Examine Fuzzy System to Present an Equilibrium Model for the Internal Pressure Losses of Alpha Type Stirling Engine: Comparison with ANN Model
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".