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Record W4381619340 · doi:10.11159/ffhmt23.123

Improvement in Energy Performance of a HVAC System Working with Nanofluid

2023· article· en· W4381619340 on OpenAlexvenueno aff
Marco Milanese, Marco Potenza, Claudio Grisoni, Arturo de Risi

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidHVACEnergy (signal processing)Computer scienceEnergy performanceMaterials scienceMechanical engineeringAir conditioningEngineeringPhysicsNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

In the present work, a HVAC system in a residential building, operating with water firstly, followed by a nanofluid named Maxwell, has been experimentally monitored to evaluate the improvement in energy performance.Particularly, a robust measurement and verification equipment has been installed on the chillers and the pumps firstly, next a baseline data for a 30-day period has been acquired, by operating the HVAC system with water, then the same data have been measured for a 30-day nanofluid operating period, and finally the baseline data were compared to the nanofluid data.All collected data have been normalized according to the ambient temperature conditions, since this parameter plays a significant role in chiller performance and energy consumption.Furthermore, in order to ensure an objective, transparent and conservative evaluation of energy-conservation measures, the International Performance Measurement and Verification Protocol (IPMVP) was applied.The present experimental demonstration has evaluated chiller energy consumption and pump energy consumption as well as coefficient of performance (COP) resulting in a mean decrease in chiller energy use of about 16.85%.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.208
Teacher spread0.184 · 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

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicSolar Thermal and Photovoltaic SystemsFrench-language works237,207