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Record W3088295787 · doi:10.1115/1.4048532

Design of a Multigenerational System With Absorption Cooling for a Residential Community: A Case Study

2020· article· en· W3088295787 on OpenAlexaffabout
Andre Bolt, Branson Chea, İbrahim Dinçer, Martin Agelin‐Chaab, Marc A. Rosen

Bibliographic record

VenueJournal of Energy Resources Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCoefficient of performanceAbsorption refrigeratorExergyEnvironmental scienceWater coolingHeating systemElectricityNuclear engineeringGeothermal gradientCooling loadEfficient energy useProcess engineeringMechanical engineeringHeat pumpEngineeringAir conditioningHeat exchanger

Abstract

fetched live from OpenAlex

Abstract In this paper, a multigeneration system is proposed, which utilizes geothermal energy and a lithium-bromide absorption cooling cycle. The proposed system is capable of providing electricity, heating, cooling, and domestic hot water to a small residential community in Vancouver, British Columbia, Canada. The performance of the system's heating and cooling capabilities were evaluated energetically and exergetically. A case study is presented by considering human occupancy loads and the impact of building material conditions on heating and cooling. System performance was investigated using parametric studies, where the operating conditions and ambient conditions were varied. Similar systems in the open literature were found to have an energetic and exergetic coefficient of performance (COP) of 0.8 and 0.3 for heating, while the proposed multigeneration system resulted in an energetic and exergetic coefficient of performance of 1.14 and 0.63 for heating, an increase of 30–52%. Additionally, the literature revealed that some systems resulted in an energy and exergy efficiency of 26.2% and 36.6%. The proposed multigeneration system achieved an energy and exergy efficiency of 31.86% and 63.33%, an improvement of 5.66–26.73%. The study was able to utilize the existing recommendations made by British Columbia to determine the necessary heating and cooling loads while also being able to successfully generate four useful outputs with a smaller footprint than those in the literature.

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 score0.483
Threshold uncertainty score0.394

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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