Enhancements to the Load Acceptance and Rejection Capability of a High Pressure Aeroderivative Engine
Bibliographic record
Abstract
This paper describes improvements to the control of a high pressure, aeroderivative industrial gas turbine in order to better accommodate rapid load changes. In such circumstances it is important to maintain the speed of the driven equipment within an acceptable range. This can require the gas turbine to quickly adjust to the new load, to minimize the power imbalance, which is the cause of the speed variation. The paper describes the theory behind control schedules required to achieve this, and how they relate to avoiding surge, flameout or instability, while minimizing speed variations of the driven equipment. A whole engine thermodynamic model coupled to the control software was used to simulate the engine response during these rapid transients. The features of this model are described. The model allowed optimization of the control software in advance of the engine test. Results of whole engine tests are presented and compared to the model; and the types of load steps that remain most challenging are highlighted. The resulting capability remains partly determined by the specifics of the application, for example the inertia of the driven equipment, the nominal speed of operation, and the allowable speed variations. The effects of these can be predicted using the model and are discussed.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".