Location of layered structures in the ionosphere and atmosphere by use of GPS occultation data

A method is introduced to locate the layered structures in the atmosphere and ionosphere based on simultaneous observations of radio wave intensity and phase variations in trans-ionospheric satellite-to-satellite links. The method determines location of a tangent point on the trans-ionospheric ray trajectory where gradient of refractivity is perpendicular to the ray trajectory and influence of a layered structure on radio wave parameters is maximal. An estimate of the location of a layer can be obtained from a combination of the phase and intensity variations. This new technique was applied to measurements provided during FORMOSAT-3 and CHAMP radio occultation (RO) missions. For the considered RO events the location of the inclined plasma layer in the lower ionosphere is found and the electron density distribution is retrieved. The method is checked by measuring the location of the tangent point on the ray trajectory in the neutral gas in the atmosphere. The results showed a fairly good agreement.     

Design of SIMO Excitation Controllers for Synchronous Generators

Stabilization of synchronous generator oscillations is investigatedby using a single-input multiple-output (SIMO) excitation controller.The feedback signals which are most effective for the damping ofelectromechanical oscillations are selected based on the participationfactors of various signals. In order to improve the dampings ofpoorly damped oscillation modes, the eigenvalues associated withthese modes are shifted to certain prespecified locations by adjustingthe parameters of the excitation controller. Time domain simulationresults are also presented to show the validity of the proposedmethod.     

Three-dimensional global simulation of multiple ICMEs’ interaction and propagation from the Sun to the heliosphere following the 25–28 October 2003 solar events

This study performs simulations of interplanetary coronal mass ejection (ICME) propagation in a realistic three-dimensional (3D) solar wind structure from the Sun to the Earth by using the newly developed hybrid code, HAFv.2+3DMHD. This model combines two simulation codes, Hakamada–Akasofu–Fry code version 2 (HAFv.2) and a fully 3D, time-dependent MHD simulation code. The solar wind structure is simulated out to 0.08 AU (18 Rs) from source surface maps using the HAFv.2 code. The outputs at 0.08 AU are then used to provide inputs for the lower boundary, at that location, of the 3D MHD code to calculate solar wind and its evolution to 1 AU and beyond. A dynamic disturbance, mimicking a particular flare’s energy output, is delivered to this non-uniform structure to model the evolution and interplanetary propagation of ICMEs (including their shocks). We then show the interaction between two ICMEs and the dynamic process during the overtaking of one shock by the other. The results show that both CMEs and heliosphere current sheet/plasma sheet were deformed by interacting with each other.     

Modeling of LEO orbital debris populations for ORDEM2008

The NASA Orbital Debris Engineering Model, ORDEM2000, is in the process of being updated to a new version: ORDEM2008. The data-driven ORDEM covers a spectrum of object size from 10 μm to greater than 1 m, and ranging from LEO (low Earth orbit) to GEO (geosynchronous orbit) altitude regimes. ORDEM2008 centimeter-sized populations are statistically derived from Haystack and HAX (the Haystack Auxiliary) radar data, while micron-sized populations are estimated from shuttle impact records. Each of the model populations consists of a large number of orbits with specified orbital elements, the number of objects on each orbit (with corresponding uncertainty), and the size, type, and material assignment for each object. This paper describes the general methodology and procedure commonly used in the statistical inference of the ORDEM2008 LEO debris populations. Major steps in the population derivations include data analysis, reference-population construction, definition of model parameters in terms of reference populations, linking model parameters with data, seeking best estimates for the model parameters, uncertainty analysis, and assessment of the outcomes. To demonstrate the population-derivation process and to validate the Bayesian statistical model applied in the population derivations throughout, this paper uses illustrative examples for the special cases of large-size (>1 m, >32 cm, and >10 cm) populations that are tracked by SSN (the Space Surveillance Network) and also monitored by Haystack and HAX radars operating in a staring mode.