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NASA NTRSDate not supplied

Game Based Learning For Earth Science Applications Training

Current NASA Earth capacity development programs employ mechanisms ranging from online resource sharing, and virtual and in-person trainings to share knowledge. While these programs are highly successful at engaging individuals around the world – in 2018, over 8000 individuals and over 2000 institutions from all 50 US states and over 140 countries were engaged through over 150 projects and trainings – user feedback has highlighted the desire for expanded hands-on, practical experiences in incorporating NASA EO insights with localized data and actions. We aim to address this gap by leveraging the benefits of game-based learning to build user skills in integrating NASA and local EO data to guide decisions for climate resiliency and hazard planning. This project is being executed as a two-phase crowdsourced challenge: 1) Phase 1 will require a well-researched product concept that reflects an understanding of NASA’s Earth data and tools and user needs, and proposes an innovative and interactive game or extended reality experience to train users in identifying relevant NASA data and applying insights to their climate resiliency decisions; 2) Winners of Phase 1 will be provided seed funding to develop a working prototype of the product. We aim to award 1-3 final winners to support the development of more than one game, thereby ensuring that NASA's diverse audiences around the world can access training games that best suit their needs and capabilities. This EO training game project fits in the NASA Earth Science Applied Sciences Program’s Capacity Development Program, contributing to the program mission of “helping people around the world better understand [NASA’s Earth] data and find ways to use them” (https://appliedsciences.nasa.gov/what-we-do/capacity-building). The final training game will complement existing programmatic activities of workforce development, trainings, and collaborative projects, while providing the unique value of providing interactive experiences to users and collecting real-time data and feedback to improve NASA’s Earth applications’ products and services related to climate resilience.

Human centered design↗

NASA NTRSDate not supplied

An Overview of NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI): Supporting FAIR and Open Access to Airborne and Field Data

Since 2019, NASA’s Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) has worked to promote and ensure the discoverability and accessibility of the agency’s non-satellite Earth science observations. A primary component of this effort is the development of NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI) and the vetting of key contextual details required to sustain this unique inventory of airborne and field metadata. CASEI provides information on the science objectives motivating data collection, key events/time periods in the observational record aligned with the science objectives, complementary simultaneous observations, programmatic details, and much more. The diverse set of data formats and disciplines served by CASEI have required the implementation of a common data model to organize suborbital observation metadata and efficiently connect appropriate campaigns, platforms, and instruments. The CASEI inventory provides a single entry point for users to search and browse NASA’s airborne and field data archives, regardless of which repository is responsible for their stewardship. This presentation will provide a summary of the motivations for and the development of the CASEI system. Particular attention will be granted to how CASEI facilitates discovery and reuse of these lesser-known NASA data, supporting the Open Science vision and enhancing the return on investments made to collect these unique and varied observations. An up-to-date summary of CASEI inventory content and initial metrics will be provided. Current and future avenues ADMG is pursuing to enhance both CASEI and specific components of suborbital data stewardship at various stages of the data life cycle will also be discussed.

Stephanie M. Wingo↗

NASA NTRS2022

Instrumentation and Testing of Ground and Flight Hardware

The goal of this white paper is to highlight some of the key issues associated with the instrumentation and testing of ground and flight hardware. The information is arranged topically and follows a line of organization that begins with the development of test objectives, the development of requirements, the purchase and installation of instrumentation, and covers many considerations to ensure that measurements will provide the required data during the conduct of the test. Ideally, all of these considerations would be factored into a test program; however, due to programmatic, budgetary, and calendar restraints, only a subset of these factors will typically be considered. Although the primary emphasis of this text is on thermal measurements, all disciplines can benefit from these recommendations.

