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Record W7091425839 · doi:10.4006/0836-1398-38.1.87

On the Carnot efficiency and the 2nd law of thermodynamics

2025· article· en· W7091425839 on OpenAlexvenueno aff

Bibliographic record

VenuePhysics Essays · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsCarnot cycleSecond law of thermodynamicsWork (physics)Laws of thermodynamicsHeat engineThermodynamic cycleFirst law of thermodynamicsFirst law

Abstract

fetched live from OpenAlex

A review is carried out on the general applicability of the Carnot efficiency of conversion of heat to work in a cycling engine. It is found that the expression for the calculation of such efficiency is correct when applied to cycles where the waste or unused heat from the engine is transferred outside the cycle in each and every cycle. In other words, for such engine, an entirely new amount of heat must be supplied in each and every cycle. If we call such cycle as a Carnot cycle, the 2nd law of thermodynamics is its most logical consequence whereby the efficiency of conversion of heat to work can never exceed the Carnot efficiency. Since the 2nd law of thermodynamics has the status of a law of physics, any attempt to question its validity or define its limitations is considered to be useless. Indeed, the present study confirms that the 2nd law of thermodynamics is a law of physics for the Carnot cycle, as defined above. If one considers a cycle where the waste heat is not transferred outside the cycle, but is recovered, recirculated, and re-used, one finds that the Carnot efficiency is a lower limit for higher efficiencies. The thermodynamic conditions required for a cycle to have the waste heat recovered, recirculated, and re-used are found from elementary analysis, and their validity are confirmed from numerical simulation and a proof-of-principle experiment. The consequences of such finding in terms of world yearly energy saving and climate change mitigation are highlighted.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.268

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 head0.232
Teacher spread0.227 · 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 designTheoretical or conceptual
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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