Aparato Experimental Para Avaliação Termo-Energética de Refrigerador Doméstico
Bibliographic record
Abstract
Os esforcos de pesquisa e desenvolvimento na area de Refrigeracao e Climatizacao aplicados ao uso de fluidos refrigerantes naturais nao esta associada somente a necessidade de preservacao do meio-ambiente em si, mas tambem apresenta grande importância na necessidade latente do aumento da eficiencia energetica dos equipamentos. Tal caracteristica e observada na Decisao XIX/6 do Protocolo de Montreal. Neste sentido, identificar fluidos refrigerantes alternativos de longo prazo que satisfacam todos os requisitos com relacao a performance dos sistemas e uma importante area de pesquisa. O objetivo do presente trabalho e analisar as normalizacoes existentes no Brasil, referentes a ensaios em refrigeradores domesticos, e desenvolver a montagem de um aparato experimental para estudo termo-energetico/performance de um refrigerador. Foi desenvolvido um aparato experimental consistindo de um refrigerador domestico de 219 L instrumentado com sensores de pressao, temperatura e corrente eletrica. Os sensores de temperatura e corrente eletrica sao conectados a um sistema de aquisicao de dados para o registro dos dados durante todo o tempo de ensaio. As indicacoes de pressao foram obtidas visualmente atraves de manifold digital. O aparato e adequado para a avaliacao termodinâmica e energetica de um equipamento de refrigeracao operando com fluidos refrigerantes sinteticos ou naturais (hidrocarbonetos), dentro da estrategia de drop-in, sem alteracao dos componentes mecânicos do sistema.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".