Surrogate Modelling of a Turboprop Engine Performance
Bibliographic record
Abstract
Abstract This paper addresses the simulation of turboprop engine performance through Artificial Neural Network (ANN)-based modelling. It arises from the need for developing surrogate models that explore different engine designs and their entire flight envelope operations, while being both fast, accurate, and convenient for preliminary design. First, a database is generated using a commercially available thermodynamic cycle software. Design parameters of interest include performance, thermodynamic and geometric attributes of the engine and its propeller, while operating conditions cover an envelope of ambient temperatures, altitudes, flight speeds and aircraft offtakes. Their variability is captured through a Latin-Hypercube sampled Design of Experiment (DoE). The produced datasets are used to train a Multi-Layer Perceptron (MLP) to predict the engines resulting power and fuel consumption, while results are evaluated based on the Mean Absolute Percentage Error (MAPE). The conventional – random – split of data for training, validation and testing according to set ratios is found to yield inconsistent results, when testing for unseen engines that are not within the training dataset. Thus, a new method for structuring the subsets in terms of whole engine designs is proposed. While both methods result in equal ANN performance on unseen engine designs, the new method overcomes the inconsistency identified for the conventional practice with the validation/testing subsets. The final ANN-based surrogate model yields a 3.3% accuracy while predicting absolute or non-dimensional power. Finally, predictions for fuel flow and Specific Fuel Consumption (SFC) yield a 7% MAPE.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".