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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Trajectory Planning for Mission Survivability of Autonomous Vehicles in Moderately to Extremely Uncertain Environments

Trajectory planning is a particularly challenging task for autonomous vehicles when the uncertainties in their operating environment cannot be characterized statistically. In this paper, we propose a receding horizon control strategy with novel trajectory planning policies that enable dynamic updating of the planned trajectories of autonomous vehicles operating in environments where potential conflicts are, from a statistical perspective, either partially known or completely unknown. The proposed policies utilize two metrics: (1) the number of feasible trajectories; and (2) the robustness of the feasible trajectories. We measure the effectiveness of the suggested policies in terms of mission survivability, which is defined as the probability that the primary mission is accomplished or, if that is not possible, the vehicle lands safely at an alternative site. We show that a linear combination of both metrics is an effective objective function when there is a mix of partially known and unknown uncertainties. When the operating environment is dominated by unknown disturbances, maximizing the number of feasible trajectories results in the highest mission survivability. These findings have significant implications for achieving safe aviation. autonomy.

Aviation autonomy↗
NASA NTRSDate not supplied

Trajectory Planning for Mission Survivability of Autonomous Vehicles in Moderately to Extremely Uncertain Environments

Trajectory planning is a particularly challenging task for autonomous vehicles when there are moderate to extreme uncertainties in their operating environment, i.e., where the trajectories of hazards are partially known to completely unknown. In this paper, we propose a receding horizon control strategy with novel trajectory planning policies that enable dynamic updating of the planned trajectories of autonomous vehicles. The proposed policies utilize two metrics: (1) the number of feasible trajectories; and (2) the robustness of the feasible trajectories. We measure the effectiveness of the suggested policies in terms of mission survivability, which is defined as the probability that the primary mission is accomplished or, if that is not possible, the vehicle lands safely at an alternative site. We show that a linear combination of both metrics is an effective objective function when there is a mix of partially known and unknown uncertainties. When the operating environment is dominated by unknown disturbances, maximizing the number of feasible trajectories results in the highest mission survivability. These findings have significant implications for achieving safe aviation autonomy.

aviation autonomy↗
NASA NTRSDate not supplied

A Technology Maturation Plan for the Development of Nuclear Electric Propulsion

Over the last two years NASA’s Space Nuclear Propulsion (SNP) Project formulated a Technology Maturation Plan (TMP) for development of the sub-systems needed for a MW-class Nuclear Electric Propulsion (NEP) system which, combined with a high thrust chemical propulsion stage, would be suitable for human missions to Mars. Two recent assessments, independently conducted by the National Academies for Science, Engineering, and Medicine and the NASA Engineering & Safety Center, concluded that the technologies required for a high-power NEP system are immature and the attendant risks insufficiently quantified to justify initiating a flight project. For NEP to be available as a viable option to meet flight opportunities in the late 2030s / 2040s time frame, development of the key sub-systems must begin now. SNP has subdivided the NEP system into five Critical Technology Elements (CTE): the nuclear reactor, power conversion, power management and distribution, electric propulsion sub-system, and the primary heat rejection system. Development plans for each of these CTEs have been drafted which will serve as the template for a focused milestone-driven research and development campaign intended to advance each CTE to Technology Readiness Level (TRL) 5. This will be accomplished by building and testing hardware at relevant power levels (~ 1 MW) and for relevant durations (2,500 hours, ~10% of the required operational lifetime) and conducting numerical modeling of the CTEs anchored by the accumulated test data to predict system performance and reliability. Concurrent with this work, high-level coupled system/mission modeling will be carried out to refine the key performance parameters that the various CTEs must achieve. Non-advocate reviews will be held at milestone points to assess progress and inform down-select decisions. The strategy for formulating the TMP was described previously; this paper describes ongoing progress on the drafting and baselining of the plan, including key specific details.

