Natural convective heat transfer from interrupted rectangular fins
Bibliographic record
Abstract
Heatsinks are widely used in various industrial applications to cool electronic, power electronic, telecommunications, and automotive components.Those components might be either high-power semiconductor devices, e.g., diodes, thyristors, IGBTs and MOSFETs, or integrated circuits, e.g.audio amplifiers, microcontrollers and microprocessors.More precisely, the passive cooling heatsinks are widely used in CPU cooling, audio amplifiers and power LED cooling.In the work herein, steady-state external natural convection heat transfer from vertically-mounted rectangular interrupted finned heatsinks is investigated.After regenerating and validating the existing analytical results for continuous fins, a systematic numerical, experimental, and analytical study is conducted on the effect of the fin array and single wall interruption.FLUENT and COMSOL Multiphysics software are used in order to develop a two-dimensional numerical model for investigation of fin interruption effects.To perform an experimental study and to verify the analytical and numerical results, a custom-designed testbed was developed in Simon Fraser University (SFU).Results show that adding interruptions to vertical rectangular fins enhances the thermal performance of fins and reduces the weight of the fin arrays, which in turn, can lead to lower manufacturing costs.The optimum interruption length for maximum fin array thermal performance is found and a compact relationship for the Nusselt number based on geometrical parameters for interrupted walls is presented using a blending technic for two asymptotes of interruption length.
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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 | 0.000 | 0.000 |
| 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".