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At least 973 records · Page 54

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NASA NTRS2018

Autonomous Coordinated Airspace Services for Terminal and Enroute Operations with Wind Errors

As novel uses of the airspace continue to multiply, there is increasing demand for access to high-density terminal areas around major airports. Since the predicted demand for urban-air-mobility and urban-package-delivery is very high, and the interactions between these different types of aircraft and missions will be extremely complex, increasingly autonomous systems will be required to manage safety and efficiency. This paper presents the current status of an autonomous safety system designed to ensure safe and efficient trajectories for aircraft in terminal airspace, the Terminal Advanced Airspace Concept. Previous papers have demonstrated the efficacy of this algorithm for handling commercial arrivals into a complex metroplex when there is no uncertainty present. This study extends that work to demonstrate the performance of the algorithm under high levels of uncertainty.

terminal operations↗

DOE OSTI2020

Irradiation Assessment of a Lightweight Industrial Robot - Towards Faster, Safer and Sooner Waste Decommissioning - 20317

Robotic systems have been successfully applied in the nuclear industry for several decades as a safe approach to minimize the exposure dose of human operators. As nuclear waste management and decommissioning gathers pace, there is an emerging interest integrating modern off-the-shelf industrial robots in nuclear robotic systems which make use of complex electronics and software to improve functionality over traditional machines. The use of the industrial robots will significantly increase the pace of development of automated waste management systems at a reduced cost, and although these off-the-shelf robots are proven robust in typical industrial environments, performance in radioactive environments is less clear. This paper investigates the performance degradation of a lightweight industrial robot (KUKA iiwa 7 LBR 800) in a controlled radiation field, aiming to simulate conditions in highly radioactive nuclear waste handling facilities. The degradation of the industrial robot's performance is identified while measuring the air kerma dose-tolerance of sensitive components, via a systematic experimental methodology. The experience from this experiment has demonstrated the significant capabilities of industrial robots, which tolerated a large gamma dose of 164 Gy before a system failure. Future tests are planned, aiming to enable faster, safer and sooner waste management and decommissioning using complex robotic systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2018

Autonomous Coordinated Airspace Services for Terminal and Enroute Operations with Wind Errors

terminal operations↗

DOE OSTI2018

Green spaces are not all the same for the provision of air purification and climate regulation services: The case of urban parks

Highlights: • Air purification and climate regulation were quantified in an urban green space. • Different vegetation types shown different capacities to provide these services. • Original woodland presented the higher ecosystem services provision. • Vegetation structure, composition and management matter to services provision. • Nature-based solutions in urban areas can optimize local climate and air quality. The growing human population concentrated in urban areas lead to the increase of road traffic and artificial areas, consequently enhancing air pollution and urban heat island effects, among others. These environmental changes affect citizen's health, causing a high number of premature deaths, with considerable social and economic costs. Nature-based solutions are essential to ameliorate those impacts in urban areas. While the mere presence of urban green spaces is pointed as an overarching solution, the relative importance of specific vegetation structure, composition and management to improve the ecosystem services of air purification and climate regulation are overlooked. This avoids the establishment of optimized planning and management procedures for urban green spaces with high spatial resolution and detail. Our aim was to understand the relative contribution of vegetation structure, composition and management for the provision of ecosystem services of air purification and climate regulation in urban green spaces, in particular the case of urban parks. This work was done in a large urban park with different types of vegetation surrounded by urban areas. As indicators of microclimatic effects and of air pollution levels we selected different metrics: lichen diversity and pollutants accumulation in lichens. Among lichen diversity, functional traits related to nutrient and water requirements were used as surrogates of the capacity of vegetation to filter air pollution and to regulate climate, and provide air purification and climate regulation ecosystem services, respectively. This was also obtained with very high spatial resolution which allows detailed spatial planning for optimization of ecosystem services. We found that vegetation type characterized by a more complex structure (trees, shrubs and herbaceous layers) and by the absence of management (pruning, irrigation and fertilization) had a higher capacity to provide the ecosystems services of air purification and climate regulation. By contrast, lawns, which have a less complex structure and are highly managed, were associated to a lower capacity to provide these services. Tree plantations showed an intermediate effect between the other two types of vegetation. Thus, vegetation structure, composition and management are important to optimize green spaces capacity to purify air and regulate climate. Taking this into account green spaces can be managed at high spatial resolutions to optimize these ecosystem services in urban areas and contribute to improve human well-being.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Safety Risk Knowledge Elicitation in Support of Aeronautical R and D Portfolio Management: A Case Study

