The importance of equation<i>η</i>=<i>μn</i><sup>2</sup>in dimensional analysis and scaled vehicle experiments in vehicle dynamics
Bibliographic record
Abstract
Dimensional analysis has been very helpful in experimentation of very large or small-scale engineering systems. A good example would be experimentation on the aircrafts and ships, which was made cost-effective and simple by dimensional analysis. The history of dimensional analysis mentioned in the introduction section of the present document includes many of such applications. Automotive industry, however, never felt the need as the price or the size of land vehicles did not make experimentation so far-fetched; therefore, there are many crash tests which every new vehicle has to go through before mass production This changed with the imminent introduction of autonomous vehicles, which brought all the risks involved in experimenting with them. Many cost-effective experimental platforms are introduced, such as QCar https://www.quanser.com/products/qcar/ or laboratories, such as Scaled Autonomous Vehicles Indoor (SAVI) https://cast.tamu.edu/research/technology-demonstrator-platforms/scaled-autonomous-vehicles-indoor-tdp/. The present study will enable the results taken from such platforms to be translated to real-sized vehicles, enabling researchers to study dynamics of various vehicles. Classical vehicle equations of motion including constant velocity, accelerating bicycle model and roll model have been made dimensionless. The case of steady-state responses is also calculated in a dimensionless form. Some practical numerical examples are also mentioned as a proof of theory.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".