Bibliographic record
Abstract
This study presents a custom guide vane design to substantially reduce the waste energy in axial fans. Ideally, there is no velocity component in the radial direction in axial fans, but in practice, the air leaving the axial fan has a large tangential component of velocity which produces a large amount of swirl kinetic energy. In order to solve this problem, a guide vane is designed to remove the rotational component of the air. The methodology described in this project is based on the fundamental governing continuity, momentum, and energy equations using the Finite Volume Method (FVM). In this project, the standard k-ω model is used for turbulent modeling. Two dimensional (2D) geometry of blades and airflow cross-section are designed using AutoCAD and CATIA while GAMBIT is employed to generate a suitable mesh for the three dimensional (3D) model. The mesh independence test is done to analyze the performance. The axial fan is simulated using FLUENT software to prove an increase in airflow rate after using the guide vane. Considering the final results, it can be observed that the airflow is increased up to 6.3%.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".