Application of Autonomous Robotics for En-Masse Coolant Channel Replacement Program
Bibliographic record
Abstract
The paper summarizes current knowledge and practices used in India's En-Masse coolant channel replacement (EMCCR) program. The requisite of coolant channel replacement of the Indian pressurized heavy water reactor (PHWR) or Canadian Deuterium Uranium (CANDU) reactor type is essential which faces a big challenge in the current methodology. Current development uses partial automation and a power manipulator to do remote maintenance work in the EMCCR program. This program and process require various improvements to maintain international standards and operating procedures. Working in a high radiation area creates a lot of challenges to perform a critical components replacement and maintenance process. With the help of robotics and automation, the operation time and efforts of radiation workers in the radiation environment can be reduced. This paper describes available knowledge of various processes, measurement tools, mechanical components, and techniques used for standard safety practice in nuclear reactor components. It also suggests the implementation of robotics and automation systems to do autonomous operation and maintenance work in the industry. Primary research results of implementing automation, and robotics systems can be helpful to add additional safety for such a high radiation environment. The development can also reduce the time and cost applied for operation and maintenance work in nuclear power plants. This research will help industries to propose new designs and development of robotic manipulators, and automation systems for the operation and maintenance work in the nuclear industry.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".