A Performance Simulation Methodology for a Whole Turboshaft Engine Based on Throughflow Modelling

Abstract

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier–Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier–Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt and Whitney’s three-stage axial compressor (P&W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

“…Regulating maximum power based on gas-generator rotor r.p.m. is important for achieving optimal efficiency and reducing fuel consumption [8,9]. Employing mathematical models and neural networks predicts parameter relations, enhancing system control and efficiency and aligning with aviation industry requirements for performance and energy efficiency [10].…”