MétaCan
Menu
Back to cohort
Record W4293255246 · doi:10.1139/tcsme-2021-0160

Optimization of thermal storage using different materials for cooking with solar power

2022· article· en· W4293255246 on OpenAlexvenueno aff
Mahad Shaikh, Muhammad Uzair, Syed Ahmad Raza

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsThermal energy storageProcess engineeringEnvironmental scienceSolar powerMaterials scienceSolar energyThermalEnergy storagePower (physics)EngineeringMeteorologyElectrical engineering

Abstract

fetched live from OpenAlex

Previous studies into the use of solar power have been limited to storage materials and general applications; however, our study focused on the use of solar power for food preparation (cooking) and the selection of materials for the major components (storage material, insulation, convective lid) of a thermal energy storage system to optimize the use of solar power for cooking when sunlight is absent, e.g., evening/night. Our study incorporated different materials for each component, considering system performance and the costs of materials as key evaluation parameters. Based on our results, we optimized the system using selected materials and then compared it with an experimental model used for validation. Solar salt was selected as the storage material, sugarcane fibers were used as insulation, and copper was used as the convective material for the lid of the cooking pot. The results showed that the optimized system was 38.8 % more efficient than the experimental model. Moreover, the materials we selected for the optimized system are inexpensive, increasing affordability and thus encouraging consumers to use this eco-friendly system.

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: none
Teacher disagreement score0.686
Threshold uncertainty score0.463

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicPhase Change Materials ResearchFrench-language works237,207