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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Statistical methodologies for the control of dynamic remapping

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust.

Saltz, J. H.↗
NASA NTRS1993

NASA safety program activities in support of the Space Exploration Initiatives Nuclear Propulsion program

The activities of the joint NASA/DOE/DOD Nuclear Propulsion Program Technical Panels have been used as the basis for the current development of safety policies and requirements for the Space Exploration Initiatives (SEI) Nuclear Propulsion Technology development program. The Safety Division of the NASA Office of Safety and Mission Quality has initiated efforts to develop policies for the safe use of nuclear propulsion in space through involvement in the joint agency Nuclear Safety Policy Working Group (NSPWG), encouraged expansion of the initial policy development into proposed programmatic requirements, and suggested further expansion into the overall risk assessment and risk management process for the NASA Exploration Program. Similar efforts are underway within the Department of Energy to ensure the safe development and testing of nuclear propulsion systems on Earth. This paper describes the NASA safety policy related to requirements for the design of systems that may operate where Earth re-entry is a possibility. The expected plan of action is to support and oversee activities related to the technology development of nuclear propulsion in space, and support the overall safety and risk management program being developed for the NASA Exploration Program.

Sawyer, J. C., Jr.↗
NASA NTRS2006

Robust Coordination for Large Sets of Simple Rovers

The ability to coordinate sets of rovers in an unknown environment is critical to the long-term success of many of NASA;s exploration missions. Such coordination policies must have the ability to adapt in unmodeled or partially modeled domains and must be robust against environmental noise and rover failures. In addition such coordination policies must accommodate a large number of rovers, without excessive and burdensome hand-tuning. In this paper we present a distributed coordination method that addresses these issues in the domain of controlling a set of simple rovers. The application of these methods allows reliable and efficient robotic exploration in dangerous, dynamic, and previously unexplored domains. Most control policies for space missions are directly programmed by engineers or created through the use of planning tools, and are appropriate for single rover missions or missions requiring the coordination of a small number of rovers. Such methods typically require significant amounts of domain knowledge, and are difficult to scale to large numbers of rovers. The method described in this article aims to address cases where a large number of rovers need to coordinate to solve a complex time dependent problem in a noisy environment. In this approach, each rover decomposes a global utility, representing the overall goal of the system, into rover-specific utilities that properly assign credit to the rover s actions. Each rover then has the responsibility to create a control policy that maximizes its own rover-specific utility. We show a method of creating rover-utilities that are "aligned" with the global utility, such that when the rovers maximize their own utility, they also maximize the global utility. In addition we show that our method creates rover-utilities that allow the rovers to create their control policies quickly and reliably. Our distributed learning method allows large sets rovers be used unmodeled domains, while providing robustness against rover failures and changing environments. In experimental simulations we show that our method scales well with large numbers of rovers in addition to being robust against noisy sensor inputs and noisy servo control. The results show that our method is able to scale to large numbers of rovers and achieves up to 400% performance improvement over standard machine learning methods.

Tumer, Kagan↗
NASA NTRSDate not supplied

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Christopher J Sullivan↗
NASA NTRSDate not supplied

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of !2 to an !5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Christopher J Sullivan↗
NASA NTRSDate not supplied

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of L2 to an L5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Christopher J. Sullivan↗
NASA NTRS2021

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Mashiku, Alinda K.↗
NASA NTRSDate not supplied

NASA Nuclear Flight Safety – New Protocols for Safety and Mission Assurance

Over the past 4 years, the United States (U.S.) Government has issued several new National policies that fundamentally change the approach to nuclear flight safety for aerospace applications, including the complete revision of the Federal policy for handling launch of spacecraft containing space nuclear systems. In response, the National Aeronautics and Space Administration (NASA) is updating its nuclear flight safety program while still maintaining consistency with other Federal policies, international conventions, and NASA’s own policies. The recently-completed revision of the NASA Procedural Requirements (NPR) document for nuclear flight safety is a key step in the evolution of NASA policy and practice in this area. This paper will briefly describe the more significant changes in the new NASA NPR.

