MétaCan
Menu
Back to cohort
Record W2557905104 · doi:10.1109/iemcon.2016.7746283

Modelling and simulation of a solar water heating system with thermal storage

2016· article· en· W2557905104 on OpenAlexaff
Ahmed Aisa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsThermal energy storageStorage water heaterEnvironmental scienceSolar energyEnergy storageHeating systemStorage tankNuclear engineeringPassive solar building designThermalSolar water heatingStorage heaterWater heatingThermal energyMeteorologyWaste managementThermodynamicsMechanical engineeringEngineeringElectrical engineeringWater heaterHeat exchangerHeat pumpPhysics

Abstract

fetched live from OpenAlex

This paper presents a solar thermal energy storage system used for domestic water heating purposes in a detached house setting. Solar heating systems with seasonal energy storage have attracted growing attention in recent decades. However, because the availability of solar energy is discontinuous, heat storage is an indispensable element in a building's solar energy-based thermal system. The objective of modelling is to determine the temperature of a tank and the heat loss of a system. System design is dependent on certain equations and data, such as temperature, time, and flow rate estimated in a lab. A BEopt and Matlab / Simulink model is used to determine the storage water temperature of the tank and house temperature. Additionally, the house has two heat sources: the hot water of the storage tank, and the solar used for heating. The maximum temperature of the storage tank was found to be 82.4°C and the temperature inside the house ranged from 18 to 25.11°C. Overall, the heating of the house needed 12,268 kWh/yr, while the total energy use was 19,537 kWh/yr.

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 score0.169
Threshold uncertainty score0.422

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 head0.205
Teacher spread0.187 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicSolar Thermal and Photovoltaic SystemsFrench-language works237,207