Results for “policy solutions” — Page 3

NASA NTRS2022

Coordinating Innovative Technology Development at NASA

Innovation requires continually using and coordinating new knowledge on the current, anticipated, and even unanticipated developments in a given area. You do not want to simply repeat what others are doing, so you must remain aware of other activities in your area of interest. You also have to maintain awareness of where the cutting edge is, because the limit of what’s possible is continually pushed and redefined. Finally, “smart” innovation involves not only seeking innovative solutions to anticipated problems but also being prepared to respond to unanticipated problems or even unanticipated solutions. As the leading U.S. agency for aeronautics development, cutting-edge scientific discovery, and human space exploration, the National Aeronautics and Space Administration (NASA) must remain at the forefront of innovation. NASA enables innovation in technology development through a coordinated effort among its internal mission directorates and its partners in government, commercial, and academic sectors. As an independent office, NASA’s Office of Technology, Policy, and Strategy (OTPS) provides the coordination effort necessary to ensure innovation. OTPS uses and coordinates inputs from a broad community of experts. With these inputs, OTPS identifies and communicates shared needs and opportunities for partnership. OTPS provides strategic advice to synergize and ultimately facilitate innovative technology development both within and outside NASA. This paper summarizes NASA’s approach to ensuring innovation through a deliberately coordinated effort. In this paper, we first provide examples of how this coordination has enabled innovative technology development. We then describe how NASA’s mission directorates use input from a broad community of experts to inform innovative technology development. Finally, we describe OTPS’s role in coordinating inputs from both internal and external experts, including standardizing communication about technology developments, facilitating conversations with external partners on shared technology needs and solutions, and advising on strategies and policies to ensure innovative technology development. This paper can help members of the aerospace community understand their role in NASA’s innovation efforts and how to engage OTPS in conversations about potential partnership.

Erica Rodgers↗

NASA NTRSDate not supplied

Coordinating Innovative Technology Development at NASA

Innovation requires continually using and coordinating new knowledge on the current, anticipated, and even unanticipated developments in a given area. You do not want to simply repeat what others are doing, so you must remain aware of other activities in your area of interest. You also have to maintain awareness of where the cutting edge is, because the limit of what’s possible is continually pushed and redefined. Finally, “smart” innovation involves not only seeking innovative solutions to anticipated problems but also being prepared to respond to unanticipated problems or even unanticipated solutions. As the leading U.S. agency for aeronautics development, cutting-edge scientific discovery, and human space exploration, the National Aeronautics and Space Administration (NASA) must remain at the forefront of innovation. NASA enables innovation in technology development through a coordinated effort among its internal mission directorates and its partners in government, commercial, and academic sectors. As an independent office, NASA’s Office of Technology, Policy, and Strategy (OTPS) provides the coordination effort necessary to ensure innovation. OTPS uses and coordinates inputs from a broad community of experts. With these inputs, OTPS identifies and communicates shared needs and opportunities for partnership. OTPS provides strategic advice to synergize and ultimately facilitate innovative technology development both within and outside NASA. This paper summarizes NASA’s approach to ensuring innovation through a deliberately coordinated effort. In this paper, we first provide examples of how this coordination has enabled innovative technology development. We then describe how NASA’s mission directorates use input from a broad community of experts to inform innovative technology development. Finally, we describe OTPS’s role in coordinating inputs from both internal and external experts, including standardizing communication about technology developments, facilitating conversations with external partners on shared technology needs and solutions, and advising on strategies and policies to ensure innovative technology development. This paper can help members of the aerospace community understand their role in NASA’s innovation efforts and how to engage OTPS in conversations about potential partnership.