Donald Helton↗
NASA NTRSDate not supplied

NASA Planetary Protection Program Overview and Update

NASA has an integrated planetary protection strategy that leverages COSPAR Policy guide-lines, workshops, scientific consensus, partnerships and international working groups to develop policy and implementation guidelines. The Oÿce of Safety and Mission Assurance, which in-cludes NASA’s Oÿce of Planetary Protection, depends on these activities to support NASA’s missions in the assurance of crew safety and mission success while protecting the public and planetary environment from harmful contamination. This report will detail the overall planetary protection policy development perspective to sup-port NASA’s upcoming mission opportunities for exploration to include crewed Mars and sam-ple return missions. NASA continues to evolve its strategies, policies, and standards. NASA’s support in the COSPAR workshop series on Refining Planetary Protection Requirements for Crewed Missions to Mars have resulted in a NASA interim directive and paved the way for the development of NASA planetary protection roadmaps. These Agency level roadmaps address technology development and testing required to fill knowledge gaps for developing responsive policy guidelines and quantitative technical standards. In addition to workshops and tech-nology management strategies, NASA is also leveraging the National Academies of Science, Engineering, and Medicine’s Committee of Planetary Protection to serve as a source of advice on planetary protection measures. Finally, the report will include how the Oÿce of Safety and Mission Assurance is incorporating planetary protection into existing and new multi-agency partnerships.

Frank Groen↗
NASA NTRS2022

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of 𝐿2 to an 𝐿5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Mashiku, Alinda K.↗
DOE OSTI2014

A hybrid inventory management system respondingto regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Accessing protected data by a high-performance computing cluster

A data protection system is provided that allows applications to access protected data in a way that restricts applications from outputting to unauthorized targets any unprotected data derived from the protected data and that ensures that the applications do not have access to a key that allows access to the unprotected data. The data protection system provides a policy server that may execute on a service node of a high performance computing system and a data encryption process that may execute on each compute node that is allocated to an application or batch job. The policy server maintains policies of entities specifying access control for protected data. The data encryption process generates a secure execution environment for an application process and interfaces with the policy server to retrieve keys for decrypting protected data in accordance with a policy, and it decrypts and provides the decrypted data to the application process.

Barnes, Peter↗
DOE OSTI2023

Building Environmental Justice and Equity into the Development of Critical Mineral Industries

The global transition toward low-carbon energy not only means an increase in demand for clean electricity and renewable resources, but also an increase in demand for the critical minerals (CMs) and rare earth elements (REEs) that these technologies rely upon. Indeed, low-carbon energy technologies, such as those used for wind turbines and electric vehicle motors, require significantly more lithium, nickel, cobalt, manganese, graphite, and other CMs than fossil-based energy technologies. The growing demand for new forms of low- carbon energy will necessitate a proportional scale-up of CM and REE extraction and processing. Presently, the majority of extraction and processing activities for CMs and REEs are concentrated in very few countries, primarily in China and the Global South. CM and REE supply chain activities conducted in or controlled by these countries are commonly associated with widespread and well- documented human rights abuses and environmental degradation. As a result of concerns related to supply chain security, worker welfare, and the overall need for additional sources of CMs and REEs, governments worldwide have begun to explore policy pathways for the development of new supply chains. The development of new supply chains for CMs and REEs re s a multifold opportunity to accelerate the global deployment of low-carbon energy fleets, reduce global carbon emissions, build political resilience—and also to enhance policy objectives of environmental justice and equity in the clean energy transition. Fulfilling these policy objectives requires energy producers to navigate a maze of global supply chain policies designed to shape and accelerate the growth of new markets. In the United States, for instance, President Biden recently implemented fiscal and trade policies that incentivize both domestic and global reliance on U.S.-produced CMs and REEs, leveraging key relationships in Asia and Europe to ensure the accelerated buildout of the U.S. supply chain. The Biden Administration’s framework also emphasizes that new supply chains for CMs, REEs, and other low-carbon energy pathways must generate benefits for marginalized and disadvantaged communities, build energy equity, and contribute to the Biden Administration’s vision of environmental justice. present CM and REE supply chains include several stages, including mining, processing, transport, utilization, and disposal, each of which involves different environmental justice considerations and potential injustices. This study explores how technical innovation, paired with responsible community engagement and empowerment, can help inform the development of energy equity and environmental justice at all stages of new supply chains. The study highlights these opportunities on a broad scale. We also lend a specific focus to research at the University of Wyoming School of Energy Resources that aims to guide the development of CM and REE industries in communities with high economic dependence on coal and other fossil fuel industries, identifying pathways to grow a U.S. domestic supply chain by producing CMs from coal, coal by-products, and coal waste streams. These production pathways represent a potential new supply for CMs across the U.S. and elsewhere, thereby enhancing national security and accelerating the widespread deployment of low-carbon energy technologies, while also generating alternative applications for remaining coal reserves and aiding in a just transition for rural energy-producing communities.

