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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Requirements and applications for robotic servicing of military space systems

The utility of on-orbit servicing of spacecraft has been demonstrated by NASA several times using shuttle-based astronaut EVA. There has been interest in utilizing on-orbit servicing for military space systems as well. This interest has been driven by the increasing reliance of all branches of the military upon space-based assets, the growing numbers, complexity, and cost of those assets, and a desire to normalize support policies for space-based operations. Many military satellites are placed in orbits which are unduly hostile for astronaut operations and/or cannot be reached by the shuttle. In addition, some of the projected tasks may involve hazardous operations. This has led to a focus on robotic systems, instead of astronauts, for the basis of projected servicing systems. This paper describes studies and activities which will hopefully lead to on-orbit servicing being one of the tools available to military space systems designers and operators. The utility of various forms of servicing has been evaluated for present and projected systems, critical technologies have been identified, and strategies for the development and insertion of this technology into operational systems have been developed. Many of the projected plans have been adversely affected by budgetary restrictions and evolving architectures, but the fundamental benefits and requirements are well understood. A method of introducing servicing capabilities in a manner which has a low impact on the system designer and does not require the prior development of an expensive infrastructure is discussed. This can potentially lead to an evolutionary implementation of the full technology.

Ledford, Otto C., Jr.↗
DOE OSTI2015

Benefits from R. and D. for D. and D. projects - 15089

CEA (French Alternative Energies and Atomic Energy Commission) is both the operator of important nuclear facilities all over the nuclear cycle, in charge of major new built or D and D projects and a R and D group with dynamic policy of technology transfer: second in France for patents deposit, active partnership agreements with industries, and support for Start-ups. The position of CEA in D and D is unique because of the number and the wide diversity of facilities under decommissioning, with some high level of contamination. Innovative solutions are being developed in 6 main axes to protect the operators, to minimize the overall costs and the volumes of waste: - Investigations in the facilities - Radiological measurement of waste - Robotics, laser cutting devices, virtual reality to simulate and choose the best way to operate, - Decontamination of soils and structures - Waste treatment and conditioning - Methods and Information Technology (IT)Tools for project and waste management The last developments are shown and examples of industrial applications given: encapsulation of Magnesium or vitrification of high level waste for UP1 retreatment plant, treatment of sodium for Phenix fast breeder, treatment of organic liquids, etc. CEA is willing to share actions in partnership with other operators or with industrials so they can use the results for other contracting authorities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Nuclear Forensics: Scientific Analysis Supporting Law Enforcement and Nuclear Security Investigations

In Nuclear Forensic Science, analytical chemists join forces with nuclear physicists, material scientists, radiochemists, and traditional forensic scientists, as well as experts in nuclear security, nuclear safeguards, law enforcement, and policy development, in an effort to deter nuclear smuggling. Nuclear forensic science, or “nuclear forensics,” aims to answer questions about nuclear material found outside of regulatory control, questions such as ‘where did this material come from?’ and ‘what is the intended use of the material?’ In this Feature, we provide a general overview of nuclear forensics, selecting examples of key “nuclear forensic signatures” which have allowed investigators to determine the identity of unknown nuclear material in real investigations.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

REopt Lite Web Tool Evaluates Photovoltaics and Battery Storage

Building on the success of the REopt renewable energy integration and optimization platform, NREL has developed a free, publicly available web version of REopt called REopt Lite. REopt Lite evaluates the economics of grid-connected photovoltaics (PV) and battery storage at a site. It allows building owners to identify the system sizes and battery dispatch strategy that minimize their life cycle cost of energy. This web tool also estimates the amount of time a PV and storage system can sustain the site's critical load during a grid outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Assessing the urban carbon footprint: An overview

