Quantification of irreversibilities in practical cyclic processes using exergy analysis and Gouy-Stodola theorem
Bibliographic record
Abstract
The exergy analysis of a process to quantify the irreversibilities is advantageous over the entropy analysis in that it provides the definition of efficiency of a process, referred to as exergetic efficiency (defined as the ratio of exergy recovered to exergy supplied to the process). Unfortunately, the exergy analysis of practical multi-unit cyclic processes is rarely covered adequately in the undergraduate courses in engineering thermodynamics. In this article, the quantification of irreversibilities is illustrated in detail for a practical cyclic steam power plant using exergy analysis and Gouy-Stodola theorem. The efficiencies are determined for the various components and for the whole process. The theoretical background related to exergy, exergy analysis, and Gouy-Stodola theorem is also covered briefly for the benefit of the students. An assessment problem dealing with vapor-compression refrigeration cycle is included at the end in order to assess the intended learning outcomes of this article. The key solution steps along with answers are also provided for the benefit of the readers. As exergy analysis involves advanced level concepts in thermodynamics, the appropriate place for the introduction of the material presented in this article is the second, advanced level, course in thermodynamics.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".