Junkui Mao

To address load increase step disturbances caused by airborne equipment switching during aviation mission profiles and the control challenges arising from multi-time-scale thermal, electrical, and chemical dynamics, this paper proposes a dynamic coordinated control strategy for an SOFC-MGT hybrid power system operating under variable aviation conditions, validated via a custom-built kilowatt-scale hardware-in-the-loop (HIL) simulation. First, a high-fidelity dynamic model encompassing the SOFC stack, aviation kerosene reformer, micro gas turbine, and heat exchanger network is established. Then, a multi-loop coordinated control framework is developed to ensure power tracking, thermal boundary constraints, and species safety during abrupt load transients, effectively reconciling the inherent time-scale disparity between SOFC thermal inertia and gas turbine aerodynamic response. An HIL experimental platform centered on a host monitoring computer and a PXI real-time computer is constructed, achieving multi-rate interaction between the 50ms model solver and 100-ms external physical component flow. Closed-loop experimental results under kilowatt-scale load step increase (from 800 W to 1600 W) and step decrease (from 1600 W to 800 W) conditions demonstrate that the system output power smoothly converges to target setpoints within 200 s without overshoot. Critical thermal boundaries are strictly maintained: cathode inlet temperature stabilizes at 923 K (±1.5 K, control error ≤±0.16%), and turbine inlet temperature rapidly converges to 1273 K with only 15 K overshoot. Furthermore, the steady-state system efficiency reaches 55%. These findings provide a vital engineering reference for aviation hybrid power systems under complex variable-load conditions.

Solid oxide fuel cells (SOFCs) are promising energy-conversion devices for future aviation power systems due to their high efficiency and fuel flexibility. However, health-state estimation under aviation dynamic environments remains challenging due to coupled variations in flight altitude, temperature, and pressure. To address this issue, an SOFC health-state estimation framework is proposed for aviation dynamic conditions. A one-dimensional dynamic SOFC model incorporating altitude, temperature, and pressure is first developed to represent aviation operating environments. Based on this model, multi-scale degradation mechanisms, including electrode delamination, fuel leakage, and Ni particle coarsening, are introduced to simulate degradation evolution under dynamic operating conditions. To enhance prediction accuracy and reduce input redundancy, SHAP-based feature selection is combined with a long short-term memory (LSTM) network for output-voltage prediction. Furthermore, a voltage-deviation-based health indicator (HI) is constructed to enable consistent health-state quantification across aviation and ground conditions. Results show that the proposed SHAP-LSTM model reduces RMSE and MAE by 26.2% and 33.5%, respectively, compared with a conventional LSTM. Under aviation dynamic conditions, HI degradation induced by electrode delamination and fuel leakage is 170% and 85% higher than that under ground conditions, respectively, while SHAP analysis identifies inlet temperature and pressure as key factors associated with degradation differences. The proposed framework provides an effective approach for health-state monitoring and degradation assessment of SOFC systems under dynamic aviation environments.

Accurate prediction of discharge coefficients (C_d) in rotating orifices is essential for the design of aero-engine internal air systems, yet existing correlations usually treat axial and radial orifices separately and do not fully represent intermediate wall inclination angles. In this study, steady-state RANS simulations in a rotating reference frame, supported by validation against published data and by rotating orifice experiments, are used to investigate the combined effects of wall inclination angle α and length-to-diameter ratio L/d on C_d. The numerical results show that, under typical conditions (N = 3000 rpm, Π = 1.03, L/d = 1.5), C_d increases from 0.301 to 0.340 as α increases from π/2 to π, corresponding to a 12.96% increase. Under low rotational speeds and high pressure ratios, the Coriolis force reduces the relative tangential velocity and the incidence angle, thereby increasing C_d with α; however, at high rotational speeds and low pressure ratios, the centrifugal resistance to radial inflow becomes dominant, and at N = 7000 rpm, the C_d for the α = π orifice is 38.96% lower than that for the α = π/2 orifice. Increasing L/d promotes flow redevelopment and amplifies the Coriolis-force effect, leading to a larger C_d increase for orifices with larger α. Based on these mechanisms, a generalized incidence-angle formulation incorporating Coriolis and centrifugal effects is developed, and a C_d prediction model applicable to π/2 ≤ α ≤ π and different L/d values is proposed. Experimental validation shows that the maximum prediction error is reduced to 2.37%, demonstrating the accuracy of the proposed model for rotating inclined orifices.

