Development of Neural Networks for Enhancement of Thermal Energy Storage using Phase Change Material
Bibliographic record
Abstract
Three Artificial Neural Network models (Feedforward, Elman, and Nonlinear Autoregressive Exogenous (NARX) networks) were used to find the performance of a thermal energy storage system with and without a phase change material. Previously obtained experimental data was used to train the neural network. Time, mass of water, mass flow rate, number of balls containing the PCM, hourly solar radiation, ambient temperature and inlet water temperature were used in the input layer of the network. The outlet water temperature was in the output layer. The obtained results were verified against previously obtained experimental data. It was found that Artificial Neural Network technique could be used to estimate the outlet temperature with excellent accuracy with the coefficient of determination of Elman, feedforward and NARX models were found to be 0.95006, 0.99411 and 0.88185, respectively. The obtained results showed that feedforward model had the best ability to estimate the required performance, while NARX and Elman network had the lowest ability to estimate it.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".