Calculation, simulation and fast estimation of thermal resistance of rotary voice coil actuators
Bibliographic record
Abstract
Abstract Rotary voice coil actuator (RVCA) is mainly used for reciprocating drive of small inertia loads in a limited range of angle, which is widely used in aerospace and other fields. Thermal resistance is one of the most important parameters of RVCAs, which is directly related to the steady‐state temperature rising and operation reliability. Because the thermal resistance cannot be measured directly, the analytical method is usually used to calculate the thermal resistance of the RVCA. However, due to the particularity of the thermal circuit calculation, the calculation is complicated and the result is not accurate. It is not conducive for engineering applications of this kind of actuator. In this paper, a series of rotary voice coil motor is selected as the research object. Through analytical calculation and thermal simulation, a more accurate thermal resistance calculation correction model and thermal resistance value of RVCAs are obtained, and the accuracy of the estimation results is verified by experiments. On this basis, the effective volume of the coil frame of the RVCA is selected as the variable, and the concise variation law of the series thermal resistance data is obtained. This paper provides a fast and accurate estimation method for the thermal resistance of RVCA, which has an important reference value for expanding the engineering applications of RVCAs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".