Data-driven torque identification of turboprop engines using optimized feedforward neural networks

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.