A review of convective and artificial vortices for power generation
Bibliographic record
Abstract
Thermal energy transfer in the atmosphere occurs from a high temperature zone to a low one by means of convective vortices where mechanical energy is produced.There are two ways of driving vertical flow in the core of a vortex: (1) by the direct action of buoyancy acting on hot air and (2) by producing a vertical pressure gradient along the axis of a vortex because of core development involving the lateral spread of the vortex with height.In particular, it indicates that the intensity of convective vortices depends on the depth of the convective layer via thermodynamic efficiency, the enthalpy perturbation across them, and the existence of sources of vorticity.The atmospheric vortex engine (AVE) is a device for producing an artificial vortex.The operation of AVE is based on the fact that the atmosphere is heated from the bottom and cooled at the top.By artificial vortex generation, it is aimed to eliminate the physical solar updraft tower and reduce the capital cost of solar chimney power plants.This paper reviews natural convective vortices and vortex creation via physical principles of vortex generation.Vortex analysis and modelling are presented.Furthermore, a new model of a solar vortex engine (SVE) is proposed and discussed.An idea on utilizing the solar energy as the heat source for establishing the vortex and operating the SVE model is adopted.The SVE model is feasible and promising for electrical power generation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".