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This is the status report for the Game Changing Development (GCD) Regenerative Fuel Cell (RFC) project to be presented at the fiscal year 2020 (FY20) GCD Annual Review. Project to develop hydrogen/oxygen RFC energy storage technology from TRL 3 to TRL 5 for lunar surface applications.
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This project concerns construction of an orbital and ground resource network that provides Position, Navigation, and Timing (PNT) services for lunar surface operations. While functionally similar to Global Navigation Satellite Systems (GNSS) for Earth, this system will instead build an automated PNT framework utilizing limited infrastructure on board orbiting assets along with a controlled number of highly accurate assets, or anchor nodes. The goal is to use advanced algorithms to autonomously coordinate on demand or as needed asset participation to achieve orbit determination and time synchronization of accuracy sufficient for end-user localization. Other proposed lunar PNT solutions, such as weak signal GPS, will not meet many mission localization requirements without additional user INS augmentation and a dedicated GNSS constellation would require prohibitive infrastructure development. This proposed effort, the Lunar Autonomous Positioning System (LAPS), instead offers a design that could be deployed in the near term and is facilitated by accessible hardware technology.
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The SMART (Scalable Mobile Autonomous Rocket engine Test) testbed system initiative at SSC was conceived to attempt to address many of the principal cost drivers in developing and maintaining a rocket engine test facility. The system (optimized to test engines and components generating up to 10K lbf nominal thrust) is serving as a testbed for innovative technologies and processes to provide lower cost test services with a rapid test cadence and expedient turnaround times. The system can also potentially be used as a testbed to test other related technologies relevant to surface situations (e.g., moon, Mars associated with crogenic fluid management, engine/component testing, autonomous operations, etc.). This FY20 CIF project, being conducted as part of the SMART testbed system, is developing and testing a propellant delivery system via electrically driven centrifugal pumps (obviating dependence upon Multi-Layer Pressure Vessels) with configuration and operation by a minimal number of personnel. During FY20 the team identified the requirements and worked with P3 and Masten Space Systems to develop the long lead (9 months after receipt of order) items, the 400 VDC pumps, for delivery in mid FY21. Since control of the 400 VDC pump motor is not well developed the team has established heuristics to control flows in LN2 at off nominal shaft speeds to allow deep throttling of the pump in flow test scenarios. Various test scenarios including nominal i.e. high flow high pressure, high flow low pressure, low flow high pressure, low flow low pressure, minimum throttle step change, and low inlet pressure cavitation testing were developed ahead of the anticipated hardware delivery and test. FY20 COVID Stage 3 conditions restricted access to the center and hindered lab work, so efforts focused on the system design and testing plans along with the project procurement paperwork for the hardware... now with its anticipated delivery in spring FY21. Some limited access to the center is expected by spring/summer FY21 for the continuing second year (FY21) CIF project effort meant to be focused upon system integration and initial testing.
State-of-the-art technology like additive manufacturing has not yet been thoroughly applied to fluid control valves. Many valve manufacturing issues such as flow passage machining, deep hole boring, and bi-metallic joining could successfully be addressed with this new technology. The goal of this project was to apply additive manufacturing technology specifically to the problems that fluid control valves present. In order to properly apply additive manufacturing processes to the design of control valves, the plan was to manufacture samples using metals compatible with low temperature, high pressure oxygen service and then tested. While vetting this project with manufacturers, it was determined that the materials chosen were not ideal for additive manufacturing due to their high reflectivity. Therefore, materials for this project were currently undergoing reevaluation and materials that are better suited for additive manufacturing may be substituted. During FY20, additional alloys were selected that would be both appropriate for high pressure oxygen environments as well appropriate for additive manufacturing processes. However, final bid for manufacturing was six times larger than the available funding for this project. Based on previous additive manufacturing projects, the scope and funding for this project were appropriate, but a suitable manufacturer willing to provide a reasonable proposal, was not found. Therefore, funds were de-obligated for this project, and then re-obligated to augment another FY20 CIF project.
The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.
