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Kennedy Space Center (KSC) has been developing self healing materials and technologies. This project seeks to further develop self healing functionality in thin films for applications such as corrosion protective coatings, inflatable structures, space suit materials, and electrical wire insulation.
Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.
To investigate the pathways and kinetics of protein aggregation to allow accurate predictive modeling of the process and evaluation of potential inhibitors to prevalent diseases including cataract formation, chronic traumatic encephalopathy, Alzheimer's Disease, Parkinson's Disease and others.
Jenn Gustetic will talk about how NASA selects ideas and collaborates with the ecosystem to develop new technologies for NASA’s portfolio. She will explain how the organization encourages collaboration, co-invests to develop ideas from lab to market, and acts as the hub-and-spoke of the ecosystem. Jenn will also discuss NASA’s journey to break silos and work horizontally across programs. • Gathering and evaluating ideas internally and externally with diverse players – engaging communities, being the hub-and-spoke of the ecosystem, and encouraging collaboration through co-investments • Identify the best ideas, funding ideas from maturation to hand-off with fluctuating budgets, and managing IP and tech transfer • Balancing investments in projects that address NASA’s needs with higher risk ideas that may not have an obvious application • Transitioning ideas to the next phase – training people and teams and ensuring ownership • Developing a 3.0 innovation capacity strategy – disrupting silos, enabling the ecosystem to work seamlessly together, and taking people and leaders along
A new approach for assessing development risk associated with technology development projects is presented. The method represents technology evolution in terms of sector-specific discrete development stages. A Monte Carlo simulation is used to generate development probability distributions based on statistical models of the discrete transitions. Development risk is derived from the resulting probability distributions and specific program requirements. Two sample cases are discussed to illustrate the approach, a single rocket engine development and a three-technology space transportation portfolio.
As NASA continues to develop technology for spaceflight beyond low earth orbit, we must develop the right systems for sustaining human life on a long duration or planetary mission. Plasma arc gasification (PAG) is an energy efficient mechanism of waste management for power generation and synthetic gas(syngas) production.
NASA has developed a project plan to address issues related to UAS access to the NAS The plan is being formulated with inputs from our stakeholders. NASA will work with our stakeholders to develop ConOps and a national roadmap to determine key research technologies and policy issues to enable UAS access to the NAS. NASA will use ConOps and roadmap to either validate current NASA research investment areas and make any necessary changes to proposed UAS research portfolio. NASA will conduct integration and testing of key research areas to enable UAS access to the NAS. Use phase I to do detailed test planning for phase II Assist subelements with test planning Assist with documenting test objectives, data and facilities/infrastructure requirements, and detailed test planning Provide facilities/infrastructure to meet test requirements Provide interfaces between tools Develop, document, and execute data handling and dissemination plans Provide a test engineer to facilitate scheduling of facilities, support specific equipment and software needs, track schedule progress, and monitor changes to schedule Provide guidance for alternative facilities or equipment to mitigate risk associated with loss of availability Provide opportunities for subelements to gather data in relevant and increasingly complex environments
The project implements prognostics capabilities to predict when a component, system or subsystem will no longer meet desired functional or performance criteria, called the "end of life." The capability also provides an assessment of the "remaining useful life" of a hardware component.
NASA's Ultra Efficient Engine Technology (UEET) project features advanced aeropropulsion technologies that include highly loaded turbomachinery, an advanced low-NOx combustor, high-temperature materials, and advanced fan containment technology. A probabilistic system assessment is performed to evaluate the impact of these technologies on aircraft CO2 (or equivalent fuel burn) and NOx reductions. A 300-passenger aircraft, with two 396-kN thrust (85,000-lb) engines is chosen for the study. The results show that a large subsonic aircraft equipped with the current UEET technology portfolio has very high probabilities of meeting the UEET minimum success criteria for CO2 reduction (-12% from the baseline) and LTO (landing and takeoff) NOx reductions (-65% relative to the 1996 International Civil Aviation Organization rule).
Components in the aerospace industry must perform with accuracy and precision under extreme conditions, and surface contamination can be detrimental to the desired performance, especially in cases when the components come into contact with strong oxidizers such as liquid oxygen. Therefore, precision cleaning is an important part of a components preparation prior to utilization in aerospace applications. Current cleaning technologies employ a variety of cleaning agents, many of which are halogenated solvents that are either toxic or cause environmental damage. Thus, this project seeks to identify alternative precision cleaning solvents and technologies, including use of less harmful cleaning solvents, ultrasonic and megasonic agitation, low-pressure plasma cleaning techniques, and supercritical carbon dioxide extraction. Please review all data content found in the Public Data tab located at: https:techport.nasa.govview11697public
Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.