Erica Rodgers↗

NASA NTRSDate not supplied

Coordinating Innovative Technology Development at NASA

Innovation requires continually using and coordinating new knowledge on the current, anticipated, and even unanticipated developments in a given area. You do not want to simply repeat what others are doing, so you must remain aware of other activities in your area of interest. You also have to maintain awareness of where the cutting edge is, because the limit of what’s possible is continually pushed and redefined. Finally, “smart” innovation involves not only seeking innovative solutions to anticipated problems but also being prepared to respond to unanticipated problems or even unanticipated solutions. As the leading U.S. agency for aeronautics development, cutting-edge scientific discovery, and human space exploration, the National Aeronautics and Space Administration (NASA) must remain at the forefront of innovation. NASA enables innovation in technology development through a coordinated effort among its internal mission directorates and its partners in government, commercial, and academic sectors. As an independent office, NASA’s Office of Technology, Policy, and Strategy (OTPS) provides the coordination effort necessary to ensure innovation. OTPS uses and coordinates inputs from a broad community of experts. With these inputs, OTPS identifies and communicates shared needs and opportunities for partnership. OTPS provides strategic advice to synergize and ultimately facilitate innovative technology development both within and outside NASA. This paper summarizes NASA’s approach to ensuring innovation through a deliberately coordinated effort. In this paper, we first provide examples of how this coordination has enabled innovative technology development. We then describe how NASA’s mission directorates use input from a broad community of experts to inform innovative technology development. Finally, we describe OTPS’s role in coordinating inputs from both internal and external experts, including standardizing communication about technology developments, facilitating conversations with external partners on shared technology needs and solutions, and advising on strategies and policies to ensure innovative technology development. This paper can help members of the aerospace community understand their role in NASA’s innovation efforts and how to engage OTPS in conversations about potential partnership.

Erica Rodgers↗

NASA NTRSDate not supplied

In-Space Inspection Needs: Opportunities for advanced NDE tools such as x-ray CT for additively manufactured parts, in-situ resource utilization, geological applications, and more

It is now 50 years since the first human presence on the surface of the Moon and as we strive to return with women and men in the next few years, we embrace new technical challenges, goals, and innovative solutions to address 21st century objectives. These new ambitions carry fresh challenges and risks, with the field of NDE playing an increasingly more relevant role towards meeting these essential goals. In recent years, more advanced NDE tools have triggered a rapid expansion of applications for the space industry. In particular, x-ray Computed Tomography (CT) has proven to be a trusted and powerful asset for spaceflight hardware inspection, as well as applied geotechnical analysis for natural materials (e.g., rocks, soils) for NASA and across industry. However, such methods have yet to be extended to “deep space” applications such as those that are now part of the US National Space Policy Directive (SPD-1) and the accelerated push to return humans to the Moon (i.e., Artemis). For this reason, advancing these powerful Earth-based laboratory methods via new technologies, integrated computational solutions, and creative engineering approaches is directly aligned with national space policies, as well as with multiple NASA Strategic Plan priorities. The use of x-ray CT at scales as fine as a few microns or smaller can identify spacecraft part failure modes relevant to quality assurance for flight hardware and AM parts such as those recently developed for ISS. This technology could also identify valuable metallic phases within geological materials (i.e., rocks or drill cores), enabling resource-relevant triage of samples for In-Situ Resource Utilization (ISRU) and high science value sample return to Earth laboratories. There is also significant application for 3D imaging tools for medical use such as inspecting protective gear as well as bone density degradation studies which are critical in establishing a sustained presence in space. Timing for development of these tools for space use is advantageous as we prepare for new opportunities in the next few years and recognize recent commercial technology advancements which make it feasible. Moreover, as NASA strives to take full advantage of developments in AM technologies, including In-Space Manufacturing (ISM), it is widely recognized that NDE tools such as CT will play an essential role in acceptance of these parts for widespread use. New in-space 3D inspection tools with complimentary technology such as AI-based automated feature recognition (accelerated by machine learning), rapid compositional analysis, and advanced sample manipulation, would be a game-changing step toward a new class of crew-based laboratory sensors once human outposts on the Moon are established.

In-Space Non-Destructive Evaluation↗

NASA NTRS2006

Autonomous Information Unit: Why Making Data Smart Can also Make Data Secured?

In this paper, we introduce a new fine-grain distributed information protection mechanism which can self-protect, self-discover, self-organize, and self-manage. In our approach, we decompose data into smaller pieces and provide individualized protection. We also provide a policy control mechanism to allow 'smart' access control and context based re-assembly of the decomposed data. By combining smart policy with individually protected data, we are able to provide better protection of sensitive information and achieve more flexible access during emergency conditions. As a result, this new fine-grain protection mechanism can enable us to achieve better solutions for problems such as distributed information protection and identity theft.

data security↗

NASA NTRS2022

Unlocking the Spacecraft and Human Habitat Microbiome to Enable the Next Generation of Space Exploration

