Analysis and optimization of a transcritical power cycle with regenerator using artificial neural networks and genetic algorithms
Bibliographic record
Abstract
This article presents a parametric study for and optimization of a transcritical power cycle. First, thermal efficiency, exergy efficiency, and specific network are selected as objective functions for parametric optimization. In order to optimize these functions, a procedure based on artificial neural networks (ANNs) and genetic algorithms (GAs) is proposed. This procedure comprises three steps. Step 1 is to find thermal efficiency, exergy efficiency, and specific network for different values of inlet turbine pressure, inlet turbine temperature, and fraction of the maximum power using the robust numerical code, engineering equation solver. In step 2, three distinct multi-layer perceptron ANNs based on the data obtained from step 1 are trained. In step 3, three distinct GAs are used to optimize the thermal efficiency, exergy efficiency, and specific network. The variables and fitness functions in these algorithms constitute, respectively, the inputs and outputs of the corresponding trained neural networks. For the purpose of validation of this study, for a special case, the results were compared with a previously reported case and were found to be in good agreement. Also in this article, this optimization process is applied to four different working fluids. Several interesting features among optimal objective functions and decision variables involved in the transcritical power cycle are identified.
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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".