Integration of magnetic bearings in the design of advanced gas turbine engines

Active magnetic bearings provide revolutionary advantages for gas turbine engine rotor support. These advantages include tremendously improved vibration and stability characteristics, reduced power loss, improved reliability, fault-tolerance, and greatly extended bearing service life. The marriage of these advantages with innovative structural network design and advanced materials utilization will permit major increases in thrust to weight performance and structural efficiency for future gas turbine engines. However, obtaining the maximum payoff requires two key ingredients. The first key ingredient is the use of modern magnetic bearing technologies such as innovative digital control techniques, high-density power electronics, high-density magnetic actuators, fault-tolerant system architecture, and electronic (sensorless) position estimation. This paper describes these technologies. The second key ingredient is to go beyond the simple replacement of rolling element bearings with magnetic bearings by incorporating magnetic bearings as an integral part of the overall engine design. This is analogous to the proper approach to designing with composites, whereby the designer tailors the geometry and load carrying function of the structural system or component for the composite instead of simply substituting composites in a design originally intended for metal material. This paper describes methodologies for the design integration of magnetic bearings in gas turbine engines

NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology

Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin. In this work, we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. NOSA allows to efficiently identify oscillatory frequencies in electrical patterns, quantify neuronal response parameters and moreover provides an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions

The Butterfly Fauna Of The Italian Maritime Alps:Results Of The «Edit» Project

Bonelli, Simona, Barbero, Francesca, Casacci, Luca Pietro, Cerrato, Cristiana, Balletto, Emilio (2015): The butterfly fauna of the Italian Maritime Alps: results of the EDIT project. Zoosystema 37 (1): 139-167, DOI: 10.5252/z2015n1a6, URL: http://dx.doi.org/10.5252/z2015n1a

Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here

Synthesis of nonlinear multiport resistors: a PWL approach

This paper presents a method for the approximate synthesis of nonlinear multiport resistors. According to some fundamental circuit theory results, the general problem of synthesizing a multiport resistor with given constitutive equations corresponds to that of the synthesis of nonlinear controlled sources. Following this idea, in this paper, we focus on the design of nonlinear controlled sources using a piecewise-linear (PWL) approach. The constitutive equations are first approximated by resorting to canonical expressions for continuous PWL functions, and then implemented using a set of elementary building blocks. The proposed method is applied to the synthesis of the nonlinear resistive part of an equivalent circuit of the Hodgkin\u2013Huxley nerve membrane model

Synthesis of multiport resistors with piecewise-linear characteristics: a mixed-signal architecture

Non-linear multiport resistors are the main ingredients in the synthesis of non-linear circuits. Recently, a particular PWL representation has been proposed as a generic design platform (IEEE Trans. Circuits Syst.-I 2002; 49:1138\u20131149). In this paper, we present a mixed-signal circuit architecture, based on standard modules, that allows the electronic integration of non-linear multiport resistors using the mentioned PWL structure. The proposed architecture is fully programmable so that the unit can implement any user-defined non-linearity. Moreover, it is modular: an increment in the number of input variables can be accommodated through the addition of an equal number of input modules