Aviation is a problem domain characterized by a high level of system complexity and uncertainty. Safety risk analysis in such a domain is especially challenging given the multitude of operations and diverse stakeholders. The Federal Aviation Administration (FAA) projects that by 2025 air traffic will increase by more than 50 percent with 1.1 billion passengers a year and more than 85,000 flights every 24 hours contributing to further delays and congestion in the sky (Circelli, 2011). This increased system complexity necessitates the application of structured safety risk analysis methods to understand and eliminate where possible, reduce, and/or mitigate risk factors. The use of expert judgments for probabilistic safety analysis in such a complex domain is necessary especially when evaluating the projected impact of future technologies, capabilities, and procedures for which current operational data may be scarce. Management of an R&D product portfolio in such a dynamic domain needs a systematic process to elicit these expert judgments, process modeling results, perform sensitivity analyses, and efficiently communicate the modeling results to decision makers. In this paper a case study focusing on the application of an R&D portfolio of aeronautical products intended to mitigate aircraft Loss of Control (LOC) accidents is presented. In particular, the knowledge elicitation process with three subject matter experts who contributed to the safety risk model is emphasized. The application and refinement of a verbal-numerical scale for conditional probability elicitation in a Bayesian Belief Network (BBN) is discussed. The preliminary findings from this initial step of a three-part elicitation are important to project management practitioners as they illustrate the vital contribution of systematic knowledge elicitation in complex domains.

Shih, Ann T.↗

DOE OSTI2017

Land management strategies for improving water quality in biomass production under changing climate

Here, the Corn Belt states are the largest corn-production areas in the United States because of their fertile land and ideal climate. This attribute is particularly important as the region also plays a key role in the production of bioenergy feedstock. In much of the nation, agricultural nutrients are a primary cause of water quality degradation. This study focuses on potential change in streamflow, sediment, nitrogen, and phosphorus due to climate change and land management practices in the South Fork Iowa River (SFIR) watershed, Iowa. Thirty-six projections from select Regional Climate Models (RCM) for Representative Concentration Pathways (RCP) 2.5, 4.5, and 8.5 were used to develop climate change scenarios for the SFIR watershed and incorporated into the Soil and Water Assessment Tool (SWAT). The watershed is covered primarily with annual crops (corn and soybeans). Three scenarios of land use change and conservation practices were further developed to examine their impacts on water quality under historical and modeled future climate. With cropland conversion to switchgrass, stover harvest, and best management practices (BMPs) (such as establishing riparian buffers and applying cover crops) significant reductions in nutrients were observed in the SFIR watershed under historical climate and future climate scenarios. Under historical climate, suspended sediment (SS), total nitrogen (N), and phosphorus (P) at the outlet point of the SFIR watershed could decrease by up to 56.7%, 32.0%, and 16.5%, respectively, compare with current land use when a portion of the cropland is converted to switchgrass and cover crop is in place. Climate change could cause an increase in 12.0% (SS), 4.7% (N), and 7.7% (P) from current land use. This increase could be mitigated through land management and practices by 53.6% (SS), 27.8 (N), and 7.0% (P). Climate change reduced crop yield. Nutrient and sediments loadings distributed heterogeneously across the watershed. Water footprint analysis further revealed changes in green water that are highly dependent on land management scenarios. The study highlights the versatile approaches in landscape management that are available to address climate change adaptation and acknowledged the complex nature of different perspectives in water sustainability. Further study involving implementing landscape design and management using long-term field to watershed water monitoring data is necessary to verify the findings and moving towards watershed specific regional programs for climate adaptation.

