A new method for finding electromagnetic emitter location

The position of a source of radiation is often obtained from bearing data, taken over an interval of time, and combining it with navigation data. A new method using total least squares (TLS) has been suggested for the accurate estimation of an emitter location when bearing observation errors are random. Further, an iterative two-stage approach involving TLS and Kalman filtering is developed for accurate estimation of the emitter location when bearing observation errors are an algebraic sum of random and systematic errors. The elegance and efficacy of the proposed methods are illustrated through digital computer simulated examples     

Results from the ESA SREM monitors and comparison with existing radiation belt models

The Standard Radiation Environment Monitor (SREM) is a simple particle detector developed for wide application on ESA satellites. It measures high-energy protons and electrons of the space environment with a 20° angular resolution and limited spectral information. Of the ten SREMs that have been manufactured, four have so far flown. The first model on STRV-1c functioned well until an early spacecraft failure. The other three are on-board, the ESA spacecraft INTEGRAL, ROSETTA and PROBA-1. Another model is flying on GIOVE-B, launched in April 2008 with three L-2 science missions to follow: both Herschel and Planck in 2008, and GAIA in 2011). The diverse orbits of these spacecraft and the common calibration of the monitors provides a unique dataset covering a wide range of B-L* space, providing a direct comparison of the radiation levels in the belts at different locations, and the effects of geomagnetic shielding. Data from the PROBA/SREM and INTEGRAL/IREM are compared with existing radiation belt models.     

中心进气转静系转盘风阻扭矩数值模拟

采用计算流体力学(CFD)商业软件CFX从流动参数及几何结构两个方面对中心进气转静系盘腔内转盘风阻扭矩特性进行了数值模拟研究,同时搭建一个转盘风阻扭矩实验台测量自由盘风阻扭矩.中心进气转静系转盘风阻扭矩与经典经验公式进行了对比,结果表明:利用数值模拟方法,得到的无量纲扭矩系数与经典经验关系式计算得到的数据总体相差小于10%,利用湍流参数来表征转盘转速以及叠加流流量,对于分析转盘风阻扭矩非常便利.利用湍流参数定量分析时需注意叠加流以及转速的量级同样影响着风阻扭矩的数值.几何结构对于风阻扭矩也有影响,但相比流动参数影响较小.      

Critical wake vortex encounter scenarios

A simulation-based method was developed to investigate the severity of Wake Vortex Encounters (WVEs). This paper describes an important part of this method: the determination of worst-case WVE conditions, which is referred to as Worst-Case Search (WCS). The WCS results permit to reduce time and costs of WVE severity related piloted simulator tests and allow the comparison of the hazard that vortices of different generator aircraft exert on a follower aircraft.The WCS is based on a high fidelity, offline simulation of the follower aircraft that includes the interacting wake vortex, a hazard criterion that rates the severity of each WVE, and a pilot model. It can be formulated as an optimisation problem that is solved with the optimisation tool MOPS (Multi Objective Parameter Synthesis). MOPS varies the encounter geometry until the simulation yields maximum values of the WVE hazard criterion.Worst-case encounter conditions for different parameters were investigated and sensitivity studies performed. The influence of the height above ground, the core radius, and the models for the wake vortex velocity profiles on the severity of a WVE was examined. To show the capability of the method, a comparison of the severity of a WVE behind two different heavy transport aircraft for a 20 t aircraft was made. The WCS method demonstrated its applicability and delivered the worst-case encounter geometry.

