With the rapid growth of air travel, reducing carbon emissions in aviation is imperative. Electric aircraft play a key role in achieving sustainable aviation, especially for large civil aircraft, where reducing emissions, improving the fuel efficiency, and enabling flexible power regulation are essential. This study proposes a dual-shaft, separated-exhaust fuel cell hybrid aircraft propulsion system (HAPS), using a solid oxide fuel cell (SOFC) to replace the conventional turbine-driven compressor. The independent speed control of the high- and low-pressure spools is realized via a power distribution system. A thermodynamic model is developed, and performance evaluations, including parametric, exergy, and sensitivity analyses, are conducted. At the design point, the system delivers 36.304 kN thrust, 16.775 g/(kN·s) specific fuel consumption, 15.931 MW SOFC power, and 54.759% SOFC efficiency. The exergy analysis highlights the optimization of components like the heat exchanger and fan to reduce energy losses. The sensitivity analysis reveals that the spool speeds and fuel utilization significantly impact the performance. The findings provide valuable insights into optimizing control strategies and offer a novel, efficient, and low-carbon power solution for aviation, supporting the industry’s transition towards sustainability.

With the rapid development of artificial intelligence technology, data-driven methods show great potential in the simulation and fault diagnosis of complex industrial systems. However, the application of traditional intelligent methods in aeroengine health management still faces many challenges, including the limited number of initial fault samples and the insufficient ability to capture gas path performance degradation. To solve these problems, this paper proposed a new dual cross-modal feature fusion module and a new model-agnostic meta-learning module to construct an intelligent simulation and diagnosis framework for engine gas path. This framework can effectively integrate physical model and sensor measurement data, and realize high-precision simulation under gas path performance degradation and new fault diagnosis under small sample condition. Through the example verification and comparative analysis, the results show that under the condition of performance degradation, the simulation error of gas path parameters is only 0.074%; in the case of limited initial fault samples, the fault diagnosis accuracy is as high as 99.4%, which is significantly better than the existing intelligent methods. The research shows that the proposed intelligent simulation and diagnosis framework can provide strong support for the aeroengine intelligent health management system.