09 BIOMASS FUELS↗

DOE OSTI2011

IMIGO: An Optimal Adaptive Nonlinear Program for Accelerator Optics Modeling and Other Applications

Model-based electron accelerator control is the maintenance of optimal parameters of an electron beam such as its orbit, size, and shape, as well as machine parameters such as tunes. It works well when the model reflects reality. SLAC pioneered this technique in SPEAR about thirty decades ago. Similar techniques are now employed in particle accelerator and synchrotron laboratories around the world. There is still an inherently complex problem related to the employment of such techniques to manage the operation and analysis of accelerators and storage rings. The problem arises from the use by those techniques of complex numerical algorithms commonly known as nonlinear solvers that are difficult to control and operate. Lessons learned at SLAC have led to the development of a new, simple-to-use, and iterative nonlinear solver that holds much promise not only in advancing the derivation of errors in accelerators and storage rings at SLAC, but also in its ability to tackle a range of complex engineering problems. A CRADA project was recently completed to build on this experience at SLAC, and to collaborate with an outside company GO AI Services to further develop and validate this nonlinear solver for robust SLAC accelerator. In this report, we present the project report submitted to DOE and SLAC.

Accelerators↗

DOE OSTI2014

Agua Caliente Wind/Solar Project at Whitewater Ranch

Agua Caliente Band of Cahuilla Indians (ACBCI) was awarded a grant by the Department of Energy (DOE) to study the feasibility of a wind and/or solar renewable energy project at the Whitewater Ranch (WWR) property of ACBCI. Red Mountain Energy Partners (RMEP) was engaged to conduct the study. The ACBCI tribal lands in the Coachella Valley have very rich renewable energy resources. The tribe has undertaken several studies to more fully understand the options available to them if they were to move forward with one or more renewable energy projects. With respect to the resources, the WWR property clearly has excellent wind and solar resources. The DOE National Renewable Energy Laboratory (NREL) has continued to upgrade and refine their library of resource maps. The newer, more precise maps quantify the resources as among the best in the world. The wind and solar technology available for deployment is also being improved. Both are reducing their costs to the point of being at or below the costs of fossil fuels. Technologies for energy storage and microgrids are also improving quickly and present additional ways to increase the wind and/or solar energy retained for later use with the network management flexibility to provide power to the appropriate locations when needed. As a result, renewable resources continue to gain more market share. The transitioning to renewables as the major resources for power will take some time as the conversion is complex and can have negative impacts if not managed well. While the economics for wind and solar systems continue to improve, the robustness of the WWR site was validated by the repeated queries of developers to place wind and/or solar there. The robust resources and improving technologies portends toward WWR land as a renewable energy site. The business case, however, is not so clear, especially when the potential investment portfolio for ACBCI has several very beneficial and profitable alternatives.

17 WIND ENERGY↗

NASA NTRS1991

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design.

Malin, Jane T.↗

NASA NTRS2004

Integrated Risk Management Within NASA Programs/Projects

As NASA Project Risk Management activities continue to evolve, the need to successfully integrate risk management processes across the life cycle, between functional disciplines, stakeholders, various management policies, and within cost, schedule and performance requirements/constraints become more evident and important. Today's programs and projects are complex undertakings that include a myriad of processes, tools, techniques, management arrangements and other variables all of which must function together in order to achieve mission success. The perception and impact of risk may vary significantly among stakeholders and may influence decisions that may have unintended consequences on the project during a future phase of the life cycle. In these cases, risks may be unintentionally and/or arbitrarily transferred to others without the benefit of a comprehensive systemic risk assessment. Integrating risk across people, processes, and project requirements/constraints serves to enhance decisions, strengthen communication pathways, and reinforce the ability of the project team to identify and manage risks across the broad spectrum of project management responsibilities. The ability to identify risks in all areas of project management increases the likelihood a project will identify significant issues before they become problems and allows projects to make effective and efficient use of shrinking resources. By getting a total team integrated risk effort, applying a disciplined and rigorous process, along with understanding project requirements/constraints provides the opportunity for more effective risk management. Applying an integrated approach to risk management makes it possible to do a better job at balancing safety, cost, schedule, operational performance and other elements of risk. This paper will examine how people, processes, and project requirements/constraints can be integrated across the project lifecycle for better risk management and ultimately improve the chances for mission success.

