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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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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

JUST-R metrics for considering energy justice in early-stage energy research

We report achieving sustainable decarbonization of the energy sector requires implementing and improving energy technologies while simultaneously managing sources of social inequity in the energy system. Centering energy justice, which has "the goal of achieving equity in both the social and economic participation in the energy system, while also remediating social, economic, and health burdens on those historically harmed by the energy system," in the transition to clean energy has become an increasingly urgent priority for social scientists, policymakers, and community activists alike. However, late-stage consideration of social impacts of energy technologies may result in identifying inequities only after substantial time, money, and effort have been expended on research and development (R&D). This issue is exemplified by concerns over environmental and human health impacts related to cobalt in lithium-ion batteries, which has spurred research into alternatives only after decades of R&D and the establishment of supply chains, infrastructure, and markets for cobalt-containing chemistries. Other examples include issues with land use and resource consumption related to first-generation biofuel feedstocks as well as occupational hazards and pollution associated with photovoltaics manufacturing. In all these cases, subsequent R&D to improve technologies or processes cannot undo the effects already experienced. Incorporating energy justice from the earliest stage of R&D will enable more just technology implementation, but integrating justice considerations into early-stage research is a challenge due to a lack of tools to assess and manage them. To fill this gap, we center early-stage research to develop the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - energy justice metrics specifically targeted at early-stage researchers to assess their work on an immediate timescale. By applying these metrics to a case study focused on materials for next-generation photovoltaics, we highlight potential benefits and barriers to implementing this framework in early-stage research and discuss necessary institutional and individual actions needed for researchers to effectively leverage the tool to incorporate justice-focused criteria into R&D decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Citywide Impacts of E-Commerce: Does Parcel Delivery Travel Outweigh Household Shopping Travel Reductions?

E-commerce has facilitated online ordering of goods by households in recent years. This technological advancement has disrupted shopping related transportation. While the National Household Travel Survey (NHTS [1]) finds that household shopping frequency has declined in the last 10-20 years, deliveries by parcel delivery trucks and vans [2] have increased. However, the net effect of these phenomena on overall trip making, vehicle-miles traveled (VMT) and fuel consumption has not been quantified. From a regional planning perspective, understanding the net effect is important for informing city policies--for example, in regards to land use and transportation planning. The objective of this research is to address this gap. In this study, the net regional impact of e-commerce on transportation and fuel consumption is evaluated. The approach relies on a powerful, agent-based modeling framework (POLARIS [3]) that models decisions made by individual household and commercial agents. E-commerce demand is modeled for each household using a bilevel multinomial probit structure that evaluates e-commerce participation and ordering frequency. Last-mile delivery tours were constructed using GIS-based tools and information from a major parcel delivery company [4]. After integrating the resulting supply and demand models with all other passenger and commercial traffic within POLARIS, a traffic simulation was performed and subsequently VMT and energy consumption were analyzed. The study finds that while e-commerce has generated an increase in parcel truck delivery trips, the net effect of e-commerce is a reduction in VMT and fuel consumption due major reductions in these quantities via shopping trip reductions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Changing Institutional