Computational Assessment of Double-Inlet Collector in Solar Chimney Power Plant Systems
Bibliographic record
Abstract
Solar chimney power plant systems (SCPPS) offer a simple and reliable way to generate electricity using solar radiation to drive a flow of buoyant air. A typical SCPP setup includes a collector, a tower, and a turbine or several turbines. Current SCPP designs have low thermal efficiency: only between 0.5% and 5% of the incident solar energy is converted into electricity. Inefficiencies result partially from limited mass flow rates through the tower. It is therefore desirable to provide a new design for the collector to increase the inlet air mass flow rate. In this paper, we present a double-inlet collector concept and results of numerical analysis to evaluate this design in terms of flow rate improvement. Computational fluid dynamics (CFD) was utilized to perform the numerical modeling and simulation (M&S) by using a finite volume method package. The Manzanares prototype (the only operational solar tower power plant with available published reports) is selected to implement the double-inlet collector design and study its effect on the power plant. Beside this case, we fabricated a 1/1000 scale model of the Manzanares prototype which enables us to measure the filed variables experimentally. Validation analysis was performed to quantify the reliability of our numerical model with respect to the available experimental data. We obtained a significant increase (14%) in the available output power by using the double-inlet collector.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".