Ansys Mechanical Automation using Python for the Steady State Thermal Analysis of Fins
Bibliographic record
Abstract
A numerical investigation of the heat transfer enhancement through fins using the Ansys Mechanical solver is presented. Results are given for a uniform fin with elliptical cross-sections and uniform heat flux applied on its base while heat is dissipated to its surroundings by convection from both its lateral surface and tip. The peak temperature at the base of the fin is used to evaluate the thermal performance. Ansys Mechanical solver is automated using Python scripting to run 792 simulations for various materials, fin lengths, and ratios between the minor and major axes of the elliptical cross-sectional shape for both cases of natural and forced convection. The use of the original automated numerical procedure significantly decreases the computational time and the user intervention. It was found that the thermal performance is improved by increasing the length of the fin, using a material with higher thermal conductivity, or having a ratio between the minor and major axes of the ellipse that is farther from unity. Forced convection gives better thermal performance compared to natural convection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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".