Data-driven torque identification of turboprop engines using optimized feedforward neural networks
Bibliographic record
Abstract
This paper presents a research methodology for identifying the Pratt & Whitney Canada PW127G turboprop engine from simulation data using optimized feedforward neural networks (FNN). A set of measurable variables - ground speed, throttle lever angle, pressure altitude , high-pressure spool speed , and propeller speed - is used to predict normalized engine torque, providing a surrogate engine model suitable for integration into flight simulators. The methodology follows a two-stage strategy. First, a baseline L-BFGS–trained FNN is combined with two architecture-search methods, Extended Great Deluge (EGD) and Bayesian Optimization (BO). On the turboprop dataset, BO achieves a lower test RMSE than EGD and is therefore selected as the preferred architecture optimization strategy. Second, BO is fixed and used to optimize two FNN configurations: Baseline FNN with inputs (ground speed, throttle lever angle, pressure altitude , propeller speed ) and Core-Enhanced FNN additionally including high-pressure spool speed . The optimized Core-Enhanced FNN significantly reduces the root mean square error from 1.066 to 0.4834 on testing data, corresponding to an average error reduction of about 55% compared with Baseline FNN, and also decreases mean relative error and error variance, confirming the importance of core-speed information for high-fidelity torque prediction. The results demonstrate that L-BFGS–trained FNNs, combined with BO-based architecture search and simulation-derived data, provide an effective and computationally efficient surrogate engine model for turboprop torque (and indirectly thrust) estimation in advanced flight simulation and training applications.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".