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A viewgraph presentation on the discovery of Enceladus water vapor plumes is shown. Conservative modeling of this water vapor is also presented and also shows that Enceladus is the source of most of the water required to supply the neutrals in Saturn's system and resupply the E-ring against losses.
Surface temperature measurements from the STS-119 boundary-layer transition experiment on the space shuttle orbiter Discovery provide a rare opportunity to assess turbulent CFD models at hypersonic flight conditions. This flight data was acquired by on-board thermocouples and by infrared images taken off-board by the Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) team, and is suitable for hypersonic CFD turbulence assessment between Mach 6 and 14. The primary assessment is for the Baldwin-Lomax and Cebeci-Smith algebraic turbulence models in the DPLR and LAURA CFD codes, respectively. A secondary assessment is made of the Shear-Stress Transport (SST) two-equation turbulence model in the DPLR code. Based upon surface temperature comparisons at eleven thermocouple locations, the algebraic-model turbulent CFD results average 4% lower than the measurements for Mach numbers less than 11. For Mach numbers greater than 11, the algebraic-model turbulent CFD results average 5% higher than the three available thermocouple measurements. Surface temperature predictions from the two SST cases were consistently 3 4% higher than the algebraic-model results. The thermocouple temperatures exhibit a change in trend with Mach number at about Mach 11; this trend is not reflected in the CFD results. Because the temperature trends from the turbulent CFD simulations and the flight data diverge above Mach 11, extrapolation of the turbulent CFD accuracy to higher Mach numbers is not recommended.
With humans pushing to live further off Earth for longer periods of time, it is increasingly important to understand the changes that occur in biological systems during spaceflight whether these be astronauts, their microbial commensals, or their plant-based life support systems. In a three-part presentation, we discuss GeneLab and recent discoveries regarding the microbiota of spacecrafts and space-flown animals. Part 1: GeneLab: Open Science for Life in Space, Jonathan Galazka, NASA Ames Research Center To accelerate the pace of discovery from precious spaceflight biological experiments, NASA as develop the GeneLab data system (genelab.nasa.gov), which allows unfettered access to omics data from spaceflight and spaceflight relevant experiments. GeneLab houses metagenomic datasets from spacecraft and relevant spacecraft models. Users can download this data and associated metadata to make new discoveries about how microbial communities may change and adapt to spaceflight.
The space bioscience field has long been plagued by the challenge of spaceflight with effects of radiation and microgravity. Having multiple and repeated spaceflight experiments for model organisms to solve these space stressors is costly and time consuming. Therefore, reusing and reanalyzing legacy experiments is one way that scientists can draw new conclusions in a timely manner and without using too many resources. Moreover, advances in general biological knowledge allows legacy experiments to be placed into more complete context.Here we aim to analyze all data and metadata taken from rats flown on the SLS-1 mission to create a comprehensive biological model that can be supplemented with current data to allow new discoveries in how space flown organisms adapt to the space environment. Our approach begins with the identification of all the data and metadata, including graphs and tables, for SLS-1 in NASA archives and other sources. Then, each piece of data and metadata will be digitized, reformatted and analyzed. Lastly, a previously developed astronaut model will be used to create the data framework and a comprehensive biological rodent model. The datasets we are using is from the 1991 SpaceLab Life Science 1 (SLS-1) NASA Mission. This was the first designated spacelab mission flown. All 29 rodents were tested for nine days in two different habitats: Research Animal Holding Facility (RAHF) and Animal Enclosure Module (AEM). The rodents were prepared for a live return and compared to a ground control. A total of 30 rodent experiments were accepted as flight studies on the mission. By digitization and reorganizing SLS-1 rat data we will both directly generate new insights and indirectly enable other scientists to by providing the data and metadata in a digitized form.
