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Record W2995735720 · doi:10.1063/1.5141431

Design and development of heat recovery steam generation system for automotive engines

2019· article· en· W2995735720 on OpenAlexaff
R. Udayakumar, R. Reeghesh, C. Periasamy, Aneesh Baburaj

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWaste heatInternal combustion engineWaste heat recovery unitHeat engineInternal combustion engine coolingAutomotive engineeringExternal combustion engineEnvironmental scienceWork (physics)Stirling engineBrake specific fuel consumptionWaste managementProcess engineeringHeat exchangerCombustionEngineeringMechanical engineeringCombustion chamber

Abstract

fetched live from OpenAlex

In view of the increasing energy demand due to rapid development in all the sectors, and a relative shortage of energy, the energy management with respect to internal combustion engines have gained more importance recently. Out of the total heat supplied to the engine in the form of fuel, approximately, 30 to 40% is converted into useful mechanical work. About 60% of the generated energy is still lost, half of which being exhaust heat, with the remaining half as heat absorbed by the engine cooling system resulting in an entropy rise and serious environmental degradation. This is so even after implementing the various technologies such as direct fuel injection, variable valve timing, exhaust-driven turbo charging, brake energy regeneration etc. Hence it is required to utilize this waste heat into useful work. The recovery and utilization of waste heat not only conserves fuel but also reduces the amount of waste heat and greenhouse gases which dampen the environment. A heat recovery steam generation system designed for 3.0 Liter BMW engine and amount of heat energy that can be utilized is discussed in this paper. It is found that around 20 to 30% of the engine brake power requirement may be met from this regeneration system at different engine loads and speeds of the engine.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.535
Threshold uncertainty score0.379

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 head0.029
GPT teacher head0.218
Teacher spread0.189 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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