Bibliographic record
Abstract
In order to assess the remaining life of gas turbine critical components, it is vital to accurately define the aerothermodynamic working environments and service histories. As a part of a major multidisciplinary collaboration program, a benchmark modeling on a practical gas turbine combustor is successfully carried out, and the two-phase, steady, turbulent, compressible, reacting flow fields at both cruise and takeoff are obtained. The results show the complicated flow features inside the combustor. The airflow over each flow element of the combustor can or liner is not evenly distributed, and considerable variations, ±25%, around the average values, are observed. It is more important to note that the temperatures at the combustor can and cooling wiggle strips vary significantly, which can significantly affect fatigue life of engine critical components. The present study suggests that to develop an adequate aerothermodynamics tool, it is necessary to carry out a further systematic study, including validation of numerical results, simulations at typical engine operating conditions, and development of simple correlations between engine operating conditions and component working environments. As an ultimate goal, the cost and time of gas turbine engine fleet management must be significantly reduced.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".