Connley, Warren↗

NASA NTRS2015

Why Don't They Just Give Us Money? Project Cost Estimating and Cost Reporting

Successful projects require an integrated approach to managing cost, schedule, and risk. This is especially true for complex, multi-year projects involving multiple organizations. To explore solutions and leverage valuable lessons learned, NASA's Virtual Project Management Challenge will kick off a three-part series examining some of the challenges faced by project and program managers when it comes to managing these important elements. In this first session of the series, we will look at cost management, with an emphasis on the critical roles of cost estimating and cost reporting. By taking a proactive approach to both of these activities, project managers can better control life cycle costs, maintain stakeholder confidence, and protect other current and future projects in the organization's portfolio. Speakers will be Doug Comstock, Director of NASA's Cost Analysis Division, Kristin Van Wychen, Senior Analyst in the GAO Acquisition and Sourcing Management Team, and Mary Beth Zimmerman, Branch Chief for NASA's Portfolio Analysis Branch, Strategic Investments Division. Moderator Ramien Pierre is from NASA's Academy for Program/Project and Engineering Leadership (APPEL).

Comstock, Douglas A.↗

DOE OSTI2010

Automatic Fault Characterization via Abnormality-Enhanced Classification

Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help to identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Health Management and Prognostics for Electric Aircraft Powertrain

W and c Any air borne vehicle needs incorporating safety as key parameter of measure, and inclusion of autonomy raises the critical need for safety under autonomous operations. Management of faults and component degradation is key as complexity in autonomous operations grow over the period of time. Therefore, in addition to basic operational requirements, an autonomous electric vehicle should be able to make accurate estimates of its current system health and take the correct decisions to complete its mission successfully. Real-time safety and state-awareness tools are therefore essential for the vehicle to be able to reach its destination in a safe and successful manner. The need for safety assurance and health management capabilities is particularly relevant for aircraft electric propulsion systems, which are relatively new and with limited historical to learn. They are critical systems requiring high power density along with reliability, resilience, efficient management of weight, and operational costs. A model- based fault diagnosis and prognostics approach of complex critical systems can successfully accomplish the safety and state awareness goal for such electric propulsion systems, enabling autonomous decision making capability for safe and efficient operation. To identify critical components in the system a Qualitative Bayesian approach using FMECA is implemented. This requires the assessment of some quantities representing the state of the electric unmanned aerial systems (e-UAS), as well as look-ahead forecasts of such states during the entire flight, presented in form of safety metrics (SM). In-service data and performance data gathered from degraded components sup- ports diagnostic and prognostic methods for these systems, but this data can be difficult to obtain as weight and packaging restrictions reduce redundancy and instrumentation on-board the vehicle. Therefore, an model-based framework should be capable or operating with limited data. In addition to data scarcity, the variability of such complex critical systems re- quires the model-based framework to reason in the presence of uncertainty, such as sensor noise, and modeling imperfections. Quantification of errors and uncertainties in the measured states and quantities is therefore a fundamental step for a precise estimation of such SMs; un-modeled uncertainty may result in erroneous state assessment and un- reliable predictions of future states of e-UAVs. Typical, centralized model-based schemes suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues, and therefore may fail in such a complex scenario. This paper presents a methodology for developing a system level diagnostics and prognostics approach using a Qualitative Bayesian FMECA approach along with a formal uncertainty management framework for an e-UAS. In this work we demonstrate the efficacy of the framework to predict effects of sub-system level degradation on vehicle operation incorporating uncertainty management to predict future behavior under different operating conditions.

Kulkarni, Chetan↗

NASA NTRS1999

Managing Personal and Group Collections of Information

The internet revolution has dramatically increased the amount of information available to users. Various tools such as search engines have been developed to help users find the information they need from this vast repository. Users often also need tools to help manipulate the growing amount of useful information they have discovered. Current tools available for this purpose are typically local components of web browsers designed to manage URL bookmarks. They provide limited functionalities to handle high information complexities. To tackle this have created DIAMS, an agent-based tool to help users or groups manage their information collections and share their collections with other. the main features of DIAMS are described here.