Space Nuclear Propulsion↗
NASA NTRS2024

Applications of Remote Sensing for Land Use Planning Scenarios with Suitability Analysis

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate growing population and manage natural resources. Suitability analysis modeling is a widely used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends and land surface temperature (LST) from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current land use planning practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

remote sensing time series↗
NASA NTRSDate not supplied

Energy-Optimized Path Planning for Uas in Varying Winds Via Reinforcement Learning

In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.

trajectory planning↗
DOE OSTI2020

A Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles, and mobility-as-a-service concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool can help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions. This paper presents a description of the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

FY 2019 INL SITE SUSTAINABILITY PLAN

The mission of the Department of Energy (DOE) is to ensure the security and prosperity of the United States by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. This FY 2019 Idaho National Laboratory Site Sustainability Plan (SSP) was developed to enable and sustain Idaho National Laboratory’s (INL’s) core capabilities, including advancing nuclear energy, enabling clean energy deployment, and securing and modernizing critical infrastructure. In 2018, transitions with respect to the purpose and application of sustainability continued. On May 17, 2018, Executive Order (EO) 13834, “Efficient Federal Operations,” was issued, which directs agencies to focus priorities on statutory sustainability requirements. The new order requires further guidance and implementation instructions, both of which are forthcoming. Fiscal year (FY) 2019 is expected to be a year of transition due to the new implementing instructions and other changes in guidance. The evolving priorities for sustainability are being incorporated into planning for FY 2019 and beyond as noted in this SSP. EO 13834 changes were considered in completing planned sustainability work at the end of FY 2018. INL remains responsible for existing directives, instructions, and requirements. From energy reduction to ensuring a reliable power supply, all sustainable activities support energy resiliency and by default, make INL a more resilient institution. The intent of this SSP is to provide the overall sustainability strategy for INL during FY 2019 and provide a status of FY 2018 performance to the DOE goals. The FY 2018 performance status is derived from data input to the DOE Sustainability Dashboard (Dashboard).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Energy Master Planning: Identifying Framing Constraints that Scope Your Technology Options

This paper addresses the framing constraints for building and community energy projects that must be considered when energy master planning (EMP) is conducted. The constraints cover emissions, sustainability, resilience, regulations and directives, and regional and local limitations such as available energy types, local conditions, and project requirements. The paper reflects development results from participants in an International Energy Agency project on energy master planning and in a U.S. Department of Defense project on technology integration to achieve resilient, low-energy use military installations. It identifies a comprehensive list of framing constraints categorized into locational threats, locational resources, energy and water distribution and storage systems, building and facility, indoor environmental, and equipment in buildings and district systems constraints. In addition, it identifies limits for these constraints that exist in seven participating countries. Some framing constraints can profoundly impact technology selection while others impact the installation of technologies (as in hardening) and have little to no impact on technology selection. Furthermore, framing constraints can be assessed in different ways and there are resources available to help EMP stakeholders evaluate them. Finally, a case is made that identifying and applying framing constraints early in EMP can bring efficiencies and better focus to the EMP process. We come to the conclusion that (1) for holistic energy planning, it is essential to identify and assess the framing constraints that bound an optimized EMP solution, (2) framing constraints limits should be evaluated as either hard or soft or promising technologies may fall out of an EMP analysis, (3) to maintain consistent quality in the EMP process, the identification of framing constraints and their limits, and perhaps their evaluation, should be standardized, (4) a standardized approach could establish a baseline that can be used, built upon, and improved, (5) as automated EMP tools are improved or developed, the resources in this paper could possibly contribute to their interworkings relative to technology screening, and (6) continued climate change and resulting aggressive goal setting will likely drive a continued and strong emphasis on EMP.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

State and Local Planning for Energy (SLOPE) Platform: Overview

The State and Local Planning for Energy (SLOPE) Platform, a collaboration between eight U.S. Department of Energy (DOE) technology offices and the National Renewable Energy Laboratory (NREL), is a tool to enable more data-driven state and local energy planning by integrating dozens of distinct sources of energy efficiency, renewable energy, and sustainable transportation data and analyses into an easy-to-access online platform that more effectively supports state and local energy planning and decision making.

State and Local Planning for Energy Platform, SLOP↗
DOE OSTI2020

FY 2021 Idaho National Laboratory Site Sustainability Plan

The mission of the Department of Energy (DOE) is to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. This FY 2021 Idaho National Laboratory Site Sustainability Plan (SSP) was developed to enable and sustain Idaho National Laboratory’s (INL’s) mission to discover, demonstrate, and secure innovative nuclear solutions, clean energy options, and critical infrastructure. The SSP was developed according to the narrative requirements from the “FY 2021 DOE Site Sustainability Plan Guidance” document issued in August 2020. The SSP contains strategies and activities that will lead to continual energy, water, and waste reductions that move INL toward meeting DOE sustainability goals and requirements. The SSP summarizes energy and available fuel use reporting requirements and references criteria for instituting sustainable design. SSP requirements are integrated into each INL contractor’s Integrated Safety Management System and Environmental Management System (EMS). Finally, the Sustainability Program directives, based on this SSP, are integrated into the INL Annual Laboratory Plan Fiscal Year 2020 (INL/LTD-20-59747), and operations and acquisition systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Flexible Plant Operation and Generation Technical Program Plan for FY2023

