Coupled CFD Analysis of Propulsion-Airframe Integration
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Thermodynamics has been fundamental to the interpretation of geologic data and modeling of geologic systems for decades. However, more recent advancements in computational capabilities and a marked increase in researchers’ accessibility to computing tools has outpaced the functionality and extensibility of currently available modeling tools. Here we present VESIcal (Volatile Equilibria and Saturation Identification calculator): the first comprehensive modeling tool for H 2 O, CO 2 , and mixed (H 2 O-CO 2 ) solubility in silicate melts that: a) allows users access to seven commonly used models, plus easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an excel spreadsheet for simple post-modeling analysis; e) integrates advanced plotting capabilities directly within the tool; and f) provides all of these within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python. The tool is presented within this manuscript, which is a Jupyter notebook containing worked examples accessible to python users with a range of skill levels. The basic functions of VESIcal can also be access via a web app (https://vesical.anvil.app). The VESIcal python library is open-source and available for download at https://github.com/kaylai/VESIcal.
Autonomous systems react intelligently to their environments, making them capable of handling many possible conditions, but challenging to test. We are investigating a new test development method that aims to maximize the confidence to be achieved by combining Assurance Cases with High Throughput Testing (HTT). Assurance Cases, developed for safety-critical systems, are a rigorous argument that the system satisfies a property (e.g., the Mars rover will not tip over during a traverse). They integrate testing, analysis, and environmental and operational assumptions, from which the set of conditions that testing must cover is determined. In our method, information from the Assurance Case is used to determine the test coverage needed, and then input to HTT to generate the minimal test suites needed to provide that coverage.
Modeling the solubility of volatiles in silicate melts is fundamental to the interpretation of volcanic systems and has implications for magma dynamics, eruption style, and material transport between the mantle, crust, and atmosphere. Recent advancements in computational capabilities and access to computing tools has outpaced the functionality and extensibility of previously available modeling platforms. Here we present VESIcal (Volatile Equilibria and Saturation Index calculator), the first comprehensive modeling tool for H2O, CO2, and mixed (H2O-CO2) solubility in silicate melts that: a) allows users access to seven popular models, with easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an Excel spreadsheet or CSV file for post-modeling analysis; e) integrates plotting capabilities directly within the tool; and f) provides all of this within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python.Here we will provide a demonstration of VESIcal and its capabilities with applications to various volcanic processes affected by volatiles. VESIcal represents the first tool capable of directly comparing multiple solubility models and equations of state. We find that commonly used models predict surprisingly different volatile solubilities, particularly for pure CO2 or mixed CO2-H2O fluids. Even for melt compositions that are well represented in the calibration datasets of multiple models (e.g., MORBs), calculated solubilities for pure CO2 and pure H2O can deviate from one another by factors of >2 leading to 2x deviations in calculated saturation pressures (e.g., 5 to 10 kbar). The solubility of CO2 predicted by different rhyolitic models also differs substantially, overwhelming other sources of uncertainty such as analytical errors on measurements of volatile contents or uncertainties in crustal density profiles. This highlights the importance of model choice when drawing geological conclusions based on volatiles in magmas.
Typically for a rendezvous and docking scenario, only a single vehicle is considered the active vehicle. As a result, the target vehicle’s GN&C system is typically not a factor in the integrated performance analysis as it assumes a passive role. However, for upcoming NASA Artemis missions the active vehicle is both the chaser and target spacecraft. In addition, both vehicles are cooperating with one another, sharing telemetry data, and relying on data extracted from the GN&C system of the other. The process also includes a team of ground support personnel in mission control are tracking and monitoring each spacecraft uplinking state estimates and targeting solutions to support mission operations and enhance the onboard flight system performance. Being able to quickly analyze the impact of both vehicles with two different GN&C systems that also interact with the ground that does its own navigation and targeting uploads is critical. This paper outlines how to perform this rapid analysis using linear covariance techniques and applies them to a rendezvous scenario initiated in low lunar orbit and completed in a Near Rectilinear Halo Orbit (NRHO) representative of the NASA Artemis III mission.