Zusammenfassung

Zur Untersuchung der Heftigkeit von Wirbelschleppen wurde eine simulationsbasierte Methode entwickelt. Dieser Beitrag beschreibt einen wichtigen Bestandteil dieser Methode: die Bestimmung der ungünstigsten (worst case) Einflugbedingungen in die Wirbelschleppe. Die Kenntnis dieser Bedingungen gestattet eine Zeit- und Kostenreduzierung bei wirbelschleppenbezogenen Simulatorversuchen mit Piloten und ermöglicht gleichzeitig einen Vergleich der vom Wirbel ausgehgehenden Gefährdung für verschiedene Kombinationen voraus- und einfliegender Flugzeuge.Die Worst-Case-Bedingungen werden mit einer Offline-Simulation des Folgeflugzeugs bestimmt, die Modelle für die Wechselwirkung mit der Wirbelschleppe und für das Pilotenverhalten sowie Kriterien für die Gefährdung durch den Wirbel umfasst. Für die Steuerung der Simulation und die Worst-Case-Suche wird die Optimierungssoftware MOPS (Multi Objective Parameter Synthesis) verwendet. MOPS variiert die Geometrie des Wirbelschleppendurchflugs bis die Simulation die maximalen Kriterienwerte für die Gefährdung durch den Wirbel liefert.Die Worst-Case-Bedingungen wurden für unterschiedliche Parameter untersucht. Der Einfluss der Höhe über Grund, des Kernradius' und der unterschiedlichen Modelle für das Wirbelgeschwindigkeitsprofil auf die von den Wirbelschleppen ausgehenden Gefährdung wurde dabei ermittelt. Um die Leistungsfähigkeit der Methode zu demonstrieren, wurde die Gefährdung von Wirbelschleppen zweier generierender Flugzeuge (heavy class) auf ein nachfolgendes 20 t Transportflugzeug verglichen. Die Anwendbarkeit der Methode wurde bestätigt und die ungünstigsten Einflugbedingungen berechnet.     

Prognostic Health Management of Aircraft Power Generators

In this paper, prognostic tools are developed to detect the onset of electrical failures in an aircraft power generator, and to predict the generator's remaining useful life (RUL). Focus is on the rotor circuit since failure mode, effects, and criticality analysis (FMECA) studies indicate that it is a high priority candidate for condition monitoring. A signature feature is developed and tested by seeded fault experiments to verify that the initial stages of rotor faults are observable under diverse generator load conditions. A tracking filter is used to assess the damage state and predict generator RUL. This information helps to avoid unexpected failures while reducing the overall life-cycle cost of the system.     

ExoMars Trace Gas Orbiter (TGO) Science Ground Segment (SGS)

The ExoMars Trace Gas Orbiter (TGO) Science Ground Segment (SGS), comprised of payload Instrument Team, ESA and Russian operational centres, is responsible for planning the science operations of the TGO mission and for the generation and archiving of the scientific data products to levels meeting the scientific aims and criteria specified by the ESA Project Scientist as advised by the Science Working Team (SWT). The ExoMars SGS builds extensively upon tools and experience acquired through earlier ESA planetary missions like Mars and Venus Express, and Rosetta, but also is breaking ground in various respects toward the science operations of future missions like BepiColombo or JUICE. A productive interaction with the Russian partners in the mission facilitates broad and effective collaboration. This paper describes the global organisation and operation of the SGS, with reference to its principal systems, interfaces and operational processes.     

The Pre-CME Sun

The coronal mass ejection (CME) phenomenon occurs in closed magnetic field regions on the Sun such as active regions, filament regions, transequatorial interconnection regions, and complexes involving a combination of these. This chapter describes the current knowledge on these closed field structures and how they lead to CMEs. After describing the specific magnetic structures observed in the CME source region, we compare the substructures of CMEs to what is observed before eruption. Evolution of the closed magnetic structures in response to various photospheric motions over different time scales (convection, differential rotation, meridional circulation) somehow leads to the eruption. We describe this pre-eruption evolution and attempt to link them to the observed features of CMEs. Small-scale energetic signatures in the form of electron acceleration (signified by nonthermal radio bursts at metric wavelengths) and plasma heating (observed as compact soft X-ray brightening) may be indicative of impending CMEs. We survey these pre-eruptive energy releases using observations taken before and during the eruption of several CMEs. Finally, we discuss how the observations can be converted into useful inputs to numerical models that can describe the CME initiation.