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At least 163 records · Page 9

Holistic and Pragmatic Standards Processes Enable Interdisciplinary Science

Open, interdisciplinary science inevitably relies heavily on standards. Standards are those often unseen agreements that we take for granted when systems and processes are working fine. Yet standards work is perpetual, laborious, and sometimes contentious, especially for standards to work across diverse disciplines. Standards development, maintenance, and implementation is a complex, ongoing socio-technical process. NASA has developed a progressively open science policy and strategy that calls for the establishment of a data standards process reaching across the five diverse divisions of the Science Mission Directorate. This is a delicate exercise. We, therefore, seek to apply a holistic yet pragmatic approach to developing and maintaining a standards process. We adopt an ecological philosophy that focuses on the interactions within the data ecosystem and how standards facilitate those interactions. We couple high-level analysis with on the ground experimentation. We began by 1) mapping information ecosystem components (e.g. data centers, missions, services, protocols, users), 2)establishing how the components interact (e.g. sharing (meta)data, funding, personnel exchange), and 3) modelling system dynamics (e.g. creation of products from multiple data centers, redundant processes, shared services). The goal is to apply understanding of the ecosystem to real world applications (e.g. planning a new mission, implementing new policy requirements, improving process efficiency, etc.). We have also conducted studies of historical standardization efforts, documenting lessons learned and cautionary tales. We then contrast this more abstract work with real examples. We reviewed and assessed multiple existing standards development processes both within and external to NASA. We now work to implement an initial test process which can be further optimized. We seek to define a consistent approach for assigning persistent identifiers for research objects, especially for the purposes of citation. The experience from this relatively ‘simple’ test case adds a pragmatic perspective on how researchers and engineers actually work. This presentation will review the details of this methodology, our initial findings, and how they might apply to other interdisciplinary standardization efforts.

Mark A Parsons↗

Survey of On-Orbit Sleep Quality: Short-Duration Flyers

The NASA Human Research Program (HRP) Behavioral Health and Performance Element (BHP), in conjunction with the NASA Space Medicine Division, is currently completing the largest systematic, subjective assessment of shuttle astronauts sleep behaviors and sleep quality on Earth, during training periods, and during space flight missions. Since July 2009, a total of 66 astronauts have completed a secure online survey regarding specific sleep strategies, crew policies, and mitigation effectiveness. In addition to the survey, each astronaut participant met individually with trained BHP and SD representatives for a structured, follow-up interview. Data are currently being assessed and the study s principal investigator will be providing some preliminary findings at the Investigators Workshop. Additional analyses will be conducted in the following months to examine predictors of optimal sleep in space, and to evaluate the differences in countermeasure effectiveness between groups based on their sleep experience on the ground and on orbit. A revised survey for a subsequent investigation on the experiences of long-duration flyers will be developed in the Spring and implemented in the Summer of 2010. Findings from both of these investigations will inform countermeasure strategies for astronauts, medical operations, and habitat designers for future exploration missions, as well as upcoming shuttle and ISS missions.

Locke, J.↗

Marshall Application Realignment System (MARS) Architecture

The Marshall Application Realignment System (MARS) Architecture project was established to meet the certification requirements of the Department of Defense Architecture Framework (DoDAF) V2.0 Federal Enterprise Architecture Certification (FEAC) Institute program and to provide added value to the Marshall Space Flight Center (MSFC) Application Portfolio Management process. The MARS Architecture aims to: (1) address the NASA MSFC Chief Information Officer (CIO) strategic initiative to improve Application Portfolio Management (APM) by optimizing investments and improving portfolio performance, and (2) develop a decision-aiding capability by which applications registered within the MSFC application portfolio can be analyzed and considered for retirement or decommission. The MARS Architecture describes a to-be target capability that supports application portfolio analysis against scoring measures (based on value) and overall portfolio performance objectives (based on enterprise needs and policies). This scoring and decision-aiding capability supports the process by which MSFC application investments are realigned or retired from the application portfolio. The MARS Architecture is a multi-phase effort to: (1) conduct strategic architecture planning and knowledge development based on the DoDAF V2.0 six-step methodology, (2) describe one architecture through multiple viewpoints, (3) conduct portfolio analyses based on a defined operational concept, and (4) enable a new capability to support the MSFC enterprise IT management mission, vision, and goals. This report documents Phase 1 (Strategy and Design), which includes discovery, planning, and development of initial architecture viewpoints. Phase 2 will move forward the process of building the architecture, widening the scope to include application realignment (in addition to application retirement), and validating the underlying architecture logic before moving into Phase 3. The MARS Architecture key stakeholders are most interested in Phase 3 because this is where the data analysis, scoring, and recommendation capability is realized. Stakeholders want to see the benefits derived from reducing the steady-state application base and identify opportunities for portfolio performance improvement and application realignment.

