Algorithm-Driven Energy Conversion Efficiency Enhancement Technology for High Power Laser Devices
Bibliographic record
Abstract
With the continuous development of high-power laser equipment and the continuous expansion of the scope of the application platform, the demand and application of high-power laser equipment in various fields are becoming more and more extensive, and its output power has also put forward higher requirements. In order to promote the development of high power laser equipment toward higher energy conversion efficiency, research and design temperature control device to manage the waste heat generated in the energy conversion process of high power laser equipment. On the basis of PID control algorithm using LADRC algorithm, rapid realization of temperature precision control, so as to enhance the energy conversion efficiency of high-power laser equipment. When the temperature control device in the temperature control range of 10 ℃ ~ 40 ℃, the temperature control accuracy is better than ± 0.03 ℃, and in 144s to reach the set temperature, the temperature control overshoot is less than 2.33%, to meet the requirements of the laser working temperature control in the working process of high-power laser equipment, and to lay the foundation for the realization of high energy conversion efficiency. Compared with the modified PID controller, the energy conversion efficiency is relatively improved by 1.57%. The temperature control device designed based on the improved PID control algorithm in this paper can significantly improve the energy conversion efficiency.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".