Bibliographic record
Abstract
Vibration control in the systems of precise positioning is an important problem that nanotechnology sector is facing. This problem challenges the engineers to develop advance positioning mechanisms such as hydraulic magnetorheological (MR) actuators or MR modules. These modules combine the characteristics of hydraulic systems and electromagnetic control because of the use of magnetorheological fluids instead of traditional hydraulic fluid. This allows to avoid using inertial valves that results in higher accuracy and dynamic characteristics as compared with conventional systems. The main element of a MR valve is a solenoid that creates a magnetic field to control viscosity and rheological behavior of the fluid due to structuring of the disperse phase of magnetic particles in magnetic field.The positioning error of the MR module depends, to great extent, on the minimum current which should be applied to the coil to start the motion. This work is aimed at the experimental study of the response of the MR module on the applied current. The response was measured as the pressure drop in the fluid at the exit of the MR module.It was found that the maximum magnetic field in the working gap of the module of 0.04 T corresponded to the pressure drop 0.12 MPa. The results form the base for design of MR modules of automatic control systems operating under semi-active and active vibration control modes.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".