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A Multiple Load Aggregation Algorithm for Annual Hourly Simulations of GCHP Systems

2004· article· en· W2064497240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHVAC&R Research · 2004
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsPolytechnique Montréal
FundersInstitut National des Sciences Appliquées de Lyon
KeywordsHeat pumpComputer scienceEnvironmental scienceThermalSimulationCooling loadAggregate (composite)AlgorithmMeteorologyMechanical engineeringMaterials scienceEngineeringAir conditioningPhysicsHeat exchanger

Abstract

fetched live from OpenAlex

This article presents a technique to aggregate heating/cooling loads when using the cylindrical heat source method (CHS) to perform annual hourly energy simulations of ground-coupled heat pump (GCHP) systems. The technique, referred to as “multiple load aggregation algorithm” (or MLAA), uses two major thermal history periods, referred to as “past” and “immediate.” In addition, the MLAA accounts for thermal interference among boreholes by numerically solving the two-dimensional temperature field in the borefield. Results of a comparison between the MLAA and the duct storage (DST) model are presented. Several cases are examined with two different borefields and several load profiles. Results obtained for one- and ten-year simulations show that the MLAA is in very good agreement with the DST model. In the worst case, the maximum difference in fluid temperature is of the order of 2 K (3.6°F). This level of precision is more than adequate to perform accurate hourly simulations of GCHP systems.

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.

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)0.001
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 score0.236
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.350
Teacher spread0.290 · 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