With the combustor burning hydrogen, as well as the strongly coupled fuel and cooling system, the configuration of a hydrogen-fueled aero engine is more complex than that of a conventional aero engine. The performance, and especially the dynamic behavior of a hydrogen-fueled aero engine, need to be fully understood for engine system design and optimization. In this paper, both the transient modeling and performance analysis of hydrogen-fueled engines are presented. Firstly, the models specific to the hydrogen-fueled engine components and systems, including the hydrogen-fueled combustor, the steam injection system, a simplified model for a quick NOx emission assessment, and the heat exchangers, are developed and then integrated to a conventional engine models. The simulations with both Simulink and Speedgoat-based hardware in the loop system are carried out. Secondly, the performance analysis is performed for a typical turbofan engine configuration, CF6, and for the two hydrogen-fueled engine configurations, ENABLEH2 and HySIITE, which are currently under research and development by the European Union and Pratt & Whitney, respectively. At last, the simulation results demonstrate that the developed transient models can effectively reflect the characteristics of hydrogen burning, heat exchanging, and NOx emission for hydrogen-fueled engines. In most cases, the hydrogen-fueled engines show lower specific fuel consumption, lower turbine entry temperature, and less NOx emissions compared with conventional engines. For example, at max thrust state, the advanced hydrogen-fueled engine can reduce the parameters mentioned above by about 68.5%, 3.7%, and 12.7%, respectively (a mean value of two configurations).

Due to the substantial disparities in physical attributes between hydrogen fuel and conventional fuels, the development of an efficient controller presents a formidable challenge. In this context, this paper delves into the utilization of a model-based design (MBD) methodology for the purpose of conceiving and enhancing control systems for hydrogen-fueled turbojet engines. The investigation commences by adopting an established physical model of a hydrogen-fueled turbojet engine and subsequently validates its performance through rigorous simulation exercises. Consequently, this research undertakes a systematic deconstruction of the design process into discrete sub-phases, thus facilitating a seamless progression from system requirement analysis to system verification. This approach engenders a concurrent design and optimization of the control system. The ultimate confirmation of the controller’s efficacy and reliability is achieved through exhaustive simulations and Hardware-In-the-Loop testing. The research findings not only serve to augment design efficiency and mitigate design expenditures, but also propose avenues for further performance ameliorations in the realm of hydrogen-fueled turbojet engines. The control system accuracy of MBD is compared with the experimental results, and under high hydrogen fuel flow conditions, the errors reach an extremely low level of 0.1%. This affords a novel design paradigm within the domain of aero-engine control.

Gain-scheduled control is widely applied in the aerospace domain, yet the selection of design points for gain-scheduling controllers to ensure stability and robustness throughout the range of scheduling variables remains theoretically unguided, requiring laborious trial-and-error to ensure control performance. Therefore, this paper proposes a theoretical method for design point selection through analysis and optimization processes to meet system stability and robustness requirements. First, the method characterizes the gain-scheduled control system as a polytopic linear parameter varying (LPV) system, wherein the design points of the gain-scheduled control system correspond to the vertices of the polytopic LPV system. Second, the method utilizes linear matrix inequality (LMI) techniques to demonstrate the stability of a polytopic LPV system with a corresponding number of vertices, and by assessing the approximation degree between the polytopic LPV system and the gain-scheduled control system with an identical number of design points, it evaluates and ensures the stability of the latter, thereby establishing the minimal requirements for the number of design points. Finally, the method further refines the number of design points within the gain-scheduled control system to meet additional robustness and performance considerations. A case study on turbofan engine controls validates the proposed method. New design points, selected via stability and robustness analysis, enhance the system's steady-state phase margin and robustness against model uncertainties. Moreover, compared to a v-gap metric-based method, the proposed method exhibits similar performance in terms of stability, robustness, and tracking control; however, it requires fewer design points, resulting in less conservatism.