Belshe, Andrea↗

ORACLES: Optimized Rational Algorithm for Conjecture of Long-term Exploration of Space

ORACLES is a joint venture between the United States Space Force (USSF) and the National Aeronautics and Space Administration (NASA) to develop a model for future space exploration, commercialization, and expansion. Following the success of qualitative future scenario exploration during the Space Futures Workshops (SFW), ORACLES aims to produce a quantitative means of evaluating these same factors. This model considers the social, economic, and political environment and determines possible inflection points; that is, the most crucial investments in technologies, infrastructure, and policy for the support and protection of U.S. and allied space assets. The development of ORACLES is in its infancy, with front end interfaces constructed for discussion and debate before the overall algorithm is finalized. Comments are highly encouraged as the team wishes to gain the broadest level of perspectives before continuing with development.

Robert B Adams↗

Technology Development and Infusion by NASA's Entry Systems Modeling Project

This paper describes recent development of modeling and simulation technologies for entry systems and their infusion into NASA's exploration missions. Technology development is organized and prioritized using a system-level perspective, resulting in four broad technical areas of investment: (1) Thermal protection material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. The paper will highlight key contributions from each of these areas, their impacts from a spacecraft and mission design perspective, and discuss planned future investment. Aspects of each technical area are only briefly summarized here. Thermal protection material modeling is geared toward high-fidelity, predictive models capable of optimizing design performance, post-flight reconstruction, and quantifying thermal protection system reliability. New computational tools and experimental techniques have been applied to Orion, MSL/Mars 2020, Mars InSight, and Mars Sample Return missions. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. In both cases, substantial improvements in model uncertainty have directly impacted the development of mission margin policies, flight instrumentation design and analysis (Orion and Mars 2020), and have even revealed the importance of neglected phenomena like mid-wave infrared radiation of CO2. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics affecting Orion, Commercial Crew, and Mars programs; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate augmentation of aerothermal environments at Mars during dust storms; and studies of roughness-induced heating augmentation relevant to tiled (Orion, Mars 2020) and woven (Mars Sample Return) thermal protection systems. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent, with precision landing requirements driven by Mars human exploration goals.

Barnhardt, Michael D.↗

Space Shuttle RTOS Bayesian Network

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores. Using a prioritization of measures from the decision-maker, trade-offs between the scores are used to rank order the available set of RTOS candidates.

Morris, A. Terry↗

A Generative Control Capability for a Model-based Executive

This paper describes Burton, a core element of a new generation of goal-directed model-based autonomous executives. This executive makes extensive use of component-based declarative models to analyze novel situations and generate novel control actions both at the goal and hardware levels. It uses an extremely efficient online propositional inference engine to efficiently determine likely states consistent with current observations and optimal target states that achieve high level goals. It incorporates a flexible generative control sequencing algorithm within the reactive loop to bridge the gap between current and target states. The system is able to detect and avoid damaging and irreversible situations, After every control action it uses its model and sensors to detect anomalous situations and immediately take corrective action. Efficiency is achieved through a series of model compilation and online policy construction methods, and by exploiting general conventions of hardware design that permit a divide and conquer approach to planning. The paper presents a formal characterization of Burton's capability, develops efficient algorithms, and reports on experience with the implementation in the domain of spacecraft autonomy. Burton is being incorporated as one of the key elements of the Remote Agent core autonomy architecture for Deep Space One, the first spacecraft for NASA's New Millenium program.