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The United States’ Wide Area Augmentation System (WAAS) broadcasts data to facilitate aircraft navigation. This paper examines the spatial dependence of ionospheric disturbances that have threatened the accuracy and reliability of position estimates derived from these data over the period 2000 – 2019. We address two distinct aspects of this spatial dependence: (1) the geographic distribution of these threats, in particular, in relation to geomagnetic latitude, and (2) the geometric dependence of threats relative to the locations of the receiver sites that comprise the WAAS network. We analyze threat distributions in terms of the various means that WAAS employs to mitigate these threats, including the Extreme Storm Detector, the Moderate Storm Detector, local irregularity detectors, and the ionospheric threat model. Distinct distributions are presented for threats occurring in Solar Cycle 23 and those of Solar Cycle 24. To study the geometric dependence of threats on receiver locations, we use as a metric the distance separating a threat from the centroid of the nearest Ncentroid receivers. Large values of this metric identify threats at or beyond the edge of coverage. We conclude by discussing the implications of our results for WAAS operations.
The term “Digital Twin” was first used in 2002 for product lifecycle management. Since then, Digital Twin concepts have been proposed in various domains until very recently for Earth Science. For NASA’s Advanced Information Systems Technology (AIST) Program, an Earth System Digital Twin (ESDT) is defined as composed of three components: 1. A Digital Replica, i.e., an integrated picture of the past and current states of Earth systems 2. Forecasting capabilities, providing an integrated picture of how Earth systems will evolve in the future from the current state 3. Impact Assessment capabilities, providing an integrated picture of how Earth systems could evolve under different hypothetical what-if scenarios. Developing such a vision will require technologies related to: integrating continuous observations from various disparate sources; developing frameworks that builds on inter-connected models; improving the speed and accuracy of integrated prediction, analysis and visualization capabilities (e.g., by using machine learning); and utilizing causality and uncertainty quantification to improve our understanding of the evolution of Earth Science systems as a function of their interactions with other Earth and human systems. In addition, AIST is also investigating interoperability standards to federate multiple Digital Twins, as well as computational resources required by those systems.
As Human spaceflight evolves and develops, the technology the crew relies on for life support must become more advanced than at any point in NASA’s history. Nowhere is this more apparent than the Gateway, where lessons learned from Mercury to ISS are being applied in the design and production of the life support systems. Some of these technological improvements are proven in flight configuration, or have a heritage of proven flight hardware, but many are of a lower technology readiness level. Due to the unpredictable nature of the metabolic byproducts (CO 2 , H 2 O, and heat), even the proven technologies can fail to meet requirements for crew safety. Detailed modeling of individual components excels in proving component level requirements are met, but fails to verify system or architecture level requirements. This paper expounds upon an effort to take a number of detailed component level models of the Gateway ECLSS and integrate them into a larger architecture model known as the Gateway Integrated ECLSS Model (GIEM). The GIEM is then used to study how the subsystems work synergistically to meet environmental requirements as well as investigate how changes at the component level effect the Gateway stack as a whole.
NASA’s Artemis missions aim to return humans to the Moon for the first time since the Apollo Program. Unlike the Apollo missions, Artemis missions will take advantage of pre-emplaced assets on the lunar surface to support crewed exploration, science, and utilization. Although the initial Artemis surface missions are intended to keep crew on the lunar surface for durations ranging from several days to several weeks, crewed stays of longer periods may provide additional support toward NASA’s Moon to Mars Objectives, including long-term exploration and continuous human lunar presence. If mission durations are extended beyond the expected few weeks, the elements and concepts of operation for the lunar architecture will need to be capable of supporting that extension. This paper uses an integrated systems analysis perspective to examine the architectural considerations of extending the duration of human missions on the lunar surface. By identifying these key architectural considerations, this paper offers insight into how elements and operations might meet the demands of potential future mission concepts. This paper does not recommend changes to NASA’s lunar architecture, and it does not evaluate whether any mission concepts affect the relative satisfaction of the Moon to Mars Objectives. However, if crew do remain on the surface for progressively longer periods of time, the functions and capabilities provided by the assets supporting the crew may need to adjust to support the needs of those missions. The functions and capabilities identified as key to enable such extended duration missions include power generation and storage, logistics delivery, radiation mitigation, provision of medical and exercise capability, abort from the lunar surface, maintenance, and the provision of pressurized volume. For example, the medical capabilities available to the crew may become more extensive, and habitable elements may need to change to support longer crewed periods (and shorter quiescent periods). In some cases, these changes may be accomplished through alterations to use cases, operational changes, or the evolution of lunar surface elements, but in other cases, new elements may be preferable. By identifying key considerations for the functions and capabilities of notional lunar surface elements as the length of each crewed mission is extended from several days to continuous human presence on the Moon, this paper assesses how the elements of NASA’s lunar architecture might support the extension of mission durations. In addition, this analysis can help inform future evaluation of whether such extended duration missions improve NASA’s ability to address its Moon to Mars Objectives.
