MétaCan
Menu
← Back to cohort
Record W3121562071 · doi:10.1115/1.4049799

Model of a System-Wide Domestic Hot-Water Distribution System in a Multi-Resident High-Rise Building

2021· article· en· W3121562071 on OpenAlexafffund
Marin Vratonjic, Ali Rahmatmand, Feras Marish, Pierre E. Sullivan

Bibliographic record

VenueASME Journal of Engineering for Sustainable Buildings and Cities · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsEnvironmental scienceClosed loopEnergy consumptionWater consumptionComputer scienceProcess engineeringAutomotive engineeringCivil engineeringEnvironmental engineeringEngineeringControl engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract To reduce the environmental impact and cost, energy and water consumption of multi-resident buildings should be improved while ensuring resident comfort. Inefficient mixing of hot and cold-water streams and a non-optimal domestic hot-water (DHW) distribution system design can cause higher energy consumption, component failures, and dissatisfied residents. An OpenModelica (OM) system-wide model of a 14-story building consisting of a controlled-loop injection (CLI) device and a DHW distribution system is presented. The OM results are validated against field measurements at discreet locations within a single-zone closed-loop circuit to ensure the validity of time-varying temperature and flowrate. The study demonstrates that OM is a useful engineering tool to model single and multi-zone high-rise buildings that allows advanced analysis, including system-wide optimization, advanced on-demand controls, and energy and water-usage efficiencies.

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)0.000
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.006
GPT teacher head0.193
Teacher spread0.186 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueASME Journal of Engineering for Sustainable Buildings and Cities→Same topicBuilding Energy and Comfort Optimization→French-language works237,207→