Wolfe, Shawn R.↗

DOE OSTI2024

Sierra/SD – Verification Test Manual – 5.22

Verification and validation (V&V) of scientific computing programs are important at Sandia National Labs due to the expanding role of computational simulation in managing the United States nuclear stockpile. The complexities of structural response calculations used to analyze physical problems, the varieties of codes applied to the calculations, and the importance of accurate predictions when assessing field conditions demand confidence in the consistency and accuracy of computer codes. Confidence in the accuracy of the predictions arising from computer simulations must ultimately be gained through verification and validation. The Sierra salinas structural dynamics analysis code, Sierra/SD, is used at the DOE Laboratories, and in several DOD projects. The roles of Sierra/SD in the qualification of weapon systems and components for normal and hostile environments throughout the Stockpile-to-Target Sequence include to, • Redesign weapon components. • Certify weapon components and systems for target environments such as hypersonic vehicles. • Certify that components will survive the thermal mechanical shock loads associated with hostile environments. • Evaluate current stockpile issues, including issues associated with uncertainty quantification. • Address many other problems that are encountered in stockpile management. The Sierra/SD verification plan is described, and an evolving set of key verification tests are described in detail. The verification tests ensure the correctness of the mathematics and numerical algorithms associated with functionality describing engineering phenomena. Development is in accordance with a set of tailored Software Quality Engineering (SQE) practices. SQE practices guide the overall verification and validation effort.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1999

New challenges for Life Sciences flight project management

Scientists have conducted studies involving human spaceflight crews for over three decades. These studies have progressed from simple observations before and after each flight to sophisticated experiments during flights of several weeks up to several months. The findings from these experiments are available in the scientific literature. Management of these flight experiments has grown into a system fashioned from the Apollo Program style, focusing on budgeting, scheduling and allocation of human and material resources. While these areas remain important to the future, the International Space Station (ISS) requires that the Life Sciences spaceflight experiments expand the existing project management methodology. The use of telescience with state-the-art information technology and the multi-national crews and investigators challenges the former management processes. Actually conducting experiments on board the ISS will be an enormous undertaking and International Agreements and Working Groups will be essential in giving guidance to the flight project management Teams forged in this matrix environment must be competent to make decisions and qualified to work with the array of engineers, scientists, and the spaceflight crews. In order to undertake this complex task, data systems not previously used for these purposes must be adapted so that the investigators and the project management personnel can all share in important information as soon as it is available. The utilization of telescience and distributed experiment operations will allow the investigator to remain involved in their experiment as well as to understand the numerous issues faced by other elements of the program The complexity in formation and management of project teams will be a new kind of challenge for international science programs. Meeting that challenge is essential to assure success of the International Space Station as a laboratory in space.

NASA Discipline General Space Life Sciences↗

DOE OSTI2023

Quantifying the effects of pressure management for the Williston basin Brine Extraction and Storage Test (BEST) site using machine learning

Active reservoir management (ARM) through brine extraction can reduce pressure buildup during large-scale implementation of carbon capture and storage (CCS) projects. This study used machine learning (ML)-assisted approaches to analyze bottomhole pressure (BHP) responses to various brine injection and extraction scenarios. Field monitoring data were collected over a 2-year operation period at two injection wells and one extraction well (about 400 meters away) as part of a Brine Extraction and Storage Test (BEST) in the North Dakota portion of the Williston Basin. Injection activities increased the BHPs at the injection wells by around 0.70 MPa (~100 psi) during the operation period. Extraction activities demonstrated the capability to decrease the BHPs at the injection wells by approximately 0.21–0.34 MPa (30–50 psi) depending on the ratio of the extraction and injection well flow rates (the “extraction ratio” – a normalization procedure used in the analysis). The pressure reduction provided by the extraction well equated to 30%–50% of the pressure buildup at the injection well. Furthermore, this work shows how ML analytics can play a key role in estimating reservoir pressure responses for complex injection and extraction activities during pressure management practices of CCS projects.

58 GEOSCIENCES↗

DOE OSTI2016

Advanced Computational Framework for Environmental Management ZEM, Version 1.x

Typically environmental management problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly integration into a series of models with different complexity for various types of model analyses where the data are applied as soft and hard model constraints. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions). To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian- Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org). The ZEM framework is open-source and can be applied to any environmental management site. The framework will be open-source and released under GPL V3 license.

Vesselinov, VelimirV.↗