This project created a visualization and simulation software platform for the entire lunar surface. Given the polar Artemis landing targets, the challenging terrain, lighting, and associated integrated crew / vehicle risk assessment, a more integrated approach to mission safety certification, potentially impacting equipment design and crew activity is required. This integrated capability will enable Safety and Mission Assurance (SMA), Flight Operations (FOD), and EVA to effectively collaborate to reduce human lunar exploration operational risk utilizing the identical tools to perform mission planning, rehearsal simulations, and providing the Safety Review Panel with integrated risk analysis capability.
A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Advanced efforts with advanced models will include examining multiple production targets to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.
The Nancy Grace Roman Telescope (RST) is a NASA observatory designed to unravel the secrets of dark energy and dark matter, search for and image exoplanets, and explore many topics in infrared optics. Scheduled to launch in no earlier than October 2026, this 2.4 meter aperture telescope has a field of view 100 times greater than the Hubble Space Telescope. The mission is currently in its construction phase, where integrated modeling between thermal, structural, and optical models of the observatory is necessary to demonstrate science quality images over the range of operational parameters. This presentation discusses the most recent integrated modeling analysis cycle for Roman, including model correlation with our instrument level testing. We include a discussion on improved processes of the handling of the various flows of data between the modeling disciplines and discipline specific monte-carlo analysis predictions. We will finish with the predicted uncertainties and expected performance for our upcoming observatory alignment verification test using machine learning algorithms.
The primary contribution of this paper is an initial analysis of integrating digitally enabled cooperative operations (or digital operations for short) with Visual Flight Rules (VFR) and Instrument Flight Rules (IFR) operations in the terminal airspace around a Class D airport, specifically around Fort Worth Alliance airport (KAFW). Enabled by connected digital technologies and automated information exchange, digital operations as envisioned would utilize cooperative practices and operator-responsible separation to ensure safety. In the present study, three conflict management configurations for digital operations were analyzed: 1) centralized with a single instance of a conflict management service to model a scenario with only one fleet operator conducting digital operations or all fleet operators utilizing the same service, 2) federated with two different instances to model a scenario with two different fleet operators conducting digital operations, and 3) fully distributed with a different instance for each digital operation. In simulations in which recorded tracks for VFR and IFR operations were played back and digital operations were modeled that would nominally arrive at KAFW once every 20 minutes (given no conflict resolution maneuvers), there were one or fewer losses of separation (LOS) across the three conflict management configurations. The same result was seen in simulations in which digital operations would nominally arrive at KAFW once every 10 minutes. However, in simulations in which digital operations would nominally arrive at KAFW once every 5 minutes, the number of LOS ranged between six and ten. This highlights the need for follow-on research to investigate the extent to which additional capabilities for digital operations, such as complexity management and/or flow organization, may be needed.
The primary contribution of this paper is an initial analysis of integrating digitally enabled cooperative operations (or digital operations for short) with Visual Flight Rules (VFR) and Instrument Flight Rules (IFR) operations in the terminal airspace around a Class D airport, specifically around Fort Worth Alliance airport (KAFW). Enabled by connected digital technologies and automated information exchange, digital operations as envisioned would utilize cooperative practices and operator-responsible separation to ensure safety. In the present study, three conflict management configurations for digital operations were analyzed: 1) centralized with a single instance of a conflict management service to model a scenario with only one fleet operator conducting digital operations or all fleet operators utilizing the same service, 2) federated with two different instances to model a scenario with two different fleet operators conducting digital operations, and 3) fully distributed with a different instance for each digital operation. In simulations in which recorded tracks for VFR and IFR operations were played back and digital operations were modeled that would nominally arrive at KAFW once every 20 minutes (given no conflict resolution maneuvers), there were one or fewer losses of separation (LOS) across the three conflict management configurations. The same result was seen in simulations in which digital operations would nominally arrive at KAFW once every 10 minutes. However, in simulations in which digital operations would nominally arrive at KAFW once every 5 minutes, the number of LOS ranged between six and ten. This highlights the need for follow-on research to investigate the extent to which additional capabilities for digital operations, such as complexity management and/or flow organization, may be needed.
