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At least 703 records · Page 39

Petrological Traverse of the Olivine Cumulate Séítah Formation at Jezero Crater, Mars: A Perspective From SuperCam Onboard Perseverance

Séítah is the stratigraphically lowest formation visited by Perseverance in the Jezero crater floor. We present the data obtained by SuperCam: texture by imagery, chemistry by Laser-Induced Breakdown Spectroscopy, and mineralogy by Supercam Visible and Infrared reflectance and Raman spectroscopy. The Séítah formation consists of igneous, weakly altered rocks dominated by millimeter-sized grains of olivine with the presence of low-Ca and high-Ca pyroxenes, and other primary minerals (e.g., plagioclase, Cr-Fe-Ti oxides, phosphates). Along a ∼140 m long section in Séítah, SuperCam analyses showed evidence of geochemical and mineralogical variations, from the contact with the overlying Máaz formation, going deeper in the formation. Bulk rock and olivine Mg#, grain size, olivine content increase gradually further from the contact. Along the section, olivine Mg# is not in equilibrium with the bulk rock Mg#, indicating local olivine accumulation. These observations are consistent with Séítah being the deep ultramafic member of a cumulate series derived from the fractional crystallization and slow cooling of the parent magma at depth. Possible magmatic processes and exhumation mechanisms of Séítah are discussed. Séítah rocks show some affinity with some rocks at Gusev crater, and with some Martian meteorites suggesting that such rocks are not rare on the surface of Mars. Séítah is part of the Nili Fossae regional olivine-carbonate unit observed from orbit. Future exploration of Perseverance on the rim and outside of the crater will help determine if the observations from the crater floor can be extrapolated to the whole unit or if this unit is composed of distinct sub-units with various origins.

Mars 2020↗

Demonstration of Navigation Doppler Lidar Capabilities Onboard the First Commercial Lunar Lander

Landing on the Moon safely, softly, and precisely is still an extremely difficult task to achieve. Navigation Doppler Lidar (NDL) was designed to provide a solution to spaceflight landing vehicles in achieving this task. NDL is a high-performance, compact, and cost-effective velocity and altitude measurement instrument that has been developed, tested and improved over many years. Testing has now culminated with NDL’s successful operation on two spaceflight vehicles this year: Astrobotic’s Peregrine lunar lander and Intuitive Machines’ Odysseus lunar lander. Odysseus became the first U.S. vehicle to land successfully on the Moon since Apollo 17. The performance data obtained by NDL during the Intuitive Machines mission is exceptional, and this mission fully demonstrated NDL’s ability to produce critical velocity and altitude data for future robotic and human exploration missions to the Moon and other planetary bodies. This paper will give an overview of NDL briefly describing its history, architecture, and theory of operation. NDL’s system checkout on Astrobotic’s Peregrine lunar lander is then discussed followed by NDL’s performance during the Intuitive Machines Odysseus lunar lander mission. A description of how the data acquired by NDL is then used to perform trajectory reconstruction and animation is then provided. Finally, this paper concludes with potential uses of NDL’s acquired mission data to future applications.

Lunar Landing↗

Demonstration of Navigation Doppler Lidar Capabilities Onboard the First Commercial Lunar Lander

Landing on the Moon safely, softly, and precisely is still an extremely difficult task to achieve. Navigation Doppler Lidar (NDL) was designed to provide a solution to spaceflight landing vehicles in achieving this task. NDL is a high-performance, compact, and cost-effective velocity and altitude measurement instrument that has been developed, tested and improved over many years. Testing has now culminated with NDL’s successful operation on two spaceflight vehicles this year: Astrobotic’s Peregrine lunar lander and Intuitive Machines’ Odysseus lunar lander. Odysseus became the first U.S. vehicle to land successfully on the Moon since Apollo 17. The performance data obtained by NDL during the Intuitive Machines mission is exceptional, and this mission fully demonstrated NDL’s ability to produce critical velocity and altitude data for future robotic and human exploration missions to the Moon and other planetary bodies. This paper will give an overview of NDL briefly describing its history, architecture, and theory of operation. NDL’s system checkout on Astrobotic’s Peregrine lunar lander is then discussed followed by NDL’s performance during the Intuitive Machines Odysseus lunar lander mission. A description of how the data acquired by NDL is then used to perform trajectory reconstruction and animation is then provided. Finally, this paper concludes with potential uses of NDL’s acquired mission data to future applications.

Lunar Landing↗

Ground Operations of the ISS GNC Babb-Mueller Atmospheric Density Model

The ISS GNC system was updated recently with a new software release that provides onboard state determination capability. Prior to this release, only the Russian segment maintained and propagated the onboard state, which was periodically updated through Russian ground tracking. The new software gives the US segment the capability for maintaining the onboard state, and includes new GPS and state vector propagation capabilities. Part of this software package is an atmospheric density model based on the Babb-Mueller algorithm. Babb-Mueller efficiently mimics a full analytical density model, such as the Jacchia model. While lacchia is very robust and is used in the Mission Control Center, it is too computationally intensive for use onboard. Thus, Babb-Mueller was chosen as an alternative. The onboard model depends on a set of calibration coefficients that produce a curve fit to the lacchia model. The ISS GNC system only maintains one set of coefficients onboard, so a new set must be uplinked by controllers when the atmospheric conditions change. The onboard density model provides a real-time density value, which is used to calculate the drag experienced by the ISS. This drag value is then incorporated into the onboard propagation of the state vector. The propagation of the state vector, and therefore operation of the BabbMueller algorithm, will be most critical when GPS updates and secondary state vector sources fail. When GPS is active, the onboard state vector will be updated every ten seconds, so the propagation error is irrelevant. When GPS is inactive, the state vector must be updated at least every 24 hours, based on current protocol. Therefore, the Babb-Mueller coefficients must be accurate enough to fulfill the state vector accuracy requirements for at least one day. A ground operations concept was needed in order to manage both the on board Babb-Mueller density model and the onboard state quality. The Babb-Mueller coefficients can be determined operationally in two ways. The first method is to calibrate the coefficients in real-time, where a set of custom coefficients is generated for the real-time atmospheric conditions. The second approach is to generate pre-canned sets of coefficients that encompass the expected atmospheric conditions over the lifetime of the vehicle. These predetermined sets are known as occurrences. Even though a particular occurrence will not match the true atmospheric conditions, the error will be constrained by limiting the breadth of each occurrence. Both methods were investigated and the advantages and disadvantages of each were considered. The choice between these implementations was a trade-off between the additional accuracy of the real-time calibration and the simpler development for the approach using occurrences. The operations concept for the frequency of updates was also explored, and depends on the deviation in solar flux that still achieves the necessary accuracy of the coefficients. This was determined based on historical solar flux trends. This analysis resulted in an accurate and reliable implementation of the Babb-Mueller coefficients and how flight controllers use them during realtime operations.

Brogan, Jonathan↗