Thermocouple↗

NASA NTRSDate not supplied

Evaluating Extravehicular Activity Access Options for a Lunar Surface Habitat

NASA’s upcoming return to the moon includes a plan for longer duration lunar missions that prioritize science and surface exploration. The current Artemis Base Camp reference proposes a Surface Habitat (SH) that will support 2 to 4 crewmembers with adequate capability for living and working for 30 days or more. An important aspect of the SH’s design will be its ability to support a high frequency of Extravehicular Activities (EVAs) for scientific purposes. There are multiple airlock designs that can support EVA access from inside the SH out to the lunar surface. This paper explores three unique options for EVA access: a traditional airlock, a suitlock, and a suitport-airlock.A traditional airlock is accessed by use of a hatch, crewmembers don and doff their EVA suits inside the airlock, and it must be depressurized and repressurized for every EVA. A suitlock is also depressurized and repressurized for every EVA but thesuits are donned through a suitport attached to the airlock bulkhead. A suitport-airlock is access through suitports that are attached to the airlock bulkhead as well, but the suitport-airlock stays in vacuum, and it does not have to be depressurized and repressurized for each EVA. This study also evaluated the use of an Airlock Airsave System in the cases of a traditional airlock and suitlock. This system captures a fraction of the airlock gas instead of venting all of the gas pre-EVA. The captured gas isthen used to partially repressurize the airlock post-EVA.In this study, each option is evaluated based on seven different areas of analysis. The metrics include overhead time for EVAs, dust mitigation, consumables for resupply mass, systems mass, system power, safety and mission assurances, and programmatic considerations. The EVA access options are evaluated and compared under each area of analysis. While the baseline case assumes the SH is at 8.2 psia, the authors also investigate the impact of a 10.2 psia habitat as an alternative in this analysis. Following the conclusion of the analysis, the authors recommend the usage of a Suitport-Airlock for Lunar SH missions and detail the reasoning behind the recommendation.

EVA↗

NASA NTRSDate not supplied

Artemis Internal Science Team: Mission Planning Update

As eloquently outlined in the Artemis III Science Definition Team (A3SDT) Report, the definitive statement of science priorities for the first crewed landing of the Artemis program, the return of astronauts to the surface of the Moon is expected to yield paradigm-shifting scientific advances across a wide array of scientific disciplines while facilitating the creation of a thriving cislunar economy. To help establish a cohesive management for the Artemis campaign, the NASA Science Mission Directorate (SMD) and the Exploration Systems Development Mission Directorate (ESDMD) have established an Artemis Internal Science Team to complement future competed surface geology and payload science teams while executing critical programmatic functions at the point of hardware element interface decisions. Here, we describe aspects of Artemis III mission planning, specifically the current progress towards selection of candidate exploration regions for the Artemis III mission.

Artemis↗

NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗

NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

Artificial Intelligence↗

NASA NTRSDate not supplied

Evaluating Extravehicular Activity Access Options for a Lunar Surface Habitat

NASA’s upcoming return to the moon includes a plan for longer duration lunar missions that prioritize science and surface exploration. The current Artemis Base Camp reference proposes a Surface Habitat (SH) that will support 2 to 4 crewmembers with adequate capability for living and working for 30 days or more. An important aspect of the SH’s design will be its ability to support a high frequency of Extravehicular Activities (EVAs) for scientific purposes. There are multiple airlock designs that can support EVA access from inside the SH out to the lunar surface. This paper explores three unique options for EVA access: a traditional airlock, a suitlock, and a suitport-airlock. A traditional airlock is accessed by use of a hatch, crewmembers don and doff their EVA suits inside the airlock, and it must be depressurized and repressurized for every EVA. A suitlock is also depressurized and repressurized for every EVA but the suits are donned through a suitport attached to the airlock bulkhead. A suitport-airlock is accessed through suitports that are attached to the airlock bulkhead as well, but the suitport-airlock stays in vacuum, and it does not have to be depressurized and repressurized for each EVA. This study also evaluated the use of an Airlock Airsave System in the cases of a traditional airlock and suitlock. This system captures a fraction of the airlock gas instead of venting all of the gas pre-EVA. The captured gas is then used to partially repressurize the airlock post-EVA. In this study, each option is evaluated based on seven different areas of analysis. The metrics include overhead time for EVAs, dust mitigation, consumables for resupply mass, systems mass, system power, safety and mission assurances, and programmatic considerations. The EVA access options are evaluated and compared under each area of analysis. While the baseline case assumes the SH is at 8.2 psia, the authors also investigate the impact of a 10.2 psia habitat as an alternative in this analysis. Following the conclusion of the analysis, the authors recommend the usage of a Suitport-Airlock for Lunar SH missions and detail the reasoning behind the recommendation.

