Model‐based adaptive kinematic transformation method for accurate control of multi‐DOF boundary conditions in conventional tests and hybrid simulations
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it