Experimental and artificial neural network investigation on the effect of inclination angle on the interface temperature of CPU/metal foam heat sink
Bibliographic record
Abstract
Purpose This study aims to clarify the relationship between inclination angle of hot surface of CPU and its temperature in absence and presence of aluminum foam as a cooling system. It proposes application of the artificial neural [multi-layer perceptron (MLP) and radial basis function] networks and adaptive neuron-fuzzy inference system (ANFIS) to predict interface temperature of central processing unit (CPU)/metal foam heat sink. Design/methodology/approach To provide a consistent set of data, the surface of an aluminum cone with and without installing Duocel aluminum foam was heated in a natural convection using an electrical resistor. The hot surface temperature was measured using five K-type thermocouples (±0.1°C). To develop the predictive models, ambient temperature, input power and inclination angle are taken as input which varied from 23°C to 32°C, 4 to 20 W and 0° to 90°, respectively. The hot surface temperature is taken as the output. Findings The results show that in the presence of foam, the hot surface temperature was less sensitive to the variations of angle, and the maximum enhancement of the heat transfer coefficient was 23 per cent at the vertical position. Both MLP network and ANFIS are comparable, but the values predicted by MLP network are in more conformity with the measured values. Originality/value The effect of metal foam on the inclination angle/hot surface temperature dependence is identified. The optimum angle is clarified. The applicability of the MLP networks to predict interface temperature of CPU/heat sink is approved.
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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 | 0.001 |
| 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".