This report presents the Technical Program Plan for Fiscal Years 2023-2027 (FY2023 to FY2027) for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability Program—Flexible Plant Operation and Generation Research Pathway. The objective of this pathway is to carry out the research needed to help nuclear power plants diversify revenue generation for the life of these plants. The purpose of these research and development activities is two fold: (1) to reduce the technical and economic risks of implementing FPOG applications and (2) to provide guidance on relevant safety and environmental operating license reviews, amendments, and renewals. This pathway provides a clear understanding of the benefits of nuclear energy beyond electricity markets. A detailed description of the research and development activities that have been completed and that are planned for FY2023—FY2027 are presented in this report. These activities include completing the development of analysis tools to perform technical and economic assessments of realistic market opportunities for producing secondary energy products near nuclear power plants. They also include developing and demonstrating engineering systems and control concepts to dispatch thermal and electrical power to an industrial user. Additionally, this plan includes developing guidance for addressing potential regulatory and licensing requirements. In addition, the formation, purpose, and activities of a group referred to as the Hydrogen Regulatory Research and Review Group is discussed. An overview is also provided on the potential benefits of the Infrastructure Investment and Jobs Act (IIJA) Bill that will support the commencement of Regional Clean Hydrogen Hubs, and the Inflation Reduction Act (IRA) that provides compelling production tax credits for nuclear electricity and clean hydrogen using nuclear energy.

08 HYDROGEN↗
DOE OSTI2017

Near-Term Operational Planning at the Hanford Tank Farms: Link to Execution - 17091

Washington River Protection Solutions, LLC uses an earned value management system to flow-down contractual requirements to operational activities. A Rolling Work Window Schedule Process is then used to plan and coordinate near-term operational activities at the Hanford tank farms for execution. This near-term operational planning process has been extended using a Multi-Year Operating Plan, underpinned with a dynamic simulation model, to better link execution of daily activities with mission objectives. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Picture Speaks 360,000 Words - Virtual Reality as a Decommissioning Planning Tool - 19364

Whiteshell Laboratories, located in Manitoba, Canada, provided research facilities for the Canadian nuclear industry since the early 1960's. Decommissioning of Whiteshell Laboratories has been ongoing since 2002 under Atomic Energy of Canada (AECL) and, more currently, since 2015 under the WL Closure Project managed by Canadian Nuclear Laboratories (CNL). One of the key steps to ensuring safe decommissioning operations is effective planning, including pre-job briefings, and post-job verification and review. Effective planning involves clear and concise communication of the field conditions, system configurations, hazards, and task goals or achievements to front line employees, and task approvers and verifiers. In many instances, employees can enter, view, inspect, verify or otherwise familiarize themselves with the task and task location. However, in several cases, hazardous conditions in the task location make it difficult for employees to easily access the location themselves, due to hazardous conditions, PPE requirements, training qualification gaps or space limitations. Since 2016, 360 deg. cameras have been used to create a virtual 'tour' of the hazardous task locations at Whiteshell Laboratories, which can be viewed by one or all of the employees involved in a task, from a position of safety. This allows a task to be discussed with all employees involved without the need for added risk of exposure to dose or industrial hazards or costly PPE, and supports ALARA control techniques of removing the hazard by moving work planning walk downs away from hazardous locations and guarding the worker by limiting the time a worker is exposed to the hazard. This paper provides an overview of the technologies used to capture and present the 360 virtual tours, the benefits from a safety, schedule and cost perspective, and the limitations of the current implementation that could be improved upon in a future iteration. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Is there a Perfect D and D Plan - 20100

Problems with many Deactivation and Demolition (D and D) projects arise not necessarily from the technical difficulty of the actions required in a given facility, but rather from lack of integration of the stove piped planning efforts; engineering, operations, environmental, radiological, human resources, etc. This paper will cover Polestar Technical Service, Inc. (Polestar) systems engineering approach to D and D planning based on the company's experience over the past 27 years. This systematic, four-pronged approach to planning is applicable to any D and D project and addresses several diverse topics in a cohesive and integrated manner. The four areas to be discussed are project scope, requirements, facility end state, work force and base operations. This approach progresses a facility through the typical DOE life-cycle from operations, through deactivation, into surveillance and maintenance (S and M) and to eventual demolition and site remediation. To achieve a final end state the approach brings together the efforts associated with identifying and managing the right set of requirements, detailing the scope of work, establishing the baseline, redefining the work processes, aligning the workforce resources with the work processes, and maintaining the base operations activities as the project progresses through the various life-cycle stages. Getting these building blocks right provides a solid framework for executing any D and D project. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Applications of data mining technology to enhance O and M: automatic planning of electrical isolation with deep learning - 255