Gerace, Selena↗
DOE OSTI2019

Panel Session 82: Shareholder Engagement with DOE Associate Undersecretary's Environment, Health, Safety, and Security

This panel discussed the mission of the US DOE Office of the Associate Under Secretary for the Office of Environment, Health, Safety and Security (AU) and its functional support to the overall mission of the DOE, DOE Office of Environmental Management, and the contractor partners that support that overall mission. Participation in this panel helps achieve one of AU's strategic plan goals - to engage stakeholders to ensure AU's Environment, Health, Safety and Security (EHS and S) policy and support activities align with DOE mission needs. The Panel included stakeholders to discuss their perspectives on AU stakeholder engagement as well as representatives from four policy and support offices from AU. This panel was an important implementation of the DOE Office of the Associate Under Secretary for the Office of EHS and S (AU) strategic plan goals - to engage stakeholders to ensure AUs Environment, Health, Safety and Security (EHS and S) policy and support activities align with DOE mission needs. The panel included stakeholders to discuss their perspectives on AU stakeholder engagement as well as representatives from four policy and support offices from AU including the Office of Health and Safety (AU-10); Office of Environmental Protection and ES and H Reporting (AU-20); Office of Nuclear Safety (AU-30); and Office of Security (AU-50). Presentations proposed include: 1 - Office of Environment, Health, Safety and Security (AU) - Stakeholder Engagement (Matthew Moury, US DOE, Office of Environment, Health, Safety and Security (USA)); 2 - Environment, Health, Safety, and Security Policy Making: Rules, Directives, Technical Standards and Programs (Patricia Worthington, US DOE, Office of Environment, Health, Safety and Security (USA)); 3 - Environment, Health, Safety, and Security: Corporate Programs (Josh Silverman, US DOE, Office of Environment, Health, Safety and Security (USA)); 4 - Environment, Health, Safety and Security: Technical Assistance (Marc Brooks, US DOE, Office of Environment, Health, Safety and Security (USA)); 5 - Environment, Health, Safety and Security: Goals and Initiatives (Tom Hiltz, US DOE, Office of Environment, Health, Safety and Security (USA)); 6 - Shareholder Engagement with DOE Associate Undersecretary's Environment, Health, Safety, and Security (Greg Sosson, US DOE (USA)); 7 - Nuclear Safety Center of Excellence (Chip Lagdon, Bechtel National, Inc (USA)); 8 - EFCOG Safety Working Group (Jan Preston, Fluor Corporation (USA))

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2019

Intertwined impacts of water, energy development, and carbon emissions in China

China is rapidly expanding its alternative and non-conventional energy production capabilities. Although renewable electricity remains the focus, considerable investment has supported construction of coal liquefaction and coal gasification facilities in the desert steppes of north-central China, new coal mines in arid Inner Mongolia, and tight oil and gas extraction in the Ordos to supplement limited domestic supplies of oil and gas. At the same time, China is also facing severe drought and water scarcity in these same regions and in response has expanded various water supply technologies such as desalination and wastewater treatment. Recent government goals and measures for reducing energy and water consumption and increasing efficiency introduced in national policies, however, are poorly or not coordinated, resulting in contradictory objectives for which physical interlinkages are not well understood. This research intends to provide insights for future energy-water nexus management decisions in China, through systematic, comprehensive modeling of the water-energy nexus in China based on comprehensive, bottom-up technology characterizations. Existing studies fail to adequately characterize the details on specific technologies, nor do they comprehensively cover all energy sectors, including energy conversion for non-energy products. We developed integrated assessment (IA) capabilities to allow stakeholders to observe the tradeoffs between various technology options and policy decisions and to test hypotheses/premises in a scenario-driven environment. The results of our analysis underscore the growing interconnection between water and energy in China, the mixed trade-offs from developing low-carbon technologies such as renewable energy and inland nuclear, and the importance of water-energy nexus issues at the regional and local scales. This study lays the groundwork for an integrated resource policy planning process in China and provides an assessment methodology and research directions for future studies of the water-energy nexus. To conclude, this study contributes to the water-energy nexus literature by providing systematic data and policy implications for China, where data are typically less accessible, as well as providing references for other regions in the world that are facing similar water and energy use and planning challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Using Compliance Analysis for PPP to bridge the gap between SEA and EIA: Lessons from the Turcot Interchange reconstruction in Montréal, Québec

