Modelling the Translational Dynamics of the Atlas Motion Platform
Bibliographic record
Abstract
The novel Atlas motion platform is designed to prescribe in six degrees of freedom the motion of a spherical simulator cockpit with the unusual capability of providing kinematically-uncoupled translational and rotational motion, unbounded rotational motion, and a fully-dexterous workspace.In support of the design and operation of the Atlas simulator, a translational dynamic model of the system has been developed.It includes the prescribed motion of the interface between the translational and rotational stages, translational motion of the spherical cockpit, and translational motion of the upper 'halo' support structure.Contact points between the spherical cockpit and the constraint/actuation system were modelled using directed elements consisting of either constant-force elements or spring/damper elements.In the case of the spring/damper elements, stiffness and damping parameters were obtained by a combination of finite element analysis of structural elements and experimental characterization of viscoelastic elements along the interfacing load paths.The dynamic model is suitable for evaluating internal forces and relative motions within the simulator resulting from translation motions; and forms the basis for extension to include rotational dynamics of the simulator system.
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 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".