EVA↗

NASA NTRSDate not supplied

Designing A Lunar Terrain Vehicle: Thermal Challenges

The Lunar Terrain Vehicle (LTV) is an unpressurized rover that will be used to transport astronauts on the South Pole of the moon under the Artemis Program. The National Aeronautics and Space Administration (NASA) will be partnering with an American industry partner to develop the LTV. This paper discusses the thermal challenges faced when designing a crewed lunar vehicle concept and shares a concept that addresses these constraints. One of the most difficult aspects of the design is surviving the cold lunar night on the South Pole which includes permanently shadowed regions (PSR) that can have an extended lunar night duration of 125 hours. Other challenges are critical component temperature limits and programmatic constraints.

Zaida Y. Hernandez↗

NASA NTRSDate not supplied

Utilizing Gaps and Performance Measures to Inform NASA Environmental Control and Life Support Systems and Human Health and Performance Capability Technology Decisions

Human spaceflight is a complex endeavor requiring multiple capabilities for transportation, crew health, scientific goals, and safe return to Earth. The difference between spaceflight proven capabilities and those needed for future exploration architectures is defined as a capability gap. Capability gaps are not technology specific. Each capability gap may be closed with a wide array of technologies that have unique benefits and challenges. Determining what a capability’s relevant and distinguishing key performance parameters (KPPs) are for a mission is critical. Mass, power, and volume are always constrained and important, but defining these in a way normalized by performance is very important. KPP definition for reliability, dormancy, and integration needs are hard to define but critical. Outside of technical considerations, the programmatic factors of the estimated time to develop the technology and how the technology validation objectives are matured are strong considerations in which technologies should be pursued and how they should leverage earlier mission elements. The Environmental Control and Life Support (ECLSS) and Crew Health and Performance (CHP) capability areas are decomposed to high level gaps. While KPPs should be technology agnostic, they can be used to both compare technologies and measure progress of technology development over time. KPPs help define when the gap is closed, and the core mission objectives can be accomplished. Proposed technology improvements to enhance a capability should balance improved KPPs and against investments in other capabilities that are not yet closed. A selection of gaps, KPPs, and validation objectives and their formulation, current state, and how they inform capability roadmap planning are discussed.

Life Support↗

NASA NTRSDate not supplied

Metal Additive Manufacturing for Rocket Engines: Successes and Failures

NASA has been developing additive manufacturing (AM) for various technical and programmatic advantages for complex rocket engine and aerospace components. This maturation has focused on various AM processes, new alloys, characterizing material properties, developing standards, producing demonstrator parts, and integrating AM hardware in liquid rocket engines through test-fail-fix cycles, as well as application and dissemination of lessons learned of the AM lifecycle. The importance of proper AM processing was made evident in the failure of a Laser Powder Bed Fusion (L-PBF) copper-alloy combustion chamber during a hot-fire test due to a degraded material quality. The hot-fire test aimed to demonstrate high duty cycle under a risk-tolerant development project, where consequences of component failure would be minimal. However, the unintentional component failure emphasized the necessity of robust material characterization and rigorous process control procedures for the safe use of AM components in critical applications. This presentation provides an overview of the development failure, a discussion on the evaluation of the failed chamber and supplemental chambers produced at the same time, a representative material samples that included intentional build witness lines, and a summary of the key results and recommendations from the evaluations. NASA continues to approach AM processes and designs with a level of risk and acceptance of failures that is appropriate for the project objectives, with the overall goal of safe implementation of AM technology and transferring AM technology into commercial space applications. This presentation with provide critical awareness to the community lessons learned on proper implementation of AM.

L-PBF↗

NASA NTRS2022

COMPACT KNN V2: Analogy-Based Cost Estimation Model for CubeSats

The CubeSat Or Microsat Probabilistic and AnalogiesCost Tool, or COMPACT, is a NASA Headquarters fundedeffort to fill the gap in cost estimating capabilities for CubeSats,as well as other microsat spacecraft. The COMPACT team hasfocused mainly on CubeSats to date, and has collected technical,programmatic and cost data on dozens of flown CubeSatsmissions led by NASA, research labs, and universities. In late2019, the team released the first tool prototype which uses a nonparametricregression technique, k-Nearest Neighbors (KNN),on actual data from historical CubeSat missions to produceearly ballpark analogy-based cost estimates for new CubeSatconcepts. Since the KNN prototype was first released, theCOMPACT team has normalized 17 new missions to be addedto the model in COMPACT V2. COMPACT V2 also featureschanges to the KNN tool algorithm including the introduction ofPrincipal Component Analysis (PCA) to the model developmentprocess and changes to the input parameters which have madethe analogy results more intuitive and have improved modelperformance. This paper describes the current COMPACTKNN dataset, improvements made to the model in COMPACTV2, an assessment of current model performance, and a forwardlook at COMPACT’s planned future enhancements.

