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Simulation of Dynamic Thermal Behaviour for Housing in Warm Climate: The Case of Thermal Mass in Lightweight Envelopes

2018· article· en· W2794336292 on OpenAlexvenueno aff
Chiara Tonelli, Ilaria Montella

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2018
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTRNSYSBuilding envelopeNatural ventilationEnvironmental scienceHVACThermal massThermalThermal comfortEnvelope (radar)MicroclimateVentilation (architecture)MeteorologySimulationComputer scienceArchitectural engineeringEngineeringAerospace engineeringMechanical engineeringAir conditioningPhysicsGeography

Abstract

fetched live from OpenAlex

Comparison between simulation results and measured performances is usually an open scientific problem, crucial to achieving the goal of NZEB performance. This paper addresses this issue in relation to residential buildings, using as a case study "RhOME for denCity", the housing prototype developed by Roma TRE University and winner of Solar Decathlon Europe 2014. In a Mediterranean climate, the use of the mass combined with natural cross ventilation to control the indoor microclimate can be very effective in reducing HVAC use. Therefore, a "massive layer" was introduced in the inner surface of the envelope to not only contribute to the envelope transmittance value and the shifting phase of the thermal waves, but also as a thermal shock absorber to adjust the internal temperature, in both summer and winter. This experimental envelope was tested over two weeks during the competition in Versailles. Although prototype thermal behaviour was monitored only during the competition, and not over an extended period, initial results provide information on how to size the thermal mass contribution for indoor comfort. In-depth simulation through TRNSYS was run prior to the construction phase. This paper presents the comparison between monitored performance and simulations in order to measure the amount of mass needed to obtain a numerical improvement in indoor comfort performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.247
Teacher spread0.239 · 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 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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