The space bioscience field has long been plagued by the challenge of spaceflight with effects of radiation and microgravity. Having multiple and repeated spaceflight experiments for model organisms to solve these space stressors is costly and time consuming. Therefore, reusing and reanalyzing legacy experiments is one way that scientists can draw new conclusions in a timely manner and without using too many resources. Moreover, advances in general biological knowledge allows legacy experiments to be placed into more complete context.Here we aim to analyze all data and metadata taken from rats flown on the SLS-1 mission to create a comprehensive biological model that can be supplemented with current data to allow new discoveries in how space flown organisms adapt to the space environment. Our approach begins with the identification of all the data and metadata, including graphs and tables, for SLS-1 in NASA archives and other sources. Then, each piece of data and metadata will be digitized, reformatted and analyzed. Lastly, a previously developed astronaut model will be used to create the data framework and a comprehensive biological rodent model. The datasets we are using is from the 1991 SpaceLab Life Science 1 (SLS-1) NASA Mission. This was the first designated spacelab mission flown. All 29 rodents were tested for nine days in two different habitats: Research Animal Holding Facility (RAHF) and Animal Enclosure Module (AEM). The rodents were prepared for a live return and compared to a ground control. A total of 30 rodent experiments were accepted as flight studies on the mission. By digitization and reorganizing SLS-1 rat data we will both directly generate new insights and indirectly enable other scientists to by providing the data and metadata in a digitized form.
The discovery of a molecular thin-film contamination on Genesis flown array samples changed the course of preliminary assessment strategies. Analytical techniques developed to measure solar wind elemental abundances must now compensate for a thin-film contamination. Currently, this is done either by experimental cleaning before analyses or by depth-profiling techniques that bypass the surface contamination. Inside Johnson Space Center s Genesis dedicated ISO Class 4 (Class 10) cleanroom laboratory, the selection of collector array fragments allocated for solar wind analyses are based on the documentation of overall surface quality, visible surface particle contamination greater than 1 m, and the amount of thin film contamination measured by spectroscopic ellipsometry. Documenting the exact thickness, surface topography, and chemical composition of these contaminates is also critical for developing accurate cleaning methods. However, the first step in characterization of the molecular film is to develop accurate ellipsometry models that will determine an accurate thickness measurement of the contamination film.
Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.
A model implemented in a program that runs on the IBM PC for estimating the latent (or post ship) content of a body of software upon its initial release to the user is presented. The model employs the count of errors discovered at one or more of the error discovery processes during development, such as a design inspection, as the input data for a process which provides estimates of the total life-time (injected) error content and of the latent (or post ship) error content--the errors remaining a delivery. The model presented presumes that these activities cover all of the opportunities during the software development process for error discovery (and removal).
For two decades, the peroxychloroformyl radical, ClC(O)OO, has played a central role in models of the chemical stability of the Venus atmosphere. No confirmation, however, has been possible in the absence of laboratory measurements for ClC(O)OO. We report the isolation of ClC(O)OO in a cryogenic matrix and its infrared and ultraviolet spectral signatures. These experiments show that ClC(O)OO is thermally and photolytically stable in the Venus atmosphere. These experimental discoveries validate the existence of ClC(O)OO, confirm several longstanding model assumptions, and provide a basis for the astronomical search for this important radical species.
Models for the interior structures of Io, Ganymede and Callisto are proposed based on recent discoveries of volcanism and high heat flow on Io, a primordial, heavily cratered surface on Callisto, and a surface modified by endogenic processes on Ganymede. The model for Io consists of a thin, high-rigidity outer shell overlying a thin, partially molten or molten layer maintained by tidal dissipation in the outer shell, which in turn surrounds a solid interior. Ganymede is modeled as an ice outer layer surrounding a shell of undifferentiated, primordial ice-silicate mixture and a rock core, with accretional heating responsible for melting the ice in an originally homogeneous ice-silicate interior. Finally, the old, heavily cratered surface of Callisto is interpreted as suggesting a primordial ice-silicate mixture with little, if any, ice-rock differentiation in the interior.
