A simplified (7 species and 9 processes) plasma kinetic model is proposed to investigate the mechanism of the plasma aerodynamic actuation driven by nanosecond-pulsed dielectric barrier discharge (NS-DBD). The governing equations include conservation equations for each species, the Poisson equation for the electric potential, and Navier-Stokes equations for the gas dynamic flow. Numerical simulations of plasma discharge and flow actuation on NS-DBD plasma actuators have been carried out. Key discharge characteristics and the responses of the quiescent air were reproduced and compared to those obtained in experiments and numerical simulations. Results demonstrate that the reduced plasma kinetic model is able to capture the dominant species and reactions to predict the actuation in complicated hydrodynamics. For the one-dimensional planar and two-dimensional symmetric NS-DBD, the forming of the sheath collapse is mainly due to the charge accumulation and secondary emission from the grounded electrode. Rapid species number density rise and electric field drop occur at the edge of the plasma sheath, where the space charge density gradient peaks. For the aerodynamic actuation with typical asymmetry electrodes, discharge characteristics have a core area on the right edge of the upper electrode, where the value can be much higher. The formation and propagation of the compression waves generated through rapid heating have also been performed and compared to those measured in a recent experiment. Energy release leads to gas expansion and forms a cylindrical shock wave, centering at the upper electrode tip with low gas acceleration. For the present single pulsed 12 kV case, the mean temperature of gas heating reaches about 575 K at 1 μs and decreases to about 460 K at 10 μs.
In this paper, filter clogging is used as an aerospace integrated vehicle health management case study to demonstrate the proposed prognostic approach. The focus of this paper is on a scalable data-driven degradation model and how it can improve the remaining useful life prediction performance in condition monitoring of a filter component. Instead of overall fitting of the data, a degradation pattern (a parameterized Takagi-Sugeno fuzzy model) is learned from experimental data collected under a range of operating conditions in the proposed approach. The parameter allows the model to scale to fit different degradation profiles, and hence a more accurate model. In realtime condition monitoring, the degradation and model parameter are simultaneously estimated online based on noisy measurement updates using a particle filter. The estimation results show close tracking of the degradation state and good convergence of the model parameter to its real value. The remaining useful life prediction results show low prediction errors, regardless of operating conditions, which contrasts to a conventional data-driven model (a nonparameterized Takagi-Sugeno fuzzy model) where prediction errors increase as operating conditions deviate from the nominal condition. I. IntroductionI NTEGRATED vehicle health management (IVHM) is a major component in a new future aerospace asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled-based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimizing downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing, which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics, and providing recommended maintenance actions.The fuel system is a critical subsystem of the mobile platform asset type, like aircraft, where its functional failures can lead to an aircraft returning to ground or diverting and a remote possibility of engine shutdown. In an economic term, this means a significant cost to an operator. Several IVHM-related examples of fuel systems are reported in the literature: notably, those based on classification and estimation techniques [1], the applied support vector machine, k-nearest neighbors, and the Bayes classifier in fault detection and isolation (FDI) of an experimental electronic control fuel system. In [2,3], a rule-based expert system is developed for FDI of an aircraft fuel system. The Kalman filter is different, as it is an online parameter estimation technique and has been applied to the FDI of an aircraft fuel system [4] and a diesel engine [5]. The previously mentioned literature, however, only addresses FDI problems. It does not predict deterioration of a system, which is key to predictive maintenance.Until now, [6] was the only study related to prognostics of the fuel system; however, the results show s...
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