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Record W2587475762 · doi:10.1177/0306419016689501

Demystification of the Gouy-Stodola theorem of thermodynamics for closed systems

2017· article· en· W2587475762 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2017
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExergySecond law of thermodynamicsThermodynamicsEntropy (arrow of time)Work (physics)MathematicsIrreversible processLaws of thermodynamicsStatistical physicsApplied mathematicsMathematical economicsCalculus (dental)Theoretical physicsNon-equilibrium thermodynamicsPhysics

Abstract

fetched live from OpenAlex

In the analysis and design of a process from a thermodynamics perspective, the Gouy-Stodola theorem of thermodynamics is very important. It provides a means to improve the efficiency of a process by quantification and minimization of the irreversibilities in the process. According to this theorem, the work lost or the exergy destroyed in a process is directly proportional to the amount of entropy generated in the process. Surprisingly, this theorem has received little attention in the engineering thermodynamics courses taught at the undergraduate level. The textbooks on thermodynamics rarely mention this theorem. The exact mathematical form of this theorem is not clear and the conditions under which this theorem is applicable are also not clearly stated in the existing literature. In this article, a complete analysis of the Gouy-Stodola theorem is presented for closed systems and its limitations are pointed out.

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.

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)0.001
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 score0.864
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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