Data mining technology is now readily available, thanks to high powered, low cost computing resource and useful coding tools. We study several applications of data mining to enhance operation and maintenance (O and M), such as abnormality prediction, efficiency diagnosis, document traceability, image recognition, etc. In field support, we try automatic planning of electrical isolation with deep learning. Currently, a skilled engineer plans electrical isolation procedure with hundreds of the circuit diagrams and the related documents, taking man-hours. If this task becomes automatic, it is very efficient. The automatic planning has two issues. One is much calculation time of electrical circuit simulator, searching billions of electrical conducting paths. The other is interpretation of human fuzzy information, such as ease of work. Deep learning is helpful to resolve them. We performed a model case study of the electrical isolation. We applied a deep neural network (DNN) for dropping in the calculation time. We trained the circuit diagrams and the responses of the circuit simulator to the DNN, constructing an efficient path search algorithm in the DNN. The calculation time of the DNN was shorter by a factor of 32 for the model case, compared with that of the electrical circuit simulator. We applied a deep Q-network (DQN) for the interpretation of fuzzy information. The DQN optimized isolation procedure from the viewpoint of the work load with the equipment layout, related doses, and values of wet bulb globe temperature (WBGT) to prevent heatstroke. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

AFC Metallic Fuel Research and Development 5-Year Plan

The mission of the U.S. Department of Energy (DOE) Advanced Fuel Campaign (AFC) program is conducting R&D on nuclear fuel technology that enables near- and long-term implementation of the reactor systems necessary to meet national nuclear energy objectives. Its primary goals align with goals of the DOE Office of Nuclear Energy in sustaining the current LWR fleet through Accident Tolerant Fuels program and Enabling Advanced Reactors. The latter goal is to be achieved through the following subgoals in order of logistical priority: • Establish the qualification basis for reference metallic fuel designs for sodium-cooled fast reactors. • Develop next generation metallic fuel fabrication and design for improved fissile utilization and management. • Develop accelerated fuel development and qualification methodologies. • Identify next generation fuel technologies. The purpose of this document is to serve as a five-year research and development (R&D) plan to achieve the goals identified to support enabling advanced reactor deployment related to metallic fuels starting in 2025. The specific objectives of this document are to: • align program R&D work across technical areas, national laboratories, and with stakeholder interests, and • aid yearly and outyear scope and budgetary planning activities. This plan lays the foundation of databases, capabilities, expertise, and research-commercial-regulatory integration for launching next-generation initiatives in advanced fuel technologies (fuel and cladding) and improved methodologies for achieving accelerated qualification of next generation fuel technologies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Supercharging Your ZEV Transition: Using the ZEV Ready Planning Process

The ZEV Ready Framework is a step-by-step planning process for federal fleet electrification. The planning phase covers team coordination, training, policies, financial planning, and identifying candidates for zero-emission vehicles and electric vehicle supply equipment.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

Equity-driven Planning of Distributed Solar PV using Optimal Transport

Typically, distribution system planning processes do not explicitly incorporate energy equity considerations, such as identifying consumers most affected by energy costs and determining how investments in the distribution system can address existing energy burden imbalances. This paper proposes a novel optimal transport (OT)-based method to improve the energy burden distribution of consumers. The approach involves the strategic siting and sizing of solar PV in order to assist customers with high energy burden and improve the overall energy burden distribution of the community. The desired energy burden distribution is defined using the equal distribution equivalent (EDE) concept. The OT-based method is then used to estimate the distributed solar PV capacity to be installed at various locations and the tariffs to be adjusted, all while improving the energy burden distribution and providing valuable insights into distributed generation (DG) planning. The results on IEEE 37 bus test system demonstrate how DG planning, considering EDE and OT, can help reduce the energy burden of low-income consumers. Additionally, the approach also reveals optimal tariff adjustments needed to ensure revenue neutrality for distribution utilities.

Optimal transport, equal distribution equivalent, ↗