Challenges in aero engine performance modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a continuous drive for ever more efficient aero engines due to environmental as well as economical concerns. As the technology of conventional turbofan engines matures, there is a need for new aero engine concepts as well as incremental improvement of existing technologies. In order to improve existing turbofan architectures there is a trend towards integrating the design of the different components in the whole engine system. This creates new challenges both within engine manufacturing companies and between overall equipment manufactures (OEMs) and their suppliers. Methods need to be developed where different component requirements can be balanced against each other for the best performance of the system as a whole. Furthermore, there is a need for multidisciplinary design and optimization, coupling simulations involving several different computational disciplines. In this thesis, a method for consistent conceptual design is presented. In consistent design, the outcomes of the conceptual design are used to iteratively update the assumptions made in the initial thermodynamic cycle calculations until they are consistent. This enables the designer to balance different components against each other. In addition, a first coupling study of a turbine rear structure and whole engine performance is made, indicating the necessity of coupled simulations. Some considerations regarding modeling of engines at conditions far off-design are made. This is needed because some dimensioning mechanical load cases occur at these operating points. Finally, non-hierarchical analytical target cascading is introduced as a method that can be used for coupled optimization during the remainder of this research project.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it