Bibliographic record
Abstract
Load-following capability is one of the distinguishing features between Small Modular Reactors (SMRs) and traditional large-scale reactors in existing nuclear power plants. An SMR needs to possess load-following capability to integrate with non-dispatchable energy resources, e.g., renewable ones, to form an off-grid hybrid energy system. This paper provides a comprehensive review of different aspects of load-following operations of nuclear reactors by systemically examining early studies and some practical operating experience on existing nuclear reactors in various applications. These applications include electricity generation, marine vessel and spacecraft propulsion, and heat generation for industry applications. Considering different advanced reactor design concepts proposed for SMRs, the paper has concentrated on six representative reactor types and highlighted their unique features and feasibilities for load-following operations. Since an SMR can be considered as a combination of the reactor and the balance-of-the-plant, its power output can be regulated at the reactor power output or from the balance-of-the-plant. Different techniques to implement load-following operations have been described regarding different reactor designs and compositions of the balance-of-the-plant. Several special issues deserve special attention when performing load-following operations. These issues are rarely encountered in existing nuclear power plants for base-load operation, for example, imbalance in core power distribution, flow-induced vibration, excessive production, higher risk of fission products leaking, and flow-accelerated corrosion. The paper has provided necessary coverage of these less obvious consequences associated with load-following operations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".