Using Santos Pro™ trade-off analysis to inform the rear chassis design of a novel electric scooter
Bibliographic record
Abstract
The need for nimble, eco-friendly transportation solutions in metropolitan areas continues to rise. To meet this need companies have begun to design and manufacture small profile, high payload, electric scooters. Yet, balancing the claim space requirements for payload, mechanical systems, and the battery pack while also maintaining effective occupant packaging considerations is a challenge. The claim space required for a battery that can sustain a sufficient driving range (>150km) directly influences the shape and size of the rear chassis. However, the size and shape of the rear chassis also influences the potential for an occupant’s heel and calf to catch under the rear chassis when raising or lowering a foot for balance during common vehicle maneuvers. The aim of this analysis was to identify possible collision points between the heel or calf of a 50th and 95th percentile male stature occupant and the rear chassis when lowering a foot towards the ground when the scooter was in upright and tilted by 30° positions (turning). Santos Pro™ (SantosHuman Inc., Coralville, IA) was used to model the required occupant behaviors. The Zone Differentiation tool then generated a range of motion volume map for the calf and heel assuming a seated occupant posture. The volume map was overlaid on the geometry to assist the engineering team in visualizing possible collision points for each avatar. The engineering team was able to revise the geometry for the rear chassis to reduce overlaps with the heel and calf volume map, while also maintaining minimum claim space needs for the battery pack. The improved rear chassis design was then imported into the Santos Pro™ software to visualize and verify the reduction of potential collision locations. Santos Pro™ provided a time-efficient design-on-the-fly method to understand the potential severity of, and to correct, a heel and calf clearance concern within early-stage CAE. By evaluating the clearance concern proactively, the problem was quantified and solved within days, prior to costly physical prototyping and human testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".