Williams, Brian C.↗

The Role of Margin in Link Design and Optimization

Link analysis is a system engineering process in the design, development, and operation of communication systems and networks. Link models that are mathematical abstractions representing the useful signal power and the undesirable noise and attenuation effects (including weather effects if the signal path transverses through the atmosphere) that are integrated into the link budget calculation that provides the estimates of signal power and noise power at the receiver. Then the link margin is applied which attempts to counteract the fluctuations of the signal and noise power to ensure reliable data delivery from transmitter to receiver. (Link margin is dictated by the link margin policy or requirements.) A simple link budgeting approach assumes link parameters to be deterministic values typically adopted a rule-of-thumb policy of 3 dB link margin. This policy works for most S- and X-band links due to their insensitivity to weather effects. But for higher frequency links like Ka-band, Ku-band, and optical communication links, it is unclear if a 3 dB link margin would guarantee link closure. Statistical link analysis that adopted the 2-sigma or 3-sigma link margin incorporates link uncertainties in the sigma calculation. (The Deep Space Network (DSN) link margin policies are 2-sigma for downlink and 3-sigma for uplink.) The link reliability can therefore be quantified statistically even for higher frequency links. However in the current statistical link analysis approach, link reliability is only expressed as the likelihood of exceeding the signal-to-noise ratio (SNR) threshold that corresponds to a given bit-error-rate (BER) or frame-error-rate (FER) requirement. The method does not provide the true BER or FER estimate of the link with margin, or the required signalto-noise ratio (SNR) that would meet the BER or FER requirement in the statistical sense. In this paper, we perform in-depth analysis on the relationship between BER/FER requirement, operating SNR, and coding performance curve, in the case when the channel coherence time of link fluctuation is comparable or larger than the time duration of a codeword. We compute the "true" SNR design point that would meet the BER/FER requirement by taking into account the fluctuation of signal power and noise power at the receiver, and the shape of the coding performance curve. This analysis yields a number of valuable insights on the design choices of coding scheme and link margin for the reliable data delivery of a communication system - space and ground. We illustrate the aforementioned analysis using a number of standard NASA error-correcting codes.

Cheung, K.↗

Experimental evaluation of dynamic data allocation strategies in a distributed database with changing workloads

Traditionally, allocation of data in distributed database management systems has been determined by off-line analysis and optimization. This technique works well for static database access patterns, but is often inadequate for frequently changing workloads. In this paper we address how to dynamically reallocate data for partionable distributed databases with changing access patterns. Rather than complicated and expensive optimization algorithms, a simple heuristic is presented and shown, via an implementation study, to improve system throughput by 30 percent in a local area network based system. Based on artificial wide area network delays, we show that dynamic reallocation can improve system throughput by a factor of two and a half for wide area networks. We also show that individual site load must be taken into consideration when reallocating data, and provide a simple policy that incorporates load in the reallocation decision.

Brunstrom, Anna↗

Attitude control system testing on SCOLE

This paper presents implementation of two control policies on SCOLE (Space Control Laboratory Experiment), a laboratory apparatus representing an offset-feed antenna attached to the Space Shuttle by a flexible mast. In the first case, the flexible mast was restrained by cables, permitting modeling of SCOLE as a rigid-body. Starting from an arbitrary state, SCOLE was maneuvered to a specified terminal state using rigid-body minimum-time control law. In the second case, the so called single step optimal control (SSOC) theory is applied to suppress vibrations of the flexible mast mounted as a cantilever beam. Based on the SSOC theory, two parameter optimization algorithms were developed.

Shenhar, J.↗

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.