There is increasing concern about the disjunct between the intent of higher level government goals and actual projects “on the ground” in Canada. Although strategic environmental assessment (SEA) and a wide variety of plans, policies and programmes (PPP) contain and promote goals that envision a movement towards social, economic and environmental sustainability, these goals are not necessarily upheld by large-scale projects and their environmental impact assessments (EIAs). This disconnect is often illustrated through anecdotal observations. However, to be able to overcome this disjunct it is imperative to come to a clearer understanding of the degree of sustainability or unsustainability of large-scale developments and the way in which they “measure up” in terms of the goals when compared to alternative options. This article proposes a Compliance Analysis method for investigating the level of harmonization between SEA, PPP and proposed projects and their possible alternatives (CAPPP). This method is quantified through a Likert scale which allows for comparison of alternatives for decision making and analytical purposes. The 2009 proposal for the Turcot Exchange redevelopment in Montréal, Québec, put forward by the Ministry of Transport of Québec (MTQ), as well as two alternative proposals, were utilized as a case study to clearly demonstrate the CAPPP methodology and its applicability. The approved plan for the Turcot redevelopment proposed by MTQ was found to be in poor compliance with the majority of the 178 goals in the six sectors that were examined (air quality, climate change, health, noise, socioeconomic, transport), while alternative proposals were found to be in greater accordance with the intentions of governmental SEA and PPP. Synthesis and applications: The CAPPP methodology is a versatile “watchdog” tool for the examination of the level of compliance between stated goals for regions, industrial sectors, or governments and the EIAs of concrete projects “on the ground”. CAPPP can be used as a tool for comparative analysis in decision-making situations at various scales. CAPPP is a fairly straight-forward method that can be used by policy makers, EIA experts, and members of the general public alike. Highlights: ► We investigated the level of harmonization between SEA, plans, policies and programmes and EIA projects. ► We created a new methodology: the goal compliance analysis (GCA). ► We tested it on an ongoing project, the Turcot Interchange in Montreal, Canada. ► The method is straight-forward and can be used by policy makers, EIA experts, and members of the general public alike.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Energy technology roll-out for climate change mitigation: A multi-model study for Latin America

In this paper we investigate opportunities for energy technology deployment under climate change mitigation efforts in Latin America. Through several carbon tax and CO 2 abatement scenarios until 2050 we analyze what resources and technologies, notably for electricity generation, could be cost-optimal in the energy sector to significantly reduce CO 2 emissions in the region. By way of sensitivity test we perform a cross-model comparison study and inspect whether robust conclusions can be drawn across results from different models as well as different types of models (general versus partial equilibrium). Given the abundance of biomass resources in Latin America, they play a large role in energy supply in all scenarios we inspect. This is especially true for stringent climate policy scenarios, for instance because the use of biomass in power plants in combination with CCS can yield negative CO 2 emissions. We find that hydropower, which today contributes about 800 TWh to overall power production in Latin America, could be significantly expanded to meet the climate policies we investigate, typically by about 50%, but potentially by as much as 75%. According to all models, electricity generation increases exponentially with a two- to three-fold expansion between 2010 and 2050.Wefind that in our climate policy scenarios renewable energy overall expands typically at double-digit growth rates annually, but there is substantial spread in model results for specific options such as wind and solar power: the climate policies that we simulate raise wind power in 2050 on average to half the production level that hydropower provides today, while they raise solar power to either a substantially higher or a much lower level than hydropower supplies at present, depending on which model is used. Also for CCS we observe large diversity in model outcomes, which reflects the uncertainties with regard to its future implementation potential as a result of the challenges this CO 2 abatement technology experiences. The extent to which different mitigation options can be used in practice varies greatly between countries within Latin America, depending on factors such as resource potentials, economic performance, environmental impacts, and availability of technical expertise. We provide concise assessments of possible deployment opportunities for some low-carbon energy options, for the region at large and with occasional country-level detail in specific cases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