A Practical Design Approach of the Tesla Turbine for Hydro Power Applications
Bibliographic record
Abstract
Abstract In the global push for harnessing energy in a vast number of scenarios, the Tesla Turbine stands out as a potential niche device in various small-scale applications. This device can be fabricated rapidly, using locally available materials and conventional manufacturing operations. Despite these advantages, several technological challenges have held back the development of the Tesla Turbine. Besides, modern bladed turbines have a much broader range of applications. Another issue is that this turbine’s most recent experiments were focused on numerical tests or findings from the computational simulation. Very few studies that were conducted resulted in low-efficiency ratings. For advanced applications such as particulate flow, biomedical, and some modern machining methods that use abrasive particle-rich fluids, Tesla turbines can also be used as it is not sensitive to mixed fluids compared to conventional bladed turbines. Moreover, the construction cost of a Tesla turbine is meager compared to bladed turbines. Thus, the cost-to-performance ratio is higher than that of conventional turbines, which the authors believe could pose much interest for both further research and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".