Induced mechanical motion by thermal solar energy
Bibliographic record
Abstract
Increasing energy demand and cost are the main motivators for this research project. Solar thermal energy is used to create mechanical motion. In turn, the mechanical motion drives a generator to generate electricity. The objective of this project is to use solar radiation to heat a black fined metal tank filed with air. The air is heated by the solar radiation and expands leaving the tank through a nozzle, which is mounted on the top outlet valve. A turbine wheel is driven by the force of the hot air flow when the air is leaving the nozzle causing a mechanical rotation. An electric generator is coupled to the turbine wheel generating electricity. The generated electricity can be used for direct and indirect applications. To put the concept in practical research work, a prototype is designed, constructed, and tested under lab conditions. When the black fined metal tank is heated by simulated heat source, the air expands by natural heat convection from the heat that is conducted from the walls of the metal tank. When air temperature increases the density decreases and causing the hot air flows to rise by the power of the natural bouncy effect. The black metal tank is used as air storage. Control valves are mounted on the air inlet and outlet on the black metal tank to control the air flow in and out. Consequently, the valves control the inside tank air temperature. Preliminarily tests showed that the prototype works satisfactorily under artificial heat source and lab conditions. The prototype will be tested under real environment conditions to verify the results.
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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 | 0.000 |
| 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) | 0.006 | 0.001 |
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".