The composition of meteorites and the surface of asteroids suggest that planetesimals of the early solar system have undergone partial melting and differentiation. The sepa- ration of the denser metal (Fe-FeS alloy) from the lighter silicate is the most important differentiation process. The melting is mainly induced by the heat produced through the decay of 26Al and 60Fe. The distribution of these heat sources inside the celestial body is not uniform. In fact, 26Al is a lithophile element following the migration of the silicate and 60Fe is a siderophile element following the metal. In modeling the differen- tiation of small bodies it is fundamental to include at least two fluid phases in addition to the solid matrix. This study presents a first time three-phase mixture model for the metal-silicate segregation in a compacting body. The theoretical model is developed fol- lowing the classical averaging approach. The governing equations are then implemented in a numerical model in 1-D spherical geometry. In presence of two fluids, these can exchange their position within the porous matrix even in absence of compaction. They also act a mutual viscous drag force, which results in small fractions of metal to ascend with the lighter silicate, and viceversa. / <p>Validerat; 20151008 (global_studentproject_submitter)</p>

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

In this thesis, we study two different topics: collisional probability between two bodies and dynamics of the sporadic meteoroids in the Solar System. Determination of the collision probabilities in the Solar System is one of the important problems in mod- ern celestial mechanics. Here, we generalize classical theories of the collisions between two bodies by Öpik, Wetherill or Greenberg by including the Kozai-Lidov oscillations, a mechanism that significantly change orbital eccentricity and inclination in the Solar System. Sporadic meteors have been studied for many decades providing a wealthy re- source of data. Here, we build dynamical steady-state models for all known populations observed in the sporadic meteoroid complex based on the latest and most precise data provided by Canadian Meteor Orbit Radar (CMOR). Our models using the latest theo- ries for cometary populations in the Solar System accurately describe observed sporadic background population. Our results are in agreement with observations provided by space probes IRAS and LDEF.

Light curve inversion is a standard method to determine shapes, rotation periods and spin axis orientations of asteroids. This method can be extended to determine the size, albedo, thermal inertia and surface roughness parameters of an asteroid by including observations in thermal infrared. A solution of the Heat Conduction Equation (HCE) is necessary to model infrared flux from the asteroid. We analyse the accuracy requirements of the extended method for numerical solution of the HCE. We show that current implementation leads to errors in flux that are substantial. We recommend changes in the current implementation of the HCE solving approach to address the accuracy issues. We discuss uniqueness and stability of the solutions produced by the extended method as well as the accuracy of the determined parameters and their stability. Shapes of asteroids are produced and their physical attributes are determined based on light curve and infrared data.

This thesis presents an autonomous vision-based navigation strategy applicable to the approach phase of a small body mission, developed within the Robotics Section at NASA Jet Propulsion Laboratory. Today, the operations performed to approach small planetary bodies are largely dependent on ground support and human decision-making, which demand operational complexity and restrict the spectrum of achievable activities throughout the mission. In contrast, the autonomous pipeline presented here could be run onboard, without ground intervention. Using optical data only, the pipeline estimates the target body's rotation, pole, shape, and performs identification and tracking of surface landmarks, for terrain relative navigation. An end-to-end simulation is performed to validate the pipeline, starting from input synthetic images and ending with an orbit determination solution. As a case study, the approach phase of the Rosetta mission is reproduced, and it is concluded that navigation performance is in line with the ground-based state-of-the-art. Such results are presented in detail in the paper attached in the appendix, which presents the pipeline architecture and navigation analysis. This thesis manuscript aims to provide additional context to the appended paper, further describing some implementation details used for the approach simulations. / Detta examensarbete presenterar en strategi för ett autonomt visionsbaserat navigationssystem för att närma sig en liten himlakropp. Strategin har utvecklats av robotikavdelningen vid NASA Jet Propulsion Laboratory i USA. Nuvarande system som används för att närma sig en liten himlakropp bygger till största delen på markstationer och mänskligt beslutsfattande, vilka utgör komplexa rutiner och begränsar spektrumet av möjliga aktiviteter under rymduppdraget. I jämförelse, det autonoma system presenterat i denna rapport är utformat för att köras helt från rymdfarkosten och utan krav på kontakt med markstationer. Genom att använda enbart optisk information uppskattar systemet himlakroppens rotation, poler och form samt genomför en identifiering och spårning av landmärken på himlakroppens yta för relativ terrängnavigering. En simulering har genomförts för att validera det autonoma navigationssystemet. Simuleringen utgick ifrån bilder av himlakroppen och avslutades med en lösning på banbestämningsproblemet. Fasen då rymdfarkosten i ESA:s Rosetta-rymduppdrag närmar sig kometen valdes som fallstudie för simuleringen och slutsatsen från denna fallstudie var att systemets autonoma navigationsprestanda var i linje med toppmoderna system. Den detaljerade beskrivningen av det autonoma systemet och resultaten från studien har presenterats i ett konferensbidrag, som ingår som bilaga till rapporten. Inledningen av rapporten syftar till att förtydliga bakgrunden och implementering som komplement till innehållet i bilagan.