As part of the FY20 X-Hab Challenge, BLiSS sought to create an Augmented Reality (AR) toolkit to help with analog field trek operations under the supervision of the Solar System Exploration Research Virtual Institute (SSERVI). These treks are operational and technical demonstrations at space-like destinations on Earth to test current extra-vehicular activity (EVA) techniques. While BLiSS as an organization has experience studying operational tasks such as this, it has never developed AR software at this scale. For that reason, another team at the University was brought on to work in parallel. The Collaborative Lab for Advancing Work in Space (CLAWS) is a veteran group of the NASA Spacesuit User Interface Technologies for Students (SUITS) challenge in which Hololens displays for astronauts are created within a year. The operational and technological pairing was ideally suited for tackling this problem. The team divided its responsibilities so that BLiSS would handle the research required to shape the project. As this deliverable had an end user, it was decided that interviewing these field geologists and operations specialists would provide the best insight. These interviews paired with literature review would reveal niche applications for AR that remained within feasible bounds. These science-driven EVAs in unknown terrain require more flexible tools than the current generation of EVA assistants. Rather than focus on sequential instructions, there instead needs to be a broad toolkit that's only called upon in specific instances. This AR Toolkit for Lunar Astronauts and Scientists (ATLAS) became the development goal of the project: create a non-intrusive assembly of tools that could be accessed in AR on the field. The current ATLAS design makes use of a geospatially and temporally annotated eld note system called GeoNotes. This allows for data to be collected and coordinated in a way that's synchronized across time, space, and different users. A Mission Control Center (MCC) and Mobile Support Equipment (MSE) were all needed to transport the AR headset into the field with the user. A network infrastructure was designed and set up within the University to enable this functionality. The software is based on a Protocol-Module structure that allows for modular development of each capability. A Protocol Manager coordinates different protocols that make use of modules. Each module tackles a different individual task while the protocol puts each one to use. The protocol manager coordinates when these are called to be used. This software is hosted on a head-mounted display (HMD) with the MCC acting as support from afar. While the software would be unit-tested at each level and each hardware component verified, a final demonstration would serve to prove the system's capabilities: an analog field trek. The team would prepare to support a user in a remote location from the MCC back at the University. A local area near campus would be tested before going out to do sample field geology further away. This unfortunately became impossible with the arrival of COVID-19. Access to all of the facilities to complete the project as planned were shut down. Our team was scattered across the globe and forced to complete the rest virtually. Adjustments were made to produce a small virtual concept in Adobe XD in the meantime. Even digital surveys were created based on the NASA task-load index (TLX) originally intended for testing actual users. The goal shifted towards completing software and getting feedback on the user interfaces (UI) and user experiences (UX). This team has reformed in response to COVID and its focus has shifted to what can be done remotely. There is still an intention to finish the original deliverable described in this report. The work has been expanded beyond the original X-Hab challenge and has instead become its own research e ort to be continued afterwards. This report collects the processes and knowledge gained from a year of studying and working at this problem with two teams. It should preserve it for the time until the world returns to normal and work can resume. CLAWS will be taking over full responsibility from that point forward, eventually surpassing the original needs of the project. While this document captures the work done towards an eventual end, the CLAWS team has written their own proposal alongside it. It outlines a new future for ATLAS beyond X-Hab, BLiSS, and hopefully beyond COVID-19. This project began as a vague goal hoping to place a new technology into the unique setting of exploration science. The project has since comfortably taken root and will hopefully bloom over the next year.
Auburn University FY20 X-Hab Final Report: Volume Optimization For Food Product During Deep Space Exploration
This is the one-page status report for the STMD Announcement for Collaborative Opportunity (ACO) Lunar Lander Fuel Cell (LLFC) task to be presented at the fiscal year 2020 (FY20) Game Changing Development (GCD) Annual Review.
This is the one-page status update for the Game Changing Development (GCD) Advanced Alkaline Electrolyzer (AAE) Broad Agency Announcement (BAA) contract with Teledyne Energy Systems, Inc (TESI) to be presented at the fiscal year 2020 (FY20) GCD Annual Review. This activity to develop and demonstrate a contamination-tolerant liquid water electrolyzer for use in in-situ resource utilization (ISRU) applications to generate gaseous hydrogen and oxygen.
This poster is a general description of Rotating Detonation Rocket Engine task under the GCD project. It includes a brief summary of accomplishments for FY20. The scope of this work is to develop computational tools and design rules for RDRE’s.
Crystal Ball is an application being developed that accesses multiple safety databases as a means to improve prediction of safety incidents. Year 1 was data integration, year 2 was predictive modeling, and then year 3(FY20), was the merging of those two prior year efforts into the final application, Crystal Ball (ssc.crystalball.insight.nasa.gov). Crystal Ball sits on the Insight platform (Insight is a NASA platform used to process, manage, integrate, analyze and visualize data at scale, insight.nasa.gov). InFY20, the project focus concentrated on the larger vision of Prediction of Safety Incidents using Crystal Ball as the data source. The Insight platform developer incorporated the predictive modeled data sets, and included a graphical user interface, resulting in a Dashboard for the Crystal Ball application; This application is a one-stop-shop for SMA employees working across data sets and provides a snapshot of current relative risk in different types of locations across the center. The ultimate goal is to have a tool that management can use to aid in decisions that are based on data already being collected. Ideally, the tool would highlight areas of increased risk for any given day. SMA will be conducting case studies to further refine the process of identifying higher areas of risk and potentially strategically direct resources where needed more. Our partners who leveraged funds for this project may consider use at other NASA organizations.
In FY19, this project developed a test method which employed heated ethane in the physical simulation of rocket plume aerodynamics. Based on this work, EUS partnered with significant leveraged funding. A portable, self-contained test bed was also fabricated to support validation of the new method. In FY20, this project focused on the formal validation of ethane’s performance as an aerodynamic simulant as well as the development of the analytical and hardware infrastructure required to carry out that task. The test bed was activated, a series of heated-ethane tests on one or more rocket/diffuser configurations were conducted, and characterizations of the performance of the ethane simulation compared to hot-fire were performed. A series of 127 tests were conducted on 13 aerodynamic configurations to evaluate ethane’s performance as a simulant. It was found that the new methodology can reproduce rocket plume flow fields with +/- 0.5% pressure error at ~3% physical scale. The result is a cost-per-test reduction of ~99% compared to the hot-fire subscale testing typically used to obtain equivalent data. Some initial efforts have taken place, and future efforts are being planned with LaRC supersonic retro propulsion and MSFC SLS aerodynamics teams, which are interested in using the ethane system to wind-tunnel test human-rated Mars lander retrorockets and SLS Booster. Details of the prototypical experiments were published as NASA TM 2020-5009122,and have been presented in JANNAF Virtual Conference paper, "New Developments in Retro propulsion Testing for Mars Entry, Descent and Landing".
