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Record W2593240595 · doi:10.3130/aije.82.175

A STUDY ON A THERMAL LOAD SIMULATION METHOD FOR SPACES CONDITIONED BY HEATING, COOLING AND FRESH AIR CONTROL SYSTEMS COMBINED WITH NATURAL VENTILATION

2017· article· en· W2593240595 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

VenueJournal of Environmental Engineering (Transactions of AIJ) · 2017
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsVentilation (architecture)Natural ventilationEnergy recovery ventilationAirflowEnvironmental scienceHeat recovery ventilationFresh airControl theory (sociology)MechanicsEngineeringComputer scienceMechanical engineeringControl (management)PhysicsHeat exchanger

Abstract

fetched live from OpenAlex

This paper presents a thermal load simulation method for spaces conditioned by heating, cooling and fresh air control systems combined with natural ventilation. Fresh air control systems enables free cooling, demand control ventilation and energy recovery ventilation. Natural ventilation may be carried out while spaces are conditioned by cooling equipment. For simulation of natural ventilation, many conditions which must be satisfied for natural ventilation can be considered and the under limit of space air temperature is supposed to be controlled by regulating ventilation opening area. On the other hand, natural ventilation rate is calculated under the simple conditions where airflow balance is not solved and the location of the neutral zone is assumed to be fixed. Space air temperature control by free cooling and heat recovery ventilation can be simulated. In demand control ventilation, ventilation rate is estimated in proportion of occupancy rate For heat balance simulation of multi zones where fresh air control systems combined with natural ventilation are used, a new method was developed in order to enable speedy simulation and to avoid complicated procedure. In the proposed method for iterative solution of heat balance and fresh air rate which means sum of natural ventilation rate and forced ventilation rate for free cooling, space air temperature control by regulating fresh air rate is replaced for control by a virtual heater and cooling and heating capacities are intentionally increased. This procedure suppresses iteration of calculating fresh air rate and the solution such as heating rate, cooling rate and fresh air rate can be obtained by correcting the calculated results. The proposed simulation method was applied to the building simulation engine in BEST which is a whole building energy and thermal load simulation program and the simulations for a typical office building located in Tokyo were performed. The basic effects of fresh air control techniques and natural ventilation on space thermal environment and thermal load were presented. The interaction effects of energy saving strategies such as high performance building facades, natural ventilation and fresh air control techniques were evaluated through simulations in two buildings. One is a low-performance building and the other is a high-performance building. The energy saving rate for a low-performance building is defined as the thermal load reduction achieved by adding an energy saving strategy, and that for a high-performance building is defined as the thermal load increase resulted by removing the strategy employed in the high-performance building. Although some strategies provide lower energy savings for the high-performance building than for the low-performance building, a few strategies are more effective for the high-performance building.

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)0.000
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 score0.215
Threshold uncertainty score0.643

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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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