Lima, Pedro↗

International Coordination and Cooperation on LunaNet Spectrum

LunaNet is planned to be the network of networks operated by a set of cooperating organizations to provide interoperable Communications, networking, Position, Navigation, and Timing (CPNT) services to users on and around the Moon based on a framework of mutually agreed-upon standards, protocols, frequency bands and interface requirements. LunaNet follows a service-oriented architecture that is agnostic about the types of organizations that provide services, e.g., government, industry, or academia. LunaNet is open, scalable, resilient, secure, and extensible. To achieve these goals, LunaNet Service Providers (LNSP) must coordinate with each other to define and develop the architecture, to plan initial and evolved capabilities, and to operate their networks. One of the central LunaNet tenets is the use of shared spectrum. For example, the Lunar Augmented Navigation Service (LANS) acts like a Global Navigation Satellite System (GNSS) such as the US Global Positioning System (GPS) or European Galileo but the LNSPs’ contributions to LunaNet must use the same frequency band (2483.5 MHz-2500.0 MHz) and transmit the same waveform synchronized by highly accurate clocks so that Users ‘see’ one virtual network and use the same multilateration algorithm to determine their positions. This necessitates a high degree of spectrum coordination. NASA’s Lunar and Human Spaceflight Spectrum Management Team has been actively supporting development of the LunaNet Interoperability Specification (LNIS), soliciting inputs from spectrum policy and planning experts across NASA, ESA and JAXA. Cislunar spectrum use considerations have been studied and adjudicated within the Space Frequency Coordination Group (SFCG) and inform the ongoing discussion of a lunar communication and navigation architecture within the existing radio regulatory framework of the International Telecommunication Union, leading to the 2027 World Radiocommunication Conference (WRC-27). The frequency plan contained in the publicly released draft of the LunaNet Interoperability Specification reflects the initial phase of exploration (roughly to 2030) defining an optimal set of radio frequencies in appropriately allocated services consistent with WRC-23 decisions for use by known or planned CPNT applications, while striving to maximize coexistence and compatibility amongst cislunar systems and other systems within the near-Earth regime (< 2 million km from Earth). Important considerations include: protection of extremely sensitive receive-only radio astronomy systems on the lunar far side, known as the Shielded Zone of the Moon (SZM); compatibility between Direct with Earth (DWE) communications links and links needed to support relay satellites in lunar orbit with their customer systems on orbit or on the lunar surface; compatibility between multiple lunar surface communications systems and capabilities over varied and challenging terrain and distances; as well as ensuring compatibility and interoperability between navigation systems which either leverage Earth-based or in-situ lunar systems. In addition, the lunar CPNT architecture is envisioned to be the basis – with adjustments – of the future Mars CPNT architecture as we expand into the solar system using Interplanetary Networking (IPN). The second phase of lunar spectrum definition will address planned international capabilities for the next decade that will require action at WRC-27 and beyond. This paper will discuss each of these considerations in more depth and how the current LunaNet frequency plan addresses them.

LunaNet↗

Battery State-of-Health Aware Path Planning for a Mars Rover

A rover mission consists of visiting waypoints to gather scientific samples based on set requirements. However, rovers face operational uncertainties during the mission, affecting the performance of its electrical and mechanical components and overall mission success. Hence, it is critical to have a decision-making framework that is aware of the health state of the components when planning the path of the vehicle. In particular, battery degradation, and consequently the battery State of Health (SOH), can affect the optimality of decisions made by the autonomous system in the long term. This paper presents a decision-making system that incorporates information on the energy drawn from the battery (based on the vehicle’s velocity), terrain conditions, and model-based prognostic modules to assess the impact on the battery’s state of charge (SoC). The decision-making system was formulated as a Markov Decision Process (MDP) to reach the goal destination by sending commands in a determined amount of time while maintaining the battery SoC within the policy stated. The MDP problem was programmed using the open-source framework POMDPs.jl, which has a variety of online and offline solvers. To solve the MDP problem online, we used Monte Carlo Tree Search (MCTS). Results from simulations demonstrate the effect that battery degradation and charging plans have on decision-making.

Prognostics↗

Optimal dynamic remapping of parallel computations

A large class of computations are characterized by a sequence of phases, with phase changes occurring unpredictably. The decision problem was considered regarding the remapping of workload to processors in a parallel computation when the utility of remapping and the future behavior of the workload is uncertain, and phases exhibit stable execution requirements during a given phase, but requirements may change radically between phases. For these problems a workload assignment generated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. The fundamental problem of balancing the expected remapping performance gain against the delay cost was addressed. Stochastic dynamic programming is used to show that the remapping decision policy minimizing the expected running time of the computation has an extremely simple structure. Because the gain may not be predictable, the performance of a heuristic policy that does not require estimnation of the gain is examined. The heuristic method's feasibility is demonstrated by its use on an adaptive fluid dynamics code on a multiprocessor. The results suggest that except in extreme cases, the remapping decision problem is essentially that of dynamically determining whether gain can be achieved by remapping after a phase change. The results also suggest that this heuristic is applicable to computations with more than two phases.

