Bibliographic record
Abstract
When analysing the performance of aero-engines with incomplete input data, it is often necessary to estimate the value of the missing parameters. This paper describes the work completed to develop correlations linking various turbojet parameters using a database of nearly 100 data points. The study was limited to small single-spool turbojets with static thrust up to about 8896 N (2000 lbf). For afterburning turbojets, the information for dry operation was used. Two approaches were implemented. A Single-Input-Multiple-Output (SIMO) approach was tried with thrust as the single input variable. This approach met with success, but for only a few cases. A Multiple-Input-Single-Output (MISO) approach was tried with the specific thrust as the single output parameter. This approach worked well only when the number of known input variable values was large. For example, using the correlations derived in this research, a propulsion analyst could estimate the specific thrust from known turbine inlet temperature, fuel-to-air ratio and the pressure ratio with a reasonable degree of confidence. In the process of deriving SIMO and MISO correlations, a number of Single-Input-Single-Output (SISO) correlations were also developed. Some give fine results while others lead to excessive scatter. The paper describes the process of deriving the correlations and reports on the quality of the fits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".