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Record W3155103438 · doi:10.22215/etd/2014-10292

Evaluation of a Dual Tank Indirect Solar-Assisted Heat Pump System for a High Performance House

2014· dissertation· en· W3155103438 on OpenAlexaffabout
Jenny Chu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsHeat pumpWork (physics)Water heatingCoefficient of performanceThermal energy storageHybrid heatHeating systemEnvironmental scienceEngineeringNuclear engineeringCooling loadThermalProcess engineeringMechanical engineeringWaste managementMeteorologyThermodynamicsAir conditioningHeat exchanger

Abstract

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This work focused on the design and evaluation of an integrated mechanical system, which incorporated a dual tank indirect solar-assisted heat pump, that offsets spaceheating, cooling, and domestic hot water loads for a high performance house. A model of the system was developed to investigate the effects of various parameters on the performance of the system. These parameters included the tank configurations, the solar collector size and orientation, and heat pump size and controls. In addition, an experimental study was conducted to investigate the relationship between the heat pump load side flow rate, the heat pump performance, and the thermal stratification in the storage tank. The experimental results indicated that the coefficient of performance of the heat pump reduced with lower flow rates. However, lower flow rates could result in higher temperature rises across the condenser and greater levels of the stratification which could improve the overall performance of the system by reducing the auxiliary energy consumption. Results from the modelling and experimental work were compared and the experimental results were used to improve the heat pump performance map that was used in the simulations. The simulation results showed that the system could achieve a free energy fraction of 0.506 (neglecting energy draws from circulation pumps and fans) for space-heating, cooling, and domestic hot water. This result suggests that the system does have the potential of reducing energy consumption in the residential sector in Canada. Thermal loss coefficient dependency on the collector temperatures (kJ/hm 2 K 2 ) V Volumetric flow rate (L/min) VGR Gravimetric flow rate (L/min) x Distance between nodes in the storage tank (m) xix Authors SAHP Set-up Performance Bertram, Prisch, and Tepe [27] Configurations: 3 systems involving flat plate collectors, borehole heat exchanger (BHE) and a heat pump Heat Pump: 7.9 kW Collector Type: Flat plate Energy Storage: 150 L without solar and 300 L with solar Loads: DHW and space-heating (floor area of 140 m 2 ) Climate: Strasbourg, France SPF (concept 1): about 3.8 to 4.0 with BHE of 110 m and collector area between 5 m 2 and 15 m 2 SPF (concept 2): 4.95 and 5.21 for 5 m 2 and 10 m 2 of collector area, respectively and BHE of 110 m SPF (concept 3): about 4.8 with a 5 m 2 collector area and 110 m BHE SF : 65% with 5 m 2 of collectors (for DHW) Tamasauskas, Poirer, Zmeureanu, and Suny [28] Configurations: Indirect system with an ice slurry in a tank Collector Type: Flat plate Collector Area: 65.67 m 2 Collector Orientation: Tilt of 65.625E nergy Storage: 32.05 m 3 solar thermal tank and 1.5 m 3 warm water tank Loads: DHW and space-heating (floor area of 186 m 2 ) Climate: Montreal, Quebec SPF : 8.22 SF : 0.88 COP of the Heat Pump: 4.03 with the design evaporator inlet temperature at 0 and condenser inlet temperature of 20 Collector Efficiency: 0.43 (seasonal) Sterling and Collins [29, 30] Configurations: Dual tank indirect system and solar-side system Collector Type: Flat plate Collector Area: 4 m 2 Collector Orientation: Facing south with a tilt of 45E nergy Storage: 350 L DHW tank and 500 L float tank Loads: DHW Climate: Ottawa , Ontario SF : 0.67 for the dual tank system and 0.

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)0.001
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 score0.074
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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