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Record W7081980604 · doi:10.11159/htff25.109

Design and Analysis of Moveable Solar Water Heater

2025· article· en· W7081980604 on OpenAlexvenueno aff

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsSizingPipingSolar energyWater flowRange (aeronautics)Flow (mathematics)Storage water heaterThermalSolar water heating

Abstract

fetched live from OpenAlex

Solar water heating systems are a fantastic way to harness the sun's energy to heat water for domestic use.By converting solar energy into thermal energy, these systems can significantly reduce reliance on traditional energy sources and lower energy costs.Solar water heaters provide a wide range of benefits.Such as the reduction of energy cost consumption, and environmental sustainability.However, there are inconveniences with this type of technology.It will be influenced by location, weather, system designs, and quality of the components.The aim of this project is to improve the design of solar water heater components such as solar collectors, water tank, piping system, and pump using SolidWorks.SolidWorks was used to simulate conditions akin to Boston, Massachusetts, in early July, around noon, which is considered optimal for real-life testing.Factors such as head loss in each connection and variations in water temperature within the piping and the pump have not been fully accounted for.The flow rate of water at the intake and outtake points may fluctuate based on the water's temperature, introducing uncertainties into the system's performance.Moreover, the outtake area can be influenced by the sizing of the pipes, thereby impacting the efficiency of the pump.Changes in pipe sizing could alter the flow dynamics, affecting the overall efficiency of the system.These complexities highlight the need for further analysis and refinement to ensure accurate modeling and interpretation of results.On the other hand, the system was designed to optimize the energy capture by continuously adjusting the position of the solar panels to follow the sun's path.We faced some setbacks when assembling the motors to the base and collector frame, including alignment issues and motor synchronization challenges.These hurdles required careful calibration and iterative adjustments to ensure that the motors operated smoothly and accurately.Despite these initial difficulties, the solar tracking system ultimately met and even exceeded its objectives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.192
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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