Experimental Study On Direct Kinetic To Thermal Energy Conversion
Bibliographic record
Abstract
Modern wind turbines are generally used for electricity generation; however, the final form of energy required by many users is thermal energy. Although electrical energy conversion to thermal energy is a high-efficiency process, electricity generation efficiency from wind turbines is usually low. A wind-powered heat generator is proposed that would convert the kinetic energy directly into thermal energy through the process of viscous dissipation; this process is achieved through the agitation of the working fluid inside a container. This heat generator uses an optimized flat blade turbine (FBT) impeller and a fully baffled configuration. For the experimental study, an electric motor is used to provide the kinetic energy input to the heat generator. A torque sensor, tachometer, and thermocouples are used to measure torque, rotational speed, and temperature rise of the fluid, respectively. Using the measured quantities, the efficiency of energy conversion from kinetic energy to sensible heat energy is determined. Experiments are conducted at different rotational speeds and for different working fluids. The experimental results indicate that the heat generator is up to 90% efficient in energy conversion, and the temperature rise rate increases with an increase in the impeller's diameter and the rotational speed. Furthermore, experiments indicate that changing working fluid does not impact heat generator's performance. A wind turbine can power this heat generator to provide heat to a house or a commercial building. This innovative renewable energy technology would benefit remote areas with cold weather and rich in wind energy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".