The effect of an ultra-thin molybdenum trioxide (MoO 3 ) layer thickness inserted between the indium tin oxide (ITO) substrate and copper phthalocyanine (CuPc) layer on the performance of organic photovoltaic devices (OPVs) was studied. Experimental results demonstrate that the short-circuit current density (J sc ) was decreased slightly with the increase of MoO 3 thickness; meanwhile, the fill factor (FF) was increased from 53.5 to 57.7%, respectively, leading to the improved power conversion efficiency with the optimal thickness of MoO 3 (1 nm). The experimental results also reveal that the Ohmic contact is formed with the deposition of MoO 3 . Further, the effect of the MoO 3 layer was checked from the variation of the performance of OPVs under different illumination intensities. It was found that the MoO 3 layer could effectively prevent exciton quenching at the ITO anode side, resulting in the small variation of the FF for the devices with the MoO 3 layer compared to the devices without the MoO 3 layer under high illumination intensity.

A real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve the optimization problem, a FSQP (feasible sequential quadratic programming) algorithm is utilized. The thermal mechanical fatigue is taken into account during the optimization process. Furthermore, to describe the engine life decaying, a thermal mechanical fatigue model of engine acceleration process is established. The optimization objective function not only contains the sub-item which can get fast response of the engine, but also concludes the sub-item of the total mechanical strain range which has positive relationship to engine fatigue life. Finally, the simulations of the conventional optimization control which just consider engine acceleration performance or the proposed optimization method have been conducted. The simulations demonstrate that the time of the two control methods from idle to 99.5 % of the maximum power are equal. However, the engine life using the proposed optimization method could be surprisingly increased by 36.17 % compared with that using conventional optimization control.

An integrated model including inlet, engine and nozzle with their internal and external characteristics was built to simulate the propulsion installed performance. With the integrated model, a new performance seeking control scheme under supersonic state is firstly proposed, taking inlet ramp angle as optimizing variable, which is equally important to fuel flow rate, nozzle throat area, guided vane angle of fan and compressor. Specially, engine installed thrust replaces its total thrust as one crucial factor for performance seeking control. Installed performances under supersonic state are significantly improved with the new scheme, as installed thrust increases of up to 4.9% in the maximum thrust mode, installed specific fuel consumption improvements of up to 3.8% in the minimum fuel consumption mode, and turbine temperature decreases of up to 0.6% in the minimum turbine temperature mode. The simulation results also indicates that, the performance seeking control scheme proposed shows superiority in restraining of the increasing of rotational speed and turbine temperature in performance seeking control.

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A novel nonlinear model predictive control method for aero-engine direct thrust control is proposed to improve engine response ability and reduce computational complexity of nonlinear model predictive control. The control objective of the proposed method is the thrust directly instead of the measurable parameters. The linearized model based on online sliding window deep neural network is proposed as predictive model. The online sliding window deep neural network has strong fitting capacity for nonlinear object and adopted to fitting the transient process of engine. The back propagation is adopted to obtain linearized model of online sliding window deep neural network, which greatly reduce the calculated amount. The comparison simulations of the popular nonlinear model predictive control based on extended Kalman filter and the proposed one are carried out. The simulation results show that compared with the popular nonlinear model predictive control, the proposed nonlinear model predictive control not only has the better response ability but also has reduced computational complexity greatly, nearly reduce computation time more than 35 ms.