Planetary protection is the discipline that prevents harmful contamination of the solar system during exploration activities. The current international guidelines and NASA policy addressing biological contamination on spacecraft surfaces contains prescriptive guidelines of spore requirements (e.g., 300 spores/m2, 5×105 spores per spacecraft) applicable to spacecraft bound for Mars. To verify these requirements spacecraft engineers sample spacecraft surfaces throughout the assembly, test and launch operations phase of the mission using damp water cotton swabs and polyester wipes. After sampling, the potential biological contamination is enumerated using a series of traditional microbiology techniques to include sonication, heat shocking at 80°C for 15min to select for spores, and growth on tryptic soy agar at 32°C for 72 hours. To enable crewed missions to Mars and robotic exploration of the Ocean Worlds a risk informed decision making / performance-based approach to assess biological contamination offers a promising solution in the trade space. Recognizing the need for a performance-based approach, NASA’s new Planetary Protection policies now incorporate the agility for missions to be able to leverage a performance or prescriptive approach. One of top contenders in the option space is a coupled quantitative, descriptive and functional based approach to be able to assess the quantity, types and capabilities of the biological contamination present on spacecraft surfaces. A tailored, mission by mission assurance case could then be formulated by building an argument around the target body, projected capabilities surrounding the types of organisms their potential for survival and proliferation, and ability to be transported on the target body to contaminate an area of biological interest. A performance-based requirement would then be used to demonstrate the mission’s compliance in protecting the planetary environment safety objectives. This symposium talk will showcase the background and need case for NASA to develop such a capability as well as provide an update on the efforts underway in developing a transparent and responsible performance-based approach to biological contamination assessments on spacecraft surfaces.

Habitat Microbiome↗

NASA NTRSDate not supplied

A Review of Existing Policies Affecting the Jettison of Waste in Low Earth Orbit and Deep Space

The management of waste generated onboard spacecraft during future long-duration deep-space missions will require different solutions from those currently implemented on the International Space Station which consist exclusively of collecting, storing, and returning the waste to Earth. Alternative options for managing spacecraft waste are to process it for recycling and recovery of resources, and to jettison it overboard in a solid form (such as a compacted tile) or in a gaseous form after torrefaction or other trash-to-gas technologies. The waste generated during a deep-space mission is derived mainly from spacecraft logistics supplies, food and beverage residues, personal or scientific items used by the crew, human metabolic waste, and unused spare components. Uncontained and unprocessed trash is a potential health hazard and a habitat volume liability, which makes onboard long-term storage an inefficient and non-optimal option. However, the jettison of solid, processed waste appears to be an effective solution for crewed deep-space missions, leading not only to volume reduction and habitat safening, but also to considerable mass savings in the spacecraft’s propulsion system. However, the disposal of trash overboard also creates a navigation hazard for spacecraft and the potential risk of contamination of planetary bodies, interfering with the search for life. This paper investigates the requirements covered by existing policies that could affect the jettison concept of operations and system design.

Jettison↗

NASA NTRS2006

A robust model predictive control algorithm for uncertain nonlinear systems that guarantees resolvability

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.

feed - forward↗

NASA NTRS2005

Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

model predictive control algorithm (MPC)↗

NASA NTRS2022

SatNet: A Benchmark for Satellite Scheduling Optimization

Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.

Wilson, Brian↗

NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗

NASA NTRS1996

Flexibility and Performance of Parallel File Systems

As we gain experience with parallel file systems, it becomes increasingly clear that a single solution does not suit all applications. For example, it appears to be impossible to find a single appropriate interface, caching policy, file structure, or disk-management strategy. Furthermore, the proliferation of file-system interfaces and abstractions make applications difficult to port. We propose that the traditional functionality of parallel file systems be separated into two components: a fixed core that is standard on all platforms, encapsulating only primitive abstractions and interfaces, and a set of high-level libraries to provide a variety of abstractions and application-programmer interfaces (API's). We present our current and next-generation file systems as examples of this structure. Their features, such as a three-dimensional file structure, strided read and write interfaces, and I/O-node programs, are specifically designed with the flexibility and performance necessary to support a wide range of applications.

