando com R437 foi submetido a testes abaixamento de temperatura conforme norma ABNT para diferentes cargas térmicas no evaporador. Os resultados obtidos foram comparados com o refrigerador funcionando com R22 para as mesmas cargas térmicas e revelam que a performance do refrigerador com o R22 é superior ao R437a.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.243
Teacher spread0.226 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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