The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.
Recent discoveries have established the fact that RNA is capable of acting as an enzyme. In this study two different types of molecular orbital calculations, INDO and ab initio, were used in an attempt to assess the structural/functional role of the Mg2+ hydrated complex in ribozyme reactions. Preliminary studies indicate that the reaction is multistep and that the Mg2+ complex exerts a stabilizing effect on the intermediate or midpoint of the reaction.
The Space Weather and Heliophysics research and modeling community has been pushing the limits of our ability to understand and predict space weather events. The Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) supports the community by providing a convenient collaborative platform hosting space weather models, model simulation data, curated datasets of solar events, and associated value-added services. Using these services, researchers and other end-users may exercise, evaluate, and intercompare contributed models, triage designated R2O models, as well as collaborate on a continuously updated archive of model run results. We will focus on CCMC’s ongoing commitment to the principles and guidelines of the Open Science initiative. Particularly, we will discuss our work towards making our services more transparent and our library of model simulations more accessible, open, and reproducible. We will introduce our recent tools for data discovery and correlative analysis designed to further increase the value of the user-generated data and metadata. We will also present our recent work on making heliophysical models more accessible and open to the community, particularly through simplified user experience and expert domain support. We will report on our progress in establishing an inter-center infrastructure with the ESA Virtual Space Weather Modelling Centre (VSWMC), designed to cross organizational boundaries and provide streamlined access to a joint palette of the models.
The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.
The unexpected discovery of remanent crustal magnetism on Mars was one of the most intriguing results from the Mars Global Surveyor mission. The origin of the pattern of magnetization remains elusive. Correlations with gravity and geology have been examined to better understand the nature of the magnetic anomalies. In the area of the Martian dichotomy between 50 and 90 degrees E (here referred to as the Ismenius Area), we find that both the Bouguer and the isostatic gravity anomalies appear to correlate with the magnetic anomalies and a buried fault, and allow for a better constraint on the magnetized crust].
The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.
Over the last several years there has been steady progress in several areas related to the structure and composition of planetary rings. Much of the progress was related to Saturn's rings: new observations by Hubble Space Telescope (HST) and NASA's Infrared Telescope Facility (IRTF), theoretical modeling of the vertical structure of, and particle properties in the rings, discovery of intriguing structural/spectral variations on small spatial scales, new observations and analysis of thermal emission from the rings, theoretical and observational constraints on the quadrupole brightness asymmetry at visual and microwave wavelengths, and new studies of "clumps" in and near the F ring. In addition, new observations were obtained of Jupiter's rings by Cassini, and new models were put forth to explain the narrow edges and apse alignment of the Uranian rings (and presumably other narrow eccentric rings).
Current understanding of the nature of Titan's surface and some new ideas for explaining the curious radar returns from Saturn's largest satellite are reviewed. Pre-Voyager models of the surface, based largely on cosmochemistry and the discovery of atmospheric methane, allowed for a range of possibilities, including pure methane oceans. The Voyager 1 flyby ruled out this last possibility, replacing it with compelling observational arguments in favor of a mixed light hydrocarbon and nitrogen ocean. Ground based radar observations indicated a surprisingly reflective surface which is inconsistent with a hydrocarbon ocean and more reminiscent of the Galilean Satellites. Nonetheless, passive radiometric measurements of the surface do not support the notion that Titan's surface is like that of the Galilean satellites. One of the arguments against hydrocarbon oceans reflecting radar energy is that most solid, complex hydrocarbon and nitriles will be denser than the liquid and sink. Nonetheless, many of the aerosol species will coagulate in highly nonspherical patterns, and some species probably polymerize in long chains. Such chains will have very low sedimendation velocities in the ocean and may remain near the surface through ocean mixing process. The prospect of an oceanic 'soup' of polar polymers acting as volume reflectors at radio wevelengths suggests that the interpretation of radar observations needs evaluation.