All cities present environmental sustainability issues, above all regarding greenhouse gas (GHG) emissions, and specifically carbon dioxide (CO{sub 2}), that directly affect climate change. Consequently, it is very important to quantify and report their Carbon Footprint (CF) for implementing national and international policies/strategies aimed at mitigating and adapting these concerns. The Urban Carbon Footprint (UCF), indeed, has been recognized as the more valuable choice to inform, specifically, decision makers about city environmental sustainability. Several accounting systems and inventory methods have been taken into account to perform UCF, highlighting the complexity of the topic and generating very often confusion among users. In this context, the authors aim to summarize what has been done and what is going on with UCFs, trying to classify them according to some principal dimensions. Thus, they divide UFCs in two main categories namely: “spatial” or “direct”, with a limited amount of data requested, and “economic” or “life cycle based”, more or less data inclusive according to the accounting systems considered. Furthermore, they observe that there is not a “global agreed-upon protocol” yet, neither is there a specific model shared among researchers, even if some steps have been made towards this direction (Relative Carbon Footprint - RCF, Publicly Available Specification – PAS 2070 and Global Protocol for Community scale - GPC). Consequently, it is necessary to complete and standardize, in the short term, the accounting and reporting frameworks, in order to compare different UCFs for adopting shared climate strategies and actions at global level. - Highlights: •Cities present environmental sustainability issues. •GHG emissions - specifically carbon dioxide - directly affect climate change. •UCF is a tool to support policy makers towards city environmental sustainability. •There is not a “global agreed-upon protocol” to perform UFC. •There are still missing points to be considered to provide a sharing model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Community Planning for Solar: Toolkit Overview

UMass Clean Energy Extension (CEE) and its partners have designed the Community Planning for Solar Toolkit to help municipalities in Massachusetts and throughout the Northeast proactively plan for solar PV development in their communities. The tools and processes are designed with rural and suburban communities in mind, though some of the tools may also apply to urban settings. Community Planning for Solar empowers community residents and officials to take the lead in solar development by providing communities with the resources that can help them to: (1) Identify and prioritize locations in the community for solar development; (2) Evaluate various solar financing options and provide guidance on community benefits that best match community goals and needs; (3) Assess their community's unique resources, solar development options, goals, and preferences regarding solar development; (4)Develop a Community Solar Action Plan.

14 SOLAR ENERGY↗
DOE OSTI2020

Use of the Tool to Support Renewable Energy Project Development (RE Data Explorer)

The Renewable Energy (RE) Data Explorer is a user-friendly, geospatial analysis tool for analyzing renewable energy potential and informing decisions. Developed by the National Renewable Energy Laboratory (NREL) and supported by the U.S. Agency for International Development (USAID), RE Data Explorer performs visualization and analysis of renewable energy potential that can be customized for different scenarios. RE Data Explorer can support prospecting, integrated planning, policymaking, and other decision-making activities to accelerate renewable energy deployment. This document provides information on how the RE Data Explorer can support private sector utility-scale renewable energy project development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

U.S. Hydropower Workforce: Challenges and Opportunities

Over the past four years, the National Renewable Energy Laboratory (NREL) has engaged with the hydropower industry, academia, and students to understand the perspectives and challenges of the U.S. hydropower workforce. This engagement and research were sponsored by the U.S. Department of Energy's (DOE's) Water Power Technologies Office as part of an NREL-led project—Water Power Science, Technology, Engineering, and Mathematics (STEM) to Workforce—which has the goal of developing tools and programs to strengthen the water power workforce pipeline. This report shares findings from that research and provides an update on U.S. hydropower workforce trends as a follow-on to NREL's 2019 report, Workforce Development for U.S. Hydropower: Key Trends and Findings (Keyser and Tegen 2019). Recent data from NREL, the Hydropower Foundation's survey efforts, and the hydropower industry are presented.

