Bibliographic record
Abstract
The study of dynamical performance of new Flywheel for any type of engine is presented. Design and structure of proposed Flywheel is based on George Nerubenko US Patent 7,464,800 having the control system with instantaneous frequency tuner and variable damping device adjusted for all operational frequencies in running engine. The patented scheme would be applied for a design of Flywheels successfully replacing the conventional and dual mass flywheels. A description, structural details and mathematical model of considered Flywheel are presented. The model based on the system of differential equations describing the rotation and vibration of mechanical components combined to Coulomb dry friction equations reflecting the contacts in variable damping device has been used for the analysis of the dynamic behavior of engine crankshaft system having proposed Flywheel. The analysis is presented for semi-controlled version of Tuned Flywheel equipped with variable damping device. The presented solutions are allowing be concentrated on a reduction of the variations of instantaneous value of rotational speed (angular velocity) forcing the elimination of a coefficient of fluctuation of rotational speed. Tests have been conducted for a comparison of the dynamic performance of presented Flywheel to dynamic data of conventional and dual mass flywheels. Test results of prototype of Tuned semi controlled Flywheel equipped with variable damping device show the reduction of a coefficient of fluctuation of rotational speed to 0.004 which is lower than required bottom level.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".