Hooke, Melissa↗

NASA NTRS2021

Future SAR Imaging Systems: Goals, Plans, Challenges and Opportunities

Synthetic Aperture Radar (SAR) Earth observation data are becoming increasingly ubiquitous as new spaceborne systems become operational and their data are made available to scientists and applications users. The characteristic of active sensors like SAR to be able to observe Earth independent of weather or solar illumination, coupled with regular data acquisition, fosters reliability and encourages the investment in algorithm and product development toward a beneficial result. As SAR systems typically contain proprietary or nationally important technologies, civilian SAR systems are typically developed with a national focus, or in the case of the European Union, with the Union’s focus. As a result, when viewed from a global perspective, SAR programs can be generally viewed as independent developments, each with their own requirements, schedules, development approaches, and data policies. At the same time, these systems can be expensive, and particularly in an era of increasingly open data policies, coordination of programs could reduce redundancy in observations, increase sampling density and measurement diversity, and improve dependability of data streams in the long term. Since 2018, agency representatives from NASA, ESA, DLR, JAXA, ISRO, ASI, and CONAE have been evaluating the possibilities for programmatic and technical coordination of future SAR systems, data sharing, and scientific exploitation. In this paper, we describe the work in discovering trends and possibilities associated with flight systems, by evaluating current and future plans for SAR systems around the world, and identifying opportunities for coordination.

Zink, Manfred↗

NASA NTRS2021

System Analysis and Generative Design for Icenode, a Buoyant Vehicle for Measuring Melt Rate Under Ice Shelves

Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean, and their collapse could trigger a meter or more of global sea level rise by the end of the century. Current state-of-the-art predictions for ice shelf behavior in a warming climate have large uncertainty, significantly hindered by a lack of in situ melt rate observations under ice shelves, especially near grounding zones. IceNode is a novel robotic vehicle under development at the NASA Jet Propulsion Laboratory to acquire such measurements, but presents a complex design problem owing to the large design space and conflicting performance requirements. Evaluating a preliminary design requires several types of tedious analysis which prevents rapid iteration and exploration of the design space. We present the implementation of a custom system analysis framework which was used to automate analysis such as resource budgeting, mission simulation, mass and buoyancy balancing, and static landing stability analysis of a given design configuration. Using this framework, we selected parameters which had the largest effects on design success and conducted a generative design study in which we programmatically varied a handful of parameters to generate 1540 different design candidates and test them across 160 different environmental scenarios. The custom built system analysis tool enabled rapid development of critical analysis and automated exploration of a large design space to guide preliminary design of the IceNode vehicle.

Gebara, Christine↗

NASA NTRS2021

Developing Compelling and Science-Focused Mission Concepts for NASA Competed Mission Proposals

Since the mid 1990s, NASA has used a competitive selection process to initiate new mission concepts. These competed missions are motivated by high-value science with low implementation and cost risks. Selectable mission concepts require highly focused science approaches—delivering high quality science “on a budget.” Prospective Principal Investigators (PIs) need to convince NASA that the science they are proposing is both compelling and has high programmatic value. Unfortunately, many of these PIs are not trained in the communication skills needed for “selling” a mission concept to stakeholders. This requires a proposal that presents an investigation in an accessible, relatable, authentic, and believable way. It is the onus of the proposing PI to convince NASA of the value of their particular science concept in a competitive environment, addressing any pre-conceived notions reviewers might have about the concept’s value, complexities, urgency, and other impediments to understanding.The importance of this can be seen in the NASA-sponsored PI Launchpad (Nov 2019 & Jun 2021)1 which was designed to help early career scientists understand the skills, methods, processes, and resources needed to develop compelling science mission concepts; as well as in JPL’s update to their concept maturity model (Jan 2020) which included the addition of Story and Strategy dimensions. This paper provides guidance to assist prospective PIs in developing compelling, science-focused mission concepts. It will provide direction for communicating concepts more clearly to make science objectives more relatable to both reviewers and a broader audience, improving the probability of selection.