In 2009, the Lunar Reconnaissance Orbiter (LRO)and Lunar Crater Observation and Sensing Satellite(LCROSS) provided definitive proof of water in the Lunar’s southern permanently shadowed region (PSR)[1]. Both the 2020 NASA Technology Taxonomy[2] and the Lunar Surface Innovation Initiative (LSII) team identified capability gaps in icy regolith transfer and reactor processing in Permanently Shadowed Region(PSR) environmental conditions. A screw conveyor dryer system operating from inside the PSR can continuously process water (and volatiles) for both breathable air and propellant. NASA’s Johnson Space Center (JSC) began development of a similar sub-system for Mars operation in 2017 and fabricated a unique breadboard test stand for validating the feasibility of this concept. This testing was postponed with the redirection of NASA’s mission from Mars to Moon. A JSC led trade study[3] in FY20 formulated a plan to leverage existing hardware to test concept feasibility, developed a lunar auger dryer sizing tool, and identified that both a physical flow and thermal model is required to develop an Engineering Development Unit (EDU) for environmental testing. Beginning in FY21, the Game Changing Development Program (GCDP) funded a three-year technology development project to increase the Technology Readiness Level (TRL) of the Lunar Auger Dryer ISRU (LADI) subsystem to TRL 5.
In 2009, the Lunar Reconnaissance Orbiter (LRO)and Lunar Crater Observation and Sensing Satellite(LCROSS) provided definitive proof of water in the Lunar’s southern permanently shadowed region (PSR)[1]. Both the 2020 NASA Technology Taxonomy[2] and the Lunar Surface Innovation Initiative (LSII) team identified capability gaps in icy regolith transfer and reactor processing in Permanently Shadowed Region(PSR) environmental conditions. A screw conveyor dryer system operating from inside the PSR can continuously process water (and volatiles) for both breathable air and propellant. NASA’s Johnson Space Center (JSC) began development of a similar sub-system for Mars operation in 2017 and fabricated a unique breadboard test stand for validating the feasibility of this concept. This testing was postponed with the redirection of NASA’s mission from Mars to Moon. A JSC led trade study[3] in FY20 formulated a plan to leverage existing hardware to test concept feasibility, developed a lunar auger dryer sizing tool, and identified that both a physical flow and thermal model is required to develop an Engineering Development Unit (EDU) for environmental testing. Beginning in FY21, the Game Changing Development Program (GCDP) funded a three-year technology development project to increase the Technology Readiness Level (TRL) of the Lunar Auger Dryer ISRU (LADI) subsystem to TRL 5.
Introduction: The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of planetary atmospheres. The GRAM upgrades are being developed by NASA Marshall Space Flight Center and NASA Langley Research Center. This presentation will provide details regarding the upgrades to the existing GRAMs, the development of new GRAMs, and the ongoing objectives, tasks, and milestones related to the GRAM upgrades funded by the NASA Science Mission Directorate (SMD). GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations. They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path. They are extensively used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. GRAMs are currently available for Earth, Mars, Venus, Neptune, Titan, and Uranus. Outer Planet GRAM Upgrade Status: Code Modernization. The outer planet GRAMs have been rearchitected from Fortran to a common object-oriented C++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the rearchitected legacy outer planet GRAMs (Neptune and Titan-GRAM) are straight conversions from the latest Fortran version. Model Upgrades. The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemeris has been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. In FY20, the GRAM project established a contract with Hampton University to develop empirical global models for Jupiter, Saturn, Uranus, Neptune, and Titan. Upgraded Outer Planet GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and added the rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manual are released with all GRAMs. New Outer Planet GRAM Releases. New GRAMs have been developed for Uranus and Jupiter. Uranus-GRAM is based on the NASA Ames Research Center (ARC) Uranus Atmospheric Model [1,2] and was released in GRAM Suite Version 1.2 in July 2021. Jupiter-GRAM is based on Galileo probe Atmospheric Structure Instrument (ASI) data from Seiff et al. [3] Saturn-GRAM is also under development. Both Jupiter and Saturn-GRAM will be released in future versions of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. Titan-GRAM atmosphere model upgrades will be included in the next phase of GRAM tasks. References: [1] Allen Jr., G.A. et al. (2014) 11th International Planetary Probe Workshop, Abstract #8023. [2] Allen Jr., G.A. et al. (2014) Workshop on the Study of the Ice Giant Planets, Abstract #2001. [3] Seiff, A. et al. (1998) JGR, 103, 22,857 -22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD.