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Record W3089624701 · doi:10.1115/1.4048065

Thermoelectric Generator–Integrated Solar Air Heater: A Compact Passive System

2020· article· en· W3089624701 on OpenAlexafffund
Kumar Venkateshwar, Abu Raihan Mohammad Siddique, Syeda Humaira Tasnim, H. Simha, Shohel Mahmud

Bibliographic record

VenueJournal of Energy Resources Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsThermoelectric generatorSolar energyElectric potential energyAutomotive engineeringElectricity generationThermal energyEnvironmental sciencePassive solar building designAirflowElectricityMechanical engineeringThermoelectric effectElectrical engineeringProcess engineeringEnergy (signal processing)EngineeringPower (physics)

Abstract

fetched live from OpenAlex

Abstract Solar air heater is a promising, economically viable, and matured technology for space heating and drying applications. One of the primary reasons for the limited usage of a solar air heater in developing countries is the unavailability of continuous electricity supply. Although the solar air heater is theoretically passive, practically electrical energy is required to achieve a steady airflow. Therefore, the unreliability of electricity forces people to rely on firewood for heat during the cold weather, which has severe effects on health and climate change. In the present work, the potential of thermoelectric generators (TEGs) to meet the electrical energy requirement of a solar air heater is studied. Two configurations, each with three different numbers of stages of TEGs, are analyzed. The effect of the integration of TEGs on the thermal performance of solar air heater is analyzed alongside the comparison between the electrical energy required by solar air heaters and electrical energy generated upon the integration of TEGs. A numerical model is developed in matlab and validated using the experimental results. One of the designs meets the electrical energy requirement of the fan in a wide operational range but lowers the process heat generation by approximately 1–6.25%. The electrical energy generated by the other design falls short of demand posed by the system in most operating range. However, the thermal energy generation is marginally higher compared to that of the conventional solar air heater.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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 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

Citations8
Published2020
Admission routes2
Has abstractyes

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