Two of the most important goals of the adoption of the FAIR principles are increasing the ability of agents to find and re-use research data. Achieving these goals for space life sciences research is even more pressing, given the relatively expensive and scarce nature of these data. We have reported in the past on the progress made by exemplar life sciences data systems towards implementing FAIR, showing gaps particularly in the “interoperability area” of the principles; the lack of common conceptual models for space life science research is one reason for this gap. There were few available resources that define, annotate, categorize or otherwise relate various kinds of metadata describing the acquisition, nature, and intent of investigational space life sciences data. To address this gap, NASA is working with the Open Biological and Biomedical Ontology Foundry (https://obofoundry.org/) to develop the Space Life Science Ontology (SLSO) that is intended to support archival and other kinds of systems that operate using these data. The scope of the ontology includes concepts regarding those aspects of investigation design and execution specific or unique to space environments, such as types of specialized equipment, operating organizations, and documentation. The ontology is continually being developed and published to the life science community (https://github.com/nasa/LSDAO/); at the time of this publication, the SLSO newly and uniquely defines 30 types (classes), 90 properties, and 14 relations specific to space life sciences metadata. In addition, the SLSO reuses (imports) some 2,360 types (classes), 49 properties, and 393 relations from other ontologies that are relevant to these kinds of metadata. In addition to its role as a common conceptualization for space biomedical research activities, the SLSO can also be used to provide automated support for traditionally difficult and expensive activities such as data curation and cross-system data integration and analysis.
Strategic level analysis of the integrated behavior of lunar transportation and lunar surface systems architecture options is performed to assess the benefit, viability, affordability, and robustness of system design choices. This analysis employs both deterministic and probabilistic modeling techniques so that the extent of potential future uncertainties associated with each option are properly characterized. The results of these analyses are summarized in a predefined set of high-level Figures of Merit (FOMs) so as to provide senior NASA Constellation Program (CxP) and Exploration Systems Mission Directorate (ESMD) management with pertinent information to better inform strategic level decision making. The strategic level exploration architecture model is designed to perform analysis at as high a level as possible but still capture those details that have major impacts on system performance. The strategic analysis methodology focuses on integrated performance, affordability, and risk analysis, and captures the linkages and feedbacks between these three areas. Each of these results leads into the determination of the high-level FOMs. This strategic level analysis methodology has been previously applied to Space Shuttle and International Space Station assessments and is now being applied to the development of the Constellation Program point-of-departure lunar architecture. This paper provides an overview of the strategic analysis methodology and the lunar exploration architecture analyses to date. In studying these analysis results, the strategic analysis team has identified and characterized key drivers affecting the integrated architecture behavior. These key drivers include inclusion of a cargo lander, mission rate, mission location, fixed-versus- variable costs/return on investment, and the requirement for probabilistic analysis. Results of sensitivity analysis performed on lunar exploration architecture scenarios are also presented.
Exploration Life Support (ELS) is a current project under NASA's Exploration Systems Mission Directorate. The ELS Project plans, coordinates and implements the development of advanced life support technologies for human exploration missions in space. Recent work has focused on closed loop atmosphere and water systems for long duration missions, including habitats and pressurized rovers. But, what are the critical questions facing life support system developers for these and other future human missions? This paper explores those questions and how progress in the development of ELS technologies can help answer them. The ELS Project includes the following Elements: Atmosphere Revitalization Systems, Water Recovery Systems, Waste Management Systems, Habitation Engineering, Systems Integration, Modeling and Analysis, and Validation and Testing, which includes the Sub-Elements Flight Experiments and Integrated Testing. Systems engineering analysis by ELS seeks to optimize overall mission architectures by considering all the internal and external interfaces of the life support system and the potential for reduction or reuse of commodities. In particular, various sources and sinks of water and oxygen are considered along with the implications on loop closure and the resulting launch mass requirements. Systems analysis will be validated through the data gathered from integrated testing, which will demonstrate the interfaces of a closed loop life support system. By applying a systematic process for defining, sorting and answering critical life support questions, the ELS project is preparing for a variety of future human space missions