Kotz, David↗

NASA NTRS2011

A Simulation Based Approach to Optimize Berth Throughput Under Uncertainty at Marine Container Terminals

Berth scheduling is a critical function at marine container terminals and determining the best berth schedule depends on several factors including the type and function of the port, size of the port, location, nearby competition, and type of contractual agreement between the terminal and the carriers. In this paper we formulate the berth scheduling problem as a bi-objective mixed-integer problem with the objective to maximize customer satisfaction and reliability of the berth schedule under the assumption that vessel handling times are stochastic parameters following a discrete and known probability distribution. A combination of an exact algorithm, a Genetic Algorithms based heuristic and a simulation post-Pareto analysis is proposed as the solution approach to the resulting problem. Based on a number of experiments it is concluded that the proposed berth scheduling policy outperforms the berth scheduling policy where reliability is not considered.

Golias, Mihalis M.↗

NASA NTRS2013

NIRPS - Solutions Facilitator Team Overview and Accomplishments

National Institute for Rocket Propulsion Systems (NIRPS) purpose is to help preserve and align government and private rocket propulsion capabilities to meet present and future US commercial, civil, and defense needs, while providing authoritative insight and recommendations to National decisional authorities. Stewardship: Monitor and analyze the state of the industry in order to formulate and recommend National Policy options and strategies that promote a healthy industrial base and ensure best-value for the American taxpayer. Technology: Identify technology needs and recommend technology insertions by leading roadmap assessments and actively participating in program formulation activities. Solutions Facilitator/Provider: Maintain relationships and awareness across the Government, industry and academia, to align available capacity with emerging demand.

Brown, Thomas M., III↗

NASA NTRS1989

Analysis of a turning point problem in flight trajectory optimization

The optimal control policy for the aeroglide portion of the minimum fuel, orbital plane change problem for maneuvering entry vehicles is reduced to the solution of a turning point problem for the bank angle control. For this problem a turning point occurs at the minimum altitude of the flight, when the flight path angle equals zero. The turning point separates the bank angle control into two outer solutions that are valid away from the turning point. In a neighborhood of the turning point, where the bank angle changes rapidly, an inner solution is developed and matched with the two outer solutions. An asymptotic analysis of the turning point problem is given, and an analytic example is provided to illustrate the construction of the bank angle control.

Gracey, C.↗

NASA NTRS1994

Computational Fluid Dynamics at NASA Ames Research Center

Computational fluid dynamics (CFD) is beginning to play a major role in the aircraft industry of the United States because of the realization that CFD can be a new and effective design tool and thus could provide a company with a competitive advantage. It is also playing a significant role in research institutions, both governmental and academic, as a tool for researching new fluid physics, as well as supplementing and complementing experimental testing. In this presentation, some of the progress made to date in CFD at NASA Ames will be reviewed. The presentation addresses the status of CFD in terms of methods, examples of CFD solutions, and computer technology. In addition, the role CFD will play in supporting the revolutionary goals set forth by the Aeronautical Policy Review Committee established by the Office of Science and Technology Policy is noted. The need for validated CFD tools is also briefly discussed.

Kutler, Paul↗

NASA NTRS1997

Conflict Detection and Resolution for Future Air Transportation Management

With a Free Flight policy, the emphasis for air traffic control is shifting from active control to passive air traffic management with a policy of intervention by exception. Aircraft will be allowed to fly user preferred routes, as long as safety Alert Zones are not violated. If there is a potential conflict, two (or more) aircraft must be able to arrive at a solution for conflict resolution without controller intervention. Thus, decision aid tools are needed in Free Flight to detect and resolve conflicts, and several problems must be solved to develop such tools. In this report, we analyze and solve problems of proximity management, conflict detection, and conflict resolution under a Free Flight policy. For proximity management, we establish a system based on Delaunay Triangulations of aircraft at constant flight levels. Such a system provides a means for analyzing the neighbor relationships between aircraft and the nearby free space around air traffic which can be utilized later in conflict resolution. For conflict detection, we perform both 2-dimensional and 3-dimensional analyses based on the penetration of the Protected Airspace Zone. Both deterministic and non-deterministic analyses are performed. We investigate several types of conflict warnings including tactical warnings prior to penetrating the Protected Airspace Zone, methods based on the reachability overlap of both aircraft, and conflict probability maps to establish strategic Alert Zones around aircraft.

Krozel, Jimmy↗

NASA NTRS1998

Preliminary Work for Examining the Scalability of Reinforcement Learning

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

Clouse, Jeff↗