Abstract
Aero-engine real-time models are widely used in control system design, integration, and testing. They can be used as the basis for model-based engine intelligent controls and health management, which is critical to improve engine safety, reliability, economy, and other performance indicators. This article provides an up-to-date review on aero-engine real-time modeling methods, model adaptation techniques, and applications for the last several decades. Besides, future research directions are also discussed, mainly focusing on the following four areas:(1) verification of the aero-engine real-time model over the full flight envelope; (2) better balance between real-time performance and accuracy in simplified methods for the aero-thermodynamic component level models; (3) further improvement in the real-time performance for the identified nonlinear models over the full flight envelope; (4) improvement of hybrid on-board adaptive real-time models combining the advantages of both model-based and data-based on-board adaptive real-time modeling methods.
Highlights
Due to the harsh working environment of the aircraft engine, the aero-thermodynamic process is complex, and its characteristics can only be described by a complex multivariate time-varying model with strong coupling and nonlinearity
In SAE AIR4548 standard, a real-time engine model is defined as a transient performance computer program, whose engine outputs are generated at a rate commensurate with the response of the physical system it represents [1]
With the increasing demand for realtime models, the analog model shows the disadvantages of low precision, high cost, and difficulty in use. e digital model with low cost begins to enter the view of modeling researchers
Summary
Due to the harsh working environment of the aircraft engine, the aero-thermodynamic process is complex, and its characteristics can only be described by a complex multivariate time-varying model with strong coupling and nonlinearity. E nonlinear component-level model of the engine established by the analytical method has high precision, but the real-time performance is poor because of the complex calculation process It can only run offline at the ground state and needs to be simplified before the available real-time model can be obtained. In terms of improving the accuracy and real-time performance of the small deviation state space model, Mihaloew and Roth [24] and Daniele [25] consider that the traditional partial derivative method uses a positive perturbation for a given small disturbance amount, which will result in the problem that obtained state-space model may have a large dynamic error in the field below the steady state point. Yang et al [32] comes up with a nonaffine parameter-dependent LPV modeling method, and the polynomial-based
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
