NASA/FAA Grand Challenge Overview

No abstract availabl

Orbit Selection for the Proposed Lynx Observatory Mission

The Advanced Concepts Office design team performed several analyses and trades in support of orbit selection for the proposed Lynx mission, an x-ray observatory being submitted to the Astro2020 Decadal Survey. Though the descriptions in this Technical Memorandum (TM) focus on the Lynx mission, the approach and process for selecting the final orbit is applicable to a variety of proposed science and exploration missions. To select the best orbit for the Lynx science, mission designers assembled a team of subsystem and discipline experts, in addition to mission analysts, to evaluate several candidate orbits. These discipline experts included members of the science and instrument team, power and avionics, thermal, propulsion, and environments. The goal was to clearly show the benefits and weaknesses of each orbit in the trade space and provide sound justification for the final selection. Discipline experts conducted trades and evaluated the results using a variety of methods including engineering judgement, rough estimates, and detailed calculations, and rolled the results into a final grade using a weighted grading method. The orbit options could then be ranked. The principal investigator (PI) for the mission, along with the science team, was given the task of final orbit selection. The result of the trades indicated that a halo orbit about the second Sun-Earth Lagrange point (SE-L2), similar to the planned orbit for the James Webb Space Telescope (JWST), was the best choice for the Lynx mission. Details of how the team arrived at this selection are below

Computational Simulations of a Mach 0.745 Transonic Truss-Braced Wing Design

No abstract availabl

Comparison of Three-Dimensional in Situ Observations and Phase-Field Simulations of Microstructure Formation During Directional Solidification of Transparent Alloys Aboard the ISS

No abstract availabl

NASAs Human Landing System: The Strategy for the 2024 Mission and Future Sustainability

In response to the 2018 White House Space Policy Directive- sustainable lunar exploration, and to the Vice Presidents March 2019 direction to do so by 2024, NASA is working to establish humanity's presence on and around the Moon by: 1) sending payloads to its surface, 2) assembling the Gateway outpost in orbit and 3) demonstrating the first human lunar landings since 1972. NASAs Artemis program is implementing a multi-faceted and coordinated agency-wide approach with a focus on the lunar South Pole. The Artemis missions will demonstrate new technologies, capabilities and business approaches needed for future exploration, including Mars. Assessing options to accelerate development of required systems, NASA is utilizing public-private engagements through the Human Exploration and Operations (HEO) Mission Directorates NextSTEP Broad Agency Announcements. The design, development and demonstration of the Human Landing System (HLS) is expected to be led by commercial partners. Utilizing efforts across mission directorates, the Artemis effort will benefit from programs from the Science Mission Directorate (SMD) and Space Technology Mission Directorate (STMD). SMDs Commercial Lunar Payload Services (CLPS) initiative will procure commercial robotic lunar delivery services and the development of science instruments and technology demonstration payloads. The Space Technology Mission Directorate (STMD) portfolio of technology advancements relative to HLS include lunar lander components and technologies for pointing, navigation and tracking, fuel storage and transfer, autonomy and mobility, communications, propulsion and power. In addition to describing the objectives and requirements of the 2024 Artemis mission, this paper will present NASAs approach to accessing the lunar surface with an affordable human-rated landing system, current status and the role o a sustainable lunar presence

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions

Water Ice Cloud Feedbacks over the North Polar Residual Cap at Moderate Obliquity

Several global climate modeling studies have now shown that water ice clouds can warm the surface 10s of K at moderate obliquities [1,2,3]. Significant greenhouse warming occurs because the predicted clouds are optically thick, the cloud particles are large enough to efficiently interact with infrared radiation, and the clouds either form at or are transported to high altitudes where the atmosphere is cold. Radiativedynamic feedbacks play a critical role in producing the conditions needed for a strong cloud greenhouse. Two feedbacks have been identified: one involves atmospheric warming by clouds aloft at lower latitudes. These clouds are generally associated with the global Hadley circulation. The second feedback involves clouds that form over the North Polar Residual Cap (NPRC) during summer. These clouds are more closely associated with the regional polar circulation. We focus here on the second of these feedbacks with the goal of understanding the details of the interactions between sublimation, cloud formation and transport in the north polar region. We show that these feedbacks strongly control the wetness of the atmosphere and the strength of the cloud greenhouse at moderate obliquity

We Should Search for Life in Mars N. Polar Ground Ice

The 2008 Phoenix Mars lander mission sampled ground ice at 68N latitude. Mission results, considered along with climate modeling studies, suggest that the site is habitable for life during high obliquity periods. The Icebreaker mission has been proposed to the NASA Discovery program to search for biosignatures produced during habitable periods. This paper explores its rationale and approach

Adaptable, Deployable, Entry and Placement Technology

No abstract availabl

Carbon Export Through Submesoscale instabilities: Combining in Situ and Satellite Products

No abstract availabl