Helicopter Turboshaft Engine Database as a Conceptual Design Tool
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
<div>Many interconnected parameters are involved in the helicopter turboshaft engine’s design, implying numerous limitations on the design process. These parameters include the key parameters such as weight, dimensions, power, specific fuel consumption, combustion temperature, air mass flow rate, and compressor pressure ratio, all of which correlate with one another and collectively affect the engine’s design process and consequently the helicopter. The first step in any design process is the <i>conceptual design</i> stage, where using an initial guess, an iterative parameter estimation runs until convergence. For the initial guess, a database is required, and for estimation, knowledge of the relationships between different parameters is mandatory. Hence, as an effort to help with this process and given that no publicly available database exists for turboshaft engines, in this work, a unique and comprehensive database of turboshaft engines along with novel insights into useful design parameters and their correlations are presented.</div>
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 | 0.000 | 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) | 0.001 | 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