Simulation of Positive Corona Discharge in Atmospheric-pressure Air by Plasma Hydrodynamic Model

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A standardized approach to identify nonlinearity in fighter jet noise during ground run-ups

High-intensity military sources (explosions, gunfire, fighter jet noise. . . ) fall out of the scope of linearity-based commercial software and international standards. On top of its intrinsic complexity (high temperature supersonic jet, shocks, turbulent eddies, etc), fighter jet engine noise bears additional challenges: turbulent surrounding flow, airframe interference, directionality and tonal components typical of rotating equipment. The absence of a suitable metric to determine the boundary of the nonlinear propagation region can lead to costly mispredictions in the exposure levels for noise abatement procedures and the personnel's safety. The derivative skewness of the time domain signal used as an indicator of spectral reshaping proved effective in setting a threshold to the generation of acoustic nonlinearity for seven firearms. Its application to aircraft noise is here discussed, in order to verify the claim of persistence of nonlinear behaviour at large distances and set the foundations for a common framework contemplating the peculiarities of each military noise source. The experimental data was collected during a series of static ground run-ups of an F-16 military jet aircraft. The results from five engine regimes and two propagation paths are compared with the findings from firearms, existing literature and the solution of a nonlinear acoustic propagation solver

Development of a Probabilistic Tool Using Monte Carlo Simulation and Smart Meters Measurements for the Long Term Analysis of Low Voltage Distribution Grids with Photovoltaic Generation

peer reviewedConnections of distributed generation (DG) units based on the use of photovoltaic cells are highly increasing in low voltage distribution grids. In that way, one of the major problems met by the Distribution System Operators (DSO) comes from overvoltage in the neighbourhood of dispersed units. Consequently, it is important for them to have an analysis tool that computes statistical voltage profiles and allows to assess maximal penetration rates of photovoltaic generation (PV) on low voltage (LV) distribution feeders. In previous studies, it has been shown that such a tool could be obtained by using a Probabilistic Load Flow based on analytical techniques or Monte Carlo methods. In this paper, given its simplicity of implementation, a pseudo-chronological Monte Carlo simulation is used and the statistical behaviour of prosumers (consumers with PV units) is directly based on smart meters measurements. Thanks to this tool, and using collected measurements from smart meters that are expected to be massively deployed in the future, it will be possible for the DSO to directly assess voltage profiles at all the nodes of the LV grid. Moreover, in the context of alleviating the impact of photovoltaic generation on the recorded voltage profiles, smart meters data will also be used in order to not only quantify the influence of reactive power flows on the collected results but also to estimate the auto-consumption potential over some critical nodes of the grid