Examine Fuzzy System to Present an Equilibrium Model for the Internal Pressure Losses of Alpha Type Stirling Engine: Comparison with ANN Model
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Bibliographic record
Abstract
Global warming associated with the greenhouse effect urge finding alternative energy strategies concerned with sustainable energy resources that are environmentally friendly and provide energy saving. Waste heat recovery engines are attracting devices that convert usually wasted energy to valuable mechanical or electrical energy. The current research aims to develop a mathematical model to investigate the effects of regenerator physical dimensions on the alpha Stirling engine performance indicators. A mathematical model integrating an internal pressure drop has been proposed to act as a thermodynamic optimization tool for the Stirling engine. The main conclusion was that both geometrical factors and working fluid initial charge (gas mass) craft the performance parameters of alpha type Stirling engine that operates with air as working material. After that, Artificial neural networks of Levenberg Marquardt and Orthogonal Distance Regression models, and Fuzzy systems trained for Mass Charge from M = 0.002 to 0.004 Kg are compared to find the least uncertainty. Results revealed that the Fuzzy system and Orthogonal Distance Regression model could predict more effectively than the Levenberg Marquardt model.
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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) | 0.001 | 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) | 0.000 | 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 it