13 HYDRO ENERGY↗
DOE OSTI2025

Ten questions concerning housing sufficiency

Housing sufficiency is an emerging concept in the provision of environmentally sustainable housing. It aims for demand-side strategies that reduce excessive, aggregate consumption levels to promote efficient resource utilization and sustainability in the construction sector while providing everyone with a decent standard of housing. However, it is challenging to implement as it interferes with housing-related social and cultural norms. This paper poses and answers ten questions that highlight the challenges, opportunities, and examples of sufficiency strategies in the context of housing provision and the environmental crisis. Question 1 discusses the need for sufficiency as a tool to complement supply-side strategies including efficiency and renewable energy strategies in providing housing. Question 2 examines the concept of housing sufficiency from different perspectives, such as ecological economics and social ecology. Question 3 summarizes the methods used to measure housing sufficiency, with specific focus on the upper and lower limits that respect planetary boundaries and basic human needs. Questions 4 and 5 describe the benefits, potential drawbacks, and rebound effects of housing sufficiency. Questions 6 and 7 examine housing sufficiency in developed and developing countries, respectively. Questions 8 and 9 discuss design strategies, housing occupancy, and tenure models that are needed for housing sufficiency. Finally, Question 10 provides a of policies and regulations that are needed to systematically support the implementation of sufficiency strategies. The questions and answers provide insights for wider application of housing sufficiency in research and practice.

Arceo, Aldrick↗
DOE OSTI2011

The effectiveness of the Finnish EIA system - What works, what doesn't, and what could be improved?

The article summarises the results of a multidisciplinary research project on the effectiveness of the Finnish EIA system. It examines the main strengths and weaknesses of EIA as a preventive and participatory environmental management tool. The study concludes that EIA has achieved a meaningful role in the environmental policy toolbox in Finland and has clearly enhanced the possibilities for high-quality environmental decision making. The research cites the liaison authority system as a clear strength of the Finnish EIA system in its enabling a single regional authority to specialise in and gain wide experience on EIA issues. In examining potential weaknesses of the regime, the article concludes that the key constraint on EIA effectiveness is inadequacy of the action-forcing mechanisms at the decision-making phase. The primary means to improve the effectiveness of EIA would be to strengthen the legal provisions on development consents. On the whole, the research indicates that the EU and Finnish legislation and guidance on environmental impact assessment provide a good framework for effective utilisation of the instrument.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

The NASA Orbital Debris Program Office - In Service of Space Safety

Since the NASA Orbital Debris Program Office’s (ODPO) founding in 1979 at the Johnson Space Center in Houston, Texas, it has been at the forefront of orbital debris research, modeling, and policy development. The ODPO has worked in collaboration with NASA and other U.S. government missions since the 1980s to mitigate the growth of the orbital debris environment and protect the population of the Earth. Two main products from the ODPO, the Orbital Debris Engineering Model (ORDEM) and the Debris Assessment Software (DAS), are frequently among the top three most-downloaded software packages from the NASA Software Catalog. These products are provided free of charge to the public in furtherance of the goal to ensure that new space missions, in compliance with NASA’s orbital debris mitigation requirements, are developed, operated, and disposed of responsibly. In addition to these external-facing software tools, the ODPO maintains high-fidelity internal tools for reentry simulation (the Object Reentry Survival Analysis Tool, ORSAT), short-term risk assessment for robotic and human spaceflight missions (the Satellite Breakup Risk Assessment Module, SBRAM), among others. Using data from the ORDEM model, as well as using the Meteoroid Environment Model (MEM) developed by the Meteoroid Environment Office (MEO) at NASA’s Marshall Space Flight Center, the Hypervelocity Impact Technology (HVIT) team uses the BUMPER code to assess penetration risk to space vehicles in Earth orbit and beyond. This paper will discuss the services that the ODPO and HVIT provide, from mission concept development through end-of-mission, for NASA-related and commercial missions.

Chris Ostrom↗
DOE OSTI2017

Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?