Ziemer, John↗

NASA NTRS2022

Simulating Mars: Enabling Testing of the Perseverance Rover Sampling and Caching Subsystem on Earth

The development of the Sampling and Caching Subsystem (SCS) on the JPL Perseverance Rover lies at the intersection of testing, robotics, and geology. The SCS team established three primary system test campaigns and venues to aid in the development of SCS through verification and validation testing – Qualification Model Dirty Testing (QMDT) to provide a venue for testing in a Martian environment, Vehicle System Testbed (VSTB) for testing while integrated with the mobility subsystem on Martian-like terrain, and the Flight Software Testbed (FSWTB) for conducting tests using the flight motor controllers and software system on a hexapod which had the ability to simulate rover tilt. Each venue contributed a vital piece to the SCS building blocks. However, the QMDT venue operating within a 10-ft diameter Thermal Vacuum chamber to simulate Martian environment provided a sui generis opportunity to fine tune the entire sampling and caching process while building the team’s knowledge base about rock drillability, system life, and target selection. On Earth, because Martian rocks are not readily available, the development team must utilize geoanalogs to the rocks and regolith on Mars. Geologists on the team helped establish a set of standard rock types to use for Mars missions, like Basalt, Sandstone, Mudstone, Gypsum, and other related geoanalogs. These geoanalogs are characterized with a standard suite of tests for density, compressibility, and other characteristics to categorize potential drillability. This concept of drillability is what links the geoanalogs on Earth to the samples we collect on Mars. With the simulant characteristics defined, these geoanalog rocks are ready to be drilled into as we do on the Martian surface. A key aspect of interacting with the surface on Mars is rock target identification and selection. The Perseverance robotic system uses the on-board cameras, instrumentation, and software to collect enough information to identify potential scientific targets. With the targets identified, SCS can place the Corer and abrade the surface or collect a sample. For a ground test activity like QMDT, the test team did not have all of the camera and instrumentation systems that the rover does, so the team developed ground test equivalents to process a rock, build a target map, and define the target. The team constructed a Rock Scanning Station to build a 3D point cloud of the rock. This point cloud was then processed and evaluated with predefined and programmed criteria in a Target Downselect Tool. A primary output of the Target Downselect Tool is a defined target that can be uploaded directly to the robotic software system to simulate and build the robotic sequences used in tests. With these insights and programmatic definition of targets, the QMDT test team was able to make the same decisions that the Perseverance surface operations team does. In addition, valuable lessons learned from developing the target selection ground tools and using them were implemented into the tools used for surface operations.

Kim, Junggon↗

NASA NTRSDate not supplied

Hourly Stream Heights – A Short-term Deep Learning Prediction Model

River flooding can have a detrimental impact on a community by causing loss of life, loss or damage to property, and damage to infrastructure. Having the capability to forecast flooding events can prevent the loss of life and mitigate damage to property. A programmatic, data-driven approach using deep learning to forecast a stream’s gauge height every 6 hours has been developed by NASA’s Short-term Prediction Research and Transition Center (SPoRT) and is currently in operation. Based on our end user engagement and feedback, this medium-term product has been successfully adopted by several National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC). SPoRT is currently researching and developing a short-term hourly deep learning model which will be useful in forecasting flash flood events.

Michael Antia↗

NASA NTRSDate not supplied

Mitigations Against Counterfeit Electronic Parts in NASA Missions

Traditionally, electronics used in NASA missions, especially ones that have a very low risk tolerance, have been restricted to using parts produced by manufacturers certified as “space qualified” by the Defense Logistics Agency (DLA). The numerous benefits of this requirement include a standard part numbering system, strict traceability requirements, the designing, manufacturing and testing of devices to ensure reliable operation, and a government agency to independently certify that all requirements are met. This system also has shortcomings that compel the use of commercial electronics, which in turn have their own benefits and shortcomings. Dr. Majewicz will give his perspective on risks due to the use of commercial electronics in flight hardware, specifically in relation to the counterfeit threat, and NASA programmatic requirements that reduce this risk.

counterfeit↗