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Record W4409381882 · doi:10.11159/jffhmt.2025.013

Conversion of Waste Energy from CI Engines to Electrical Power with TEG

2025· article· en· W4409381882 on OpenAlexvenueno aff
Jaafar Ali.Mahdi, Hasanain J. A. Juaifer, Hayder Jabbar Kurji, Murtdha S. Imran

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWaste managementPower (physics)Waste-to-energyElectric potential energyElectric powerEnergy transformationAutomotive engineeringEngineeringMunicipal solid wastePhysicsThermodynamics

Abstract

fetched live from OpenAlex

All internal combustion engines exhibit significant energy loss during operation; the cooling and exhaust systems dissipate the bulk of the energy produced during combustion.The use of this energy enhances engine efficiency.One use of this capability is to enhance the efficiency of the engine's air intake, achieved using a turbocharger.Utilise thermoelectric generators (TEGs) to transform thermal energy into electrical energy.This investigation used a TEG model ( 27145) installed externally at the exhaust port.A system of four thermoelectric generators was established and interconnected in both series and parallel configurations.Measurements of current, voltage, power, and temperature fluctuations between the two sides of the thermoelectric generator were conducted using measuring devices.The practical component of the research was conducted at an ambient air temperature of 37C, engine speed of 2340 rpm, specific fuel consumption of 0.173 kg/hr, and brake power of 2.34 kW.The empirical findings indicate that the maximum voltage and power achievable by connecting four thermoelectric generators in series are 13.33 volts and 11.25watts, respectively.Furthermore, when the thermoelectric generator was configured in parallel, the maximum current output was 3.14 amperes.

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 score0.425
Threshold uncertainty score0.346

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 head0.005
GPT teacher head0.207
Teacher spread0.203 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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