Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures
Bibliographic record
Abstract
The present study investigates the dependency of prediction accuracy of an artificial neural network (ANN) on the network architecture using 65 different neural networks from seven architecture patterns. The accuracy of the ANNs is compared based on their capability to predict heat transfer coefficients of air-cooled heat sinks operating in laminar flow. Scattered input data is used for training the networks to make the modelling more realistic and closer to practical applications. The input variables for the neural network are heat sink width, channel height, channel length, number of channels, fin thickness, and Reynolds number. The output is heat transfer coefficient. The training process for all ANNs is performed using ReLU as the activation function. The accuracy of the neural networks is evaluated by the root mean square error. It is found that the prediction accuracy of an ANN is strongly dictated by the optimization of the network architecture, which corresponds to the proper number of hidden layers and the number of neurons at each layer. The most accurate architecture in the present study predicts heat transfer coefficients of 60% and 86% of heat sinks within 10% and 20% of the true values, respectively. However, an ANN with an unoptimized architecture results in a substantially reduced accuracy such that it predicts heat transfer coefficients of only 19% and 30% of heat sinks within 10% and 20% of the true values, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".