We present that analytics software is increasingly used to improve and maintain operational efficiency in commercial buildings. Energy managers, owners, and operators are using a diversity of commercial offerings often referred to as Energy Information Systems, Fault Detection and Diagnostic (FDD) systems, or more broadly Energy Management and Information Systems, to cost-effectively enable savings on the order of ten to twenty percent. Most of these systems use data from meters and sensors, with rule-based and/or data-driven models to characterize system and building behavior. In contrast, physics-based modeling uses first-principles and engineering models (e.g., efficiency curves) to characterize system and building behavior. Historically, these physics-based approaches have been used in the design phase of the building life cycle or in retrofit analyses. Researchers have begun exploring the benefits of integrating physics-based models with operational data analytics tools, bridging the gap between design and operations. In this paper, we detail the development and operator use of a software tool that uses hybrid data-driven and physics-based approaches to cooling plant FDD and optimization. Specifically, we describe the system architecture, models, and FDD and optimization algorithms; advantages and disadvantages with respect to purely data-driven approaches; and practical implications for scaling and replicating these techniques. Finally, we conclude with an evaluation of the future potential for such tools and future research opportunities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Review of LCA studies of solid waste management systems – Part I: Lessons learned and perspectives

Highlights: • We perform a critical review of 222 LCA studies of solid waste management systems. • Studies mainly concentrated in Europe with little application in developing countries. • Assessments of relevant waste types apart from household waste have been overlooked. • Local specificities of systems prevent a meaningful generalisation of the LCA results. • LCA should support recommendations representative of the local conditions. - Abstract: The continuously increasing solid waste generation worldwide calls for management strategies that integrate concerns for environmental sustainability. By quantifying environmental impacts of systems, life cycle assessment (LCA) is a tool, which can contribute to answer that call. But how, where and to which extent has it been applied to solid waste management systems (SWMSs) until now, and which lessons can be learnt from the findings of these LCA applications? To address these questions, we performed a critical review of 222 published LCA studies of SWMS. We first analysed the geographic distribution and found that the published studies have primarily been concentrated in Europe with little application in developing countries. In terms of technological coverage, they have largely overlooked application of LCA to waste prevention activities and to relevant waste types apart from household waste, e.g. construction and demolition waste. Waste management practitioners are thus encouraged to abridge these gaps in future applications of LCA. In addition to this contextual analysis, we also evaluated the findings of selected studies of good quality and found that there is little agreement in the conclusions among them. The strong dependence of each SWMS on local conditions, such as waste composition or energy system, prevents a meaningful generalisation of the LCA results as we find it in the waste hierarchy. We therefore recommend stakeholders in solid waste management to regard LCA as a tool, which, by its ability of capturing the local specific conditions in the modelling of environmental impacts and benefits of a SWMS, allows identifying critical problems and proposing improvement options adapted to the local specificities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage Systems

Monitoring and analyzing a wide range of I/O activities in an HPC cluster is important in maintaining mission-critical performance in a large-scale, multi-user, parallel storage system. Center-wide I/O traces can provide high-level information and fine-grained activities per application or per user running in the system. Studying such large-scale traces can provide helpful insights into the system. It can be used to develop predictive methods for making predictive decisions, adjusting scheduling policies, or providing decisions for the design of next-generation systems. However, sharing real-world I/O traces to expedite such research efforts leaves a few concerns; i) the cost of sharing the large traces is expensive due to this large size, and ii) privacy concern is an issue.We address such issues by building an end-to-end machine learn- ing (ML) workflow that can generate I/O traces for large-scale HPC applications. We leverage ML based feature selection and gener- ative models for I/O trace generation. The generative models are trained on I/O traces collected by the darshan I/O characterization tool over a period of one year. We present a two-step generation process consisting of two deep-learning models, called the feature generator and the trace generator. The combination of two-step generative models provides robustness by reducing the bias of the model and accounting for the stochastic nature of the I/O traces across different runs of an application. We evaluate the performance of the generative models and show that the two-step model can generate time-series I/O traces with less than 20% root mean square error.

Paul, Arnab↗