Prediction of Performance and Geometrical Parameters of Single-Phase Ejectors Using Artificial Neural Networks
Bibliographic record
Abstract
Ejectors have gained renewed interest in the last decades, especially in heat-driven refrigeration systems, to reduce the load of the compressor. Their performance is usually influenced by many factors, including the working fluid, operating conditions and basic geometrical parameters. Determining the relationships between these factors and accurately predicting ejector performance over a wide range of conditions remain challenging. The objective of this study is to develop fast and efficient models for the design and operation of ejectors using artificial neural networks. To this end, two models are built. The first one predicts the entrainment and limiting compression ratio given 12 input parameters, including the operating conditions and geometry. The second model predicts the optimal geometry given the desired performance and operating conditions. An experimental database of ejectors using five working fluids (R134a, R245fa, R141b, and R1234ze(E), R1233zd(E)) has been built for training and validation. The accuracy of the ANN models is assessed in terms of the linear coefficient of correlation (R) and the mean squared error (MSE). The obtained results after training for both cases show a maximum MSE of less than 10% and a regression coefficient (R) of, respectively, 0.99 and 0.96 when tested on new data. The two models have then a good generalization capacity and can be used for design purposes of future refrigeration systems.
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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 | 0.000 |
| 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 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".