The Effect of Fluid Type and Volume on Concentrated Solar Sphere Power Generation
Bibliographic record
Abstract
Because of the rising need for renewable energy sources, several innovative systems that use natural resources to create energy and deliver power have emerged.The solar sphere system (a container) is a novel system that gathers and focuses solar energy emitted by the sun at a focal point on a multijunction device.The multijunction device is made up of a high-efficiency solar cell that transforms sunlight into energy.Many aspects/parameters in the solar sphere system influence the quantity of power generation and the related efficiency, resulting in increased overall system performance.The factors are the fluid medium inside the container/sphere and the volume or the amount of the fluid oil inside the sphere.In our previous research paper, the size, thickness, fluid medium, and some shapes were investigated.It was confirmed that the oil is the best fluid medium, and the sphere is the best shape to generate the highest efficiency and highest output power.As a result, the purpose of this work is to explore and investigate the possible kind of fluid oil, and the volume/amount of the oil inside the sphere to determine the influence of these factors on the performance of the solar sphere.The results of the trials revealed that these factors have a substantial impact on power output and system efficiency.From the results, it is found that the fluid oil type and the effect of fluid oil volume/amount inside the solar sphere significantly change the value of the output power.Hence, in order to improve the efficiency of the solar sphere, cooking oil (sunflower coconut, corn oil) is the best.Moreover, the acrylic sphere should be filled with oil completely in order to generate the highest output power and higher efficiency.
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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".