Model‐based adaptive kinematic transformation method for accurate control of multi‐DOF boundary conditions in conventional tests and hybrid simulations
Bibliographic record
Abstract
Abstract Several actuators need to be controlled to impose a multi‐degree of freedom displacement boundary conditions on a specimen in multi‐axial hybrid simulations or conventional multi‐axial displacement‐controlled tests. As the displacement boundary conditions are typically defined in the Cartesian coordinate system, kinematic transformation is required to transform the boundary conditions into actuator strokes. In previous studies, the kinematic transformation was carried out assuming no elastic deformation of the reaction system where the actuators and specimens are mounted. Accordingly, the kinematic transformation becomes inaccurate if the elastic deformation are not negligible, thereby impacting the accuracy of the experiments. There are methods to compensate for these errors by instrumenting specimens, but the existing methods often require many iterations or do not monotonically approach the target displacements. This study proposes a new method for kinematic transformation from the Cartesian system to the actuators’ local coordinate systems. The method adopts a model identification technique by which the influence of the elastic deformation can be effectively considered in calculating the actuator strokes. Numerical verification and experimental validation with the proposed transformation method are carried out. The results show that the proposed transformation method can decrease the number of iterations to achieve the target displacement boundary conditions and thus avoiding overshooting the displacement boundary conditions and reducing the interaction between actuators. It is expected that the proposed method can reduce the overall time to run a multi‐axial hybrid simulation or multi‐DOF displacement‐controlled experiments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".