An Innovization-based Model to Approximate Geometric Parameters of Solar Chimney Power Plant for Desired Efficiency and Output Power
Bibliographic record
Abstract
Solar Chimney Power Plant (SCPP) is a sustainable source of power production. A SCCP is a renewable-energy power plant that transforms solar energy into electricity using a high chimney, surrounded by a large collector roof. Two main components of the SCPP are chimney and collector which their geometric parameters, consisting height and radius, play a significant rule in the amount of efficiency and output power of the SCCP. In this paper, a method is proposed to get the best values of such parameters to design a SCPP based on desired amount of efficiency and output power using evolutionary-based meta-modeling, optimization, and innovization techniques. In fact, while multi-objective optimization gives only a limited number of solutions which the designer has to select one of them with specific values of efficiency and output power, innovization on optimization results provides the possibility to approximate geometric parameters of SCPP for a desired amount of efficiency and output power without re-running the optimization process many times. The proposed model consists of three phases: 1) using simulation data, a mathematical model is obtained to get the values of efficiency and power based on the geometric characteristics, 2) a multi-objective optimization is conducted to maximize both objectives, efficiency and output power, 3) using innovization on optimized solutions, mathematical models are obtained to get values of geometric parameters based on desired efficiency and power values. Several experiments are conducted to get the results for each phase. Based on comparing the predicted efficiency and power with desired ones, the results are promising with a low level of error values.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".