To enable real-time performance monitoring and degradation assessment of aircraft engines, a digital-twin-based methodology for real-time monitoring and evaluation of engine performance degradation is proposed. A performance digital twin architecture was designed and implemented by integrating Long Short-Term Memory (LSTM) recurrent neural networks with the engine's physical structural configuration. Baseline models of the performance digital twin were established using flight parameter data from the initial operational flights of an engine. The model demonstrates high-fidelity simulation capabilities for replicating the engine's performance across diverse flight conditions. By feeding real-time operational parameters and flight state data into the baseline model, the real-time performance metrics of a pristine (non-degraded) engine under current operating conditions are simulated. Comparative analysis between simulated outputs and actual sensor measurements enables quantitative assessment of the engine's instantaneous performance degradation. A case study involving 185 flight cycles validated the framework: Baseline models constructed from the first three flights achieved mean absolute relative errors below 0.98%, 0.94%, and 1.89% for rotational speed, pressure, and temperature predictions, respectively, with single-point inference time under 0.14 milliseconds, confirming the reliability of real-time digital twinning. The degradation assessment results of this method align well with traditional methods, demonstrating significant feasibility and advantages.

To address the complex nonlinear flow mechanisms of high-speed plasma in magnetic fields, the conventional‌ Navier-Stokes (N-S) equations based on continuum theory fail to provide accurate predictions. Therefore, a novel framework was developed by‌ coupling the nonlinear coupling constitutive relations (NCCR) with Maxwell's electromagnetic field governing equations, augmented with the Park's TTv two-temperature model and the Park 11-component chemical reaction model. This integration established‌ a numerical simulation method and code for hypersonic magnetohydrodynamic (MHD) thermochemical non-equilibrium under‌ low magnetic Reynolds numbers conditions‌. Numerical simulations of high-speed plasma flow past a spherical body were conducted to investigate the influence mechanism of a dipole magnetic field on high-speed MHD control, with particular focus on‌ the effects of magnetic field existence‌ and its induction strength on plasma flow field structures. The results show that the presence of a magnetic field significantly alters high-speed plasma flow structures, with stronger magnetic fields inducing greater Lorentz forces on charged particles and consequently increasing bow shock detachment distances (e.g., a 452.38% increment observed at B₀ = 3.0 T). Stagnation point heat flux variations exhibit dependence on multiple factors including magnetic induction strength, inflow altitude, and Mach number, showing a notable 45.55% reduction at H = 80 km after magnetic field introduction. Furthermore, the magnetic field induces prominent thermochemical non-equilibrium effects, primarily enhancing N₂ dissociation in post-shock regions while modifying recombination reactions near walls, yet exerting minimal influence on O₂ dissociation dynamics.