Experimental Study of a Wind-Powered Heat Generator with Nanofluids Agitation
Bibliographic record
Abstract
Modern wind turbines are generally used for generation of electricity, however the final form of energy required by user in many cases is thermal energy. Although conversion of electrical energy to thermal energy is a high efficiency process but the efficiency of electricity generation from wind turbines is usually low. We proposed a novel direct wind thermal energy conversion (WinTEC) device that converts the kinetic energy from wind directly into thermal energy through the process of viscous dissipation which is achieved through agitation of a working fluid in a container. Water based Aluminum oxide nanofluid is used as working fluid because of its superior thermal fluid properties. This WinTEC device uses an optimized flat blade turbine (FBT) with a baffled design. We use four standard baffles each 10% the diameter of the cylindrical container for maximum power dissipation. An electric motor is used to provide the mechanical input and a torque sensor and tachometer are used to measure the efficiency of the heat generator in converting this mechanical energy into thermal energy. Experiments are conducted at different rotational speeds and for different working fluids: distilled water and nanofluid. Our results indicate that the rate of temperature rise increases for higher rotational speed and greater nanoparticle concentration. The device will be scalable to fit the size and need of a house or a commercial building. This innovative renewable energy technology would have a beneficial impact on the economic prosperity, environmental sustainability, and social well-being in many regions of the world, particularly, in remote cold regions with rich wind energy resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".