The National Aeronautics and Space Administration's (NASA) Advanced Space Transportation Program is a customer driven, focused technology program that supports the NASA Strategic Plan and considers future commercial space business projections. The initial cycle of the Advanced Space Transportation Program implementation planning was conducted from December 1995 through February 1996 and represented increased NASA emphasis on broad base technology development with the goal of dramatic reductions in the cost of space transportation. The second planning cycle, conducted in January and February 1997, updated the program implementation plan based on changes in the external environment, increased maturity of advanced concept studies, and current technology assessments. The program has taken a business-like approach to technology development with a balanced portfolio of near, medium, and long-term strategic targets. Strategic targets are influenced by Earth science, space science, and exploration objectives as well as commercial space markets. Commercial space markets include those that would be enhanced by lower cost transportation as well as potential markets resulting in major increases in space business induced by reductions in transportation cost. The program plan addresses earth-to-orbit space launch, earth orbit operations and deep space systems. It also addresses all critical transportation system elements; including structures, thermal protection systems, propulsion, avionics, and operations. As these technologies are matured, integrated technology flight experiments such as the X-33 and X-34 flight demonstrator programs support near-term (one to five years) development or operational decisions. The Advanced Space Transportation Program and the flight demonstrator programs combine business planning, ground-based technology demonstrations and flight demonstrations that will permit industry and NASA to commit to revolutionary new space transportation systems beginning at the turn of the century and continuing far into the future.
The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi‐criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.
Wireless Surface Acoustic Wave (SAW) Sensor is a new technology for obtaining multiple, real-time measurements under extreme environmental conditions. This project plans to develop a wireless multiplexed sensor system that uses SAW sensors, with no batteries or semiconductors, that are passive and rugged, can operate down to cryogenic temperatures and up to hundreds of degrees C, and can be used to sense a wide variety of parameters over reasonable distances (meters).
The Technology Advancing Partnership (TAP) Challenge will seek to foster innovation throughout the Center by allowing the KSC workforce to identify a specific technology idea that needs improvement and to then work with an external partner to develop that technology. This Challenge will enable competitive partnerships with outside entities that will increase the value by bringing leveraged resources. The selected proposal from the University of Florida will develop new lightweight technologies with radiation mitigation for spacecraft.
This project seeks to develop a small free flyer that can be used to safely and effectively prospect on an Asteroid while being controlled by the crew. This will enable the characterization of the Asteroid for the In Situ Resource Utilization (ISRU). Lava tubes can be explored remotely from the outside Asteroids can contain vast amounts of resources such as water for propellants and metals for feed stocks. Lava Tubes on Mars and the Moon may contain frozen volatile resources. Before the resources can be used, they must be found with a prospecting method. The NASA Agency Asteroid Grand Challenge seeks new ideas for Asteroid retrieval mission technologies for exploration and utilization of asteroids in a Distant Retrograde Orbit (DRO). This project will develop a small free flying platform that can be used to safely and effectively prospect on an Asteroid with limited autonomy while being controlled by the crew. This will enable the characterization of the Asteroid for ISRU. Lava tubes can be explored remotely from the outside as well using this same technology.
This project optimized the calorie content in a breakfast meal replacement bar for the Advanced Food Technology group. Use of multivariable optimization yielded the highest weight savings possible while simultaneously matching NASA Human Standards nutritional guidelines. The scope of this research included the study of shelf-life indicators such as water activity, moisture content, and texture analysis. Key metrics indicate higher protein content, higher caloric density, and greater mass savings as a result of the reformulation process. The optimization performed for this study demonstrated wide application to other food bars in the Advanced Food Technology portfolio. Recommendations for future work include shelf life studies on bar hardening and overall acceptability data over increased time frames and temperature fluctuation scenarios.
Wastewater processing systems for space generate residual brine that contains water and salts that could be recovered to life support consumables. The project assessed the use of ion-exchange resins to selectively remove salts from wastewater treatment brines. The resins were then regenerated for additional use. The intention would be to generate a Na/K and CI rich or purified brine that would then be processed into high value chemicals, such as acids, bases, and/or bleach.