Experimental Study of Kinetic to Thermal Energy Conversion with Fluid Agitation for a Wind-Powered Heat Generator
Bibliographic record
Abstract
In this paper, a heat generator with fluid agitation is developed and experimentally studied. This heat generator can convert kinetic energy from a wind turbine directly to thermal energy through the process of viscous dissipation—a process achieved through the agitation of the working fluid inside a container. In the experimental study, an electric motor (instead of a wind turbine) was used to provide the kinetic energy input to the heat generator. The torque, rotational speed, and temperature rise in the fluid were measured. Using the measured quantities, the efficiency of kinetic energy to sensible heat conversion was calculated. Experiments were conducted to investigate the effects of different impellers, rotational speeds, and working fluids, including distilled water, ethylene glycol (EG), and their respective nanofluids, with Al2O3 nanoparticles at different concentrations. The study also found that the temperature rise in fluids due to viscous dissipation was influenced by the specific heat of the fluid, suggesting that the heat generator can be optimized for energy storage with high-specific-heat fluids, such as water, or for achieving a higher temperature rise with low-specific-heat fluids, such as ethylene glycol. The experimental results indicated that the heat generator was up to 90% efficient in converting kinetic energy to thermal energy. The study revealed that, for constant power input, the heat dissipation rate depends solely on the vessel’s geometry, not the fluid properties. Optimizing the impeller design and baffles within the vessel is crucial for maximizing power input. For applications, a wind turbine can power this heat generator to provide heat to a house or a commercial building.
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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".