Analysis of Energy and Exergy for Mixed Convection Flow in Microstructure Filled Vented Enclosures
Bibliographic record
Abstract
Understanding heat transfer through saturated porous media is of great importance to many engineering and geophysical systems such as cooling the electronic devices and solar power collectors, and post-accidental heat removal in nuclear reactors. Large numbers of research studies have been and are conducted on the expanding field of porous media due to the high rate of heat transfer in these systems. Despite the efforts made towards the study of the mechanics of fluid flow through porous media, little is studied the rate of exergy which is the only factor presenting the rate of reusable energy potentially produced by a heat generating body. The objective of this study is to develop a design of experiment to carry out a numerical analysis of heat transfer in a rectangular enclosure filled with a saturated porous medium. The optimum heat transfer rate will be obtained for various configuration-related parameters, namely different inlet to outlet ratios and different inlet width to cavity width ratios. These parameters will be optimized to achieve maximum rate of heat transfer and minimum rate of entropy generation. The results of this study will also help to determine relationships for predicting the heat transfer characteristics of the enclosure.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".