Nicol, David M.↗

Deep RL for Fast Long-Horizon Operations Scheduling on NASA's Carruthers Geocorona Observatory Mission

Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, constraint programming, or manual planning. We present a scalable deep reinforcement learning framework developed and deployed for NASA’s Carruthers Geocorona Observatory mission. Our framework introduces a macro-action abstraction known as activity blocks coupled with dynamic action-masking to navigate the intractably large search space and strictly enforce complex power, thermal, and instrument constraints. The resulting architecture generates globally feasible schedules with overwhelming probability, establishes operational trust, and executes a full training cycle in under six hours, circumventing the need for policy robustness by enabling rapid, on-demand retraining. Further, resulting schedules outperform baseline heuristics in scheduled science quality. The deep reinforcement learning framework was deployed as the default operational scheduler for the Carruthers Geocorona Observatory mission from the outset of the mission, demonstrating that deep reinforcement learning can be trusted for real spacecraft operations under complex, evolving constraints.

Geocorona↗

Education Outreach Associated with Technology Transfer in a Colonia of South Texas: Green Valley Farms Science and Space Club for Middle School Aged Children in Green Valley Farms, San Benito, Texas

Texas colonias are unincorporated subdivisions characterized by inadequate water and wastewater infrastructure, inadequate drainage and road infrastructure, substandard housing, and poverty. Since 1989 the Texas Legislature has implemented policies to halt further development of colonias and to address water and wastewater infrastructure needs in existing and new colonias along the border with Mexico. Government programs and non-government and private organization projects aim to address these infrastructure needs. Texas Tech University's Water Resources Center demonstrated the use of alternative on-site wastewater treatment in the Green Valley Farms colonia, San Benito, Texas. The work in Green Valley Farms was a component of a NASA-funded project entitled Evaluation of NASA's Advanced Life Support Integrated Water Recovery System for Non-Optimal Conditions and Terrestrial Applications. Two households within the colonia are demonstration sites for constructed wetlands. A colonia resident and activist identified educational opportunities for colonia children as a primary goal for many colonia residents. Colonia parents view education as the door to opportunity and escape from poverty for their children. The educational outreach component of the project in Green Valley Farms was a Science and Space Club for middle-school age students. Involved parents, schoolteachers, and school administrators enthusiastically supported the monthly club meetings and activities. Each month, students participated in interactive learning experiences about water use and reuse in space and on earth. Activities increased knowledge and interest in water resource issues and in science and engineering fields. The Institute for the Development and Enrichment of Advanced Learners (IDEAL) at Texas Tech University provided full scholarships for five students from Green Valley Farms to attend the Shake Hands With Your Future camp at Texas Tech University in June 2003. The educational outreach component was evaluated in February 2004 using survey instruments for students and parents, and interviews with science teachers and counselors.

Potess, Marla D.↗

Optimizing space constellations for mobile satellite systems

Designing a mobile satellite system entails many complex trade-offs between a great number of parameters including: capacity, complexity of the payload, constellation geometry, number of satellites, quality of coverage, etc. This paper aims at defining a methodology which tries to split the variables to give rapidly some first results. The major input considered is the traffic assumption which would be offered by the system. A first key step is the choice of the best Rider or Walker constellation geometries - with different numbers of satellites - to insure a good quality of coverage over a selected service area. Another aspect to be addressed is the possible altitude location of the constellation, since it is limited by many constraints. The altitude ranges that seem appropriate considering the spatial environment, the launch and orbit keeping policy and the feasibility of the antenna allowing sufficient frequency reuse are briefly analyzed. To support these first considerations, some 'reference constellations' with similar coverage quality are chosen. The in-orbit capacity needed to support the assumed traffic is computed versus altitude. Finally, the exact number of satellite is determined. It comes as an optimum between a small number of satellites offering a high (and costly) power margin in bad propagation situation and a great number of less powerful satellites granting the same quality of service.

Roussel, T.↗

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