Measuring the matter distribution within z = 0.2 cluster lenses with XMM-Newton

We present an analysis of seven clusters observed by XMM-Newton as part of our survey of 17 most X-ray luminous clusters of galaxies at z  0.2 selected for a comprehensive and unbiased study of the mass distribution in massive clusters. Using the public software FTOOLS and XMMSAS we have set up an automated pipeline to reduce the EPIC MOS and pn spectro-imaging data, optimized for extended sources analysis. We also developped a code to perform intensive spectral and imaging analysis particularly focussing on proper background estimate and removal. XMM-Newton deep spectro-imaging of these clusters allowed us to fit a standard β-model to their gas emission profiles as well as a standard MEKAL emission model to their extracted spectra, and test their inferred characteristics against already calibrated relations.     

一种新颖组合变换器拓扑研究与应用

基于电源模块并联和串联的思想,提出了一种新颖双管正激组合变换器拓扑,克服了双管正激变换器副边二极管电压应力过高,输出电压和电流脉动大,只能应用于输出中低压场合的缺点,具有副边二极管电压应力低,可靠性高,原边开关管电流应力低,输出电压和电流脉动小,磁芯元件体积小等优点,本文对该新颖组合变换器进行分析,仿真和实验,表明适合应用于输入中,高电压,输出高压,大电流场合,并成功应用于某型飞机6KVA逆变器前级变换器,各项技术指标均符合设计要求。     

Comparison of resonant topologies in high-voltage DC applications

Because of their tolerance of transformer nonidealities, resonant converters are considered to be well-suited to high-voltage applications. The series and parallel resonant topologies, as well as a newly discovered hybrid resonant topology are compared for high-voltage applications. Design criteria which incorporate transformer nonidealities are developed and used in the construction of high voltage prototypes for each topology. It is found that the parallel topology leads to the lowest peak switch current and the most ideal behavior     

Monitoring the thermal condition of permanent-magnet synchronousmotors

This paper describes a novel approach to monitoring the condition of small permanent-magnet synchronous motors (PMSM) operating under thermal stress. The approach begins with the estimation of temperature-dependent motor parameters from measurements of line voltages and currents. The parameters are then used to derive estimates of motor temperatures. Next, the electrically estimated temperatures are combined with a surface measurement of motor temperature and a dynamic thermal model of the motor to yield an observer that is a Kalman filter. The temperatures estimated by the observer are used for failure prevention. Finally, by modifying the observer, it is tuned to use the geometric properties of its innovation for failure detection. The innovation, that is, the difference between the thermally and electrically estimated temperatures, is monitored and compared against appropriate thresholds to detect failures. Failure detection is demonstrated experimentally, and shown to be capable of distinguishing the conditions of normal operation, and operation with obstructed cooling     

Solid nanomembrane manufacture using ion-plasma action

A process of manufacture of porous membranes made of aluminum foil (Al 99.99%) by the method of electrochemical anodic oxidation followed by a continuous barrier layer removal with ion-plasma irradiation is described. The membranes having a 8 mm diameter made from AL2O3 with a thickness from 2 to 5 microns thick with a regular channel arrangement are obtained. A possibility to change the channel diameter in the range of 20?C100 nm by adjusting voltage and temperature during the oxidation is shown. We present the experimental dependencies of aluminum and aluminum oxide sputtering coefficients on the energy and incidence angle of xenon ions within the energy range of 100??400 eV.     

Determination of Gas-Generator Fuel Capacity for Hybrid Aerial Underwater Vehicles with Airfoil Cavity-Forming System

We determine the behavior of onboard gas-generator flowrate for generating the cavitation flow around a hybrid aerial underwater vehicle by using an annular wing on the underwater trajectory section.     

A new global version of M(3000)F2 prediction model based on artificial neural networks

A new version of global empirical model for the ionospheric propagation factor, M(3000)F2 prediction is presented. Artificial neural network (ANN) technique was employed by considering the relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. This new version is an update to the previous neural network based M(3000)F2 global model developed by Oyeyemi et al. (2007), and aims to address the inadequacy of the International Reference Ionosphere (IRI) M(3000)F2 model (the International Radio Consultative Committee (CCIR) M(3000)F2 model). The M(3000)F2 has been found to be relatively inaccurate in representing the diurnal structure of the low latitude region and the equatorial ionosphere. In particular, the existing hmF2 IRI model is unable to reproduce the sharp post-sunset drop in M(3000)F2 values, which correspond to a sharp post-sunset peak in the peak height of the F2 layer, hmF2. Data from 80 ionospheric stations globally, including a good number of stations in the low latitude region were considered for this work. M(3000)F2 hourly values from 1987 to 2008, spanning all periods of low and high solar activity were used for model development and verification process. The ability of the new model to predict the M(3000)F2 parameter especially in the low latitude and equatorial regions, which is known to be problematic for the existing IRI model is demonstrated.