Under the current aviation fuel framework, solid oxide fuel cell-gas turbine (SOFC-GT) hybrid power systems have emerged as a promising solution for long-endurance unmanned aerial vehicles (UAVs) owing to their high efficiency and low emissions. However, the transient performance of kerosene-fueled SOFC-GT systems over the full flight envelope remains insufficiently understood. In this study, a system level multi-scale dynamic analysis framework is developed for a kerosene-fueled SOFC-GT hybrid aviation power system. A decoupled compressor-turbine architecture with multi-loop recirculation is proposed, together with a six-loop safety constrained coordinated control strategy and a rule-based battery energy management method, to evaluate power following, thermal safety, and mission-level energy coordination under multi-scenario UAV operation. The results show that the proposed strategy enables autonomous thermal management and self-sustained steam reforming while maintaining stable power following under varying flight conditions. At 9000 m and Ma = 0.5, the system achieves a net output power of 185 kW, an efficiency of 55.63%, and a specific fuel consumption of 0.1501 kg/kWh. During the climb-to-cruise and cruise-to-descent transients, the lithium battery compensates for the temporary power mismatch induced by environmental variations and system thermal inertia. Based on the integrated mission scenario covering takeoff, climb, cruise, and descent, the battery charge/discharge demand is quantified, and a battery capacity of 33 kWh is determined. Throughout the mission, the battery SOC remains within 0.4-0.8, and the operating parameters of other system components are maintained within respective safety constraints. For a 3-ton UAV, the proposed hybrid system increases the endurance by 46.0% and reduces the total CO2 emissions by 29.7% compared with a conventional power system under a consistent comparison basis. This work provides a more realistic feasibility assessment of the application of kerosene-fueled SOFC-GT systems as UAV power sources based on transient characteristics.

This paper presents a collaborative optimization design methodology aimed at improving heat dissipation efficiency through the modulation of microstructural variations. The approach addresses the thermal protection requirements of high-temperature components, such as ceramic matrix composite turbine blades, which are subjected to complex and elevated thermal loads. Through the integration of numerical simulation and experimental validation, a bidirectional mapping model linking carbon nanotube (CNT) content with the macroscopic anisotropic thermal conductivity of the material was developed. Furthermore, a thermal conduction analysis and optimization framework for Ceramic Matrix Composite (CMC) high-temperature components under non-uniform thermal loads was established. This study expands the adjustable range of the material’s thermal conductivity by allowing flexible modulation of carbon nanotube content. The results demonstrate that this methodology effectively enhances the heat dissipation capacity of CMC materials in extreme thermal environments: the maximum surface temperature of the optimized flat plate is reduced by 8.96%, the peak temperature gradient is lowered by 46.64%, and the maximum thermal stress is decreased by 38.17%. This research provides new insights into the comprehensive integration of thermal dissipation requirements for CMC hot components.

Accurate prediction of flow and temperature fields within aero engine turbine passages are vital for optimizing engine performance. However, getting complete high-resolution datasets is challenging because of the limits of experimental measurements and the high cost of numerical simulations, such as computational fluid dynamics (CFD). Experimental methods often lead to incomplete data, while high-precision CFD simulations are costly in computation. Data assimilation methods that mix experimental and CFD simulation data can address missing data issues but lose accuracy when significant data proportions are absent. This study introduces a multiple fusion data assimilation method based on proper orthogonal decomposition (POD) to overcome these problems. The approach includes three key advancements: (1) an enhanced mode filtering strategy for improved reconstruction accuracy, (2) embedding physical constraints into the fusion process, and (3) a multistep reconstruction framework that iteratively restores missing data. The method was validated on turbine passage velocity fields and blade surface temperature fields. For the velocity field with 84% missing data, it achieved 95% accuracy over 81.9% of the rebuilt area, surpassing classic POD-based methods by 12.2%. For the temperature field with 91% missing data, it attained 97% accuracy over 89.42% of the rebuilt area, representing a 28.03% improvement over existing methods.

Addressing the power matching problem of the solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid power system for aviation, a multi-objective optimization approach based on neural networks was proposed. Numerical simulations were used to analyze the impact of different power distributions between the SOFC and GT on the system performance (including total system weight, thermal efficiency, electrical efficiency, fuel consumption rate, and CO₂ emission rate). The BP neural network surrogate model was employed to predict the performance of various power matching schemes, and a multi-objective genetic algorithm combined with the TOPSIS decision-making method was used to optimize the power matching solution. The optimal solution was found with the SOFC output power of 133.092kW and the GT output power of 66.908kW. The optimization results show that the total system weight is 117.124 kg, the fuel consumption rate is 0.196 kg/s, and the thermal efficiency is 73.21%. Compared to a solution with equal power outputs from the SOFC and GT, the optimized solution reduces total system weight by 27.7%. This study provides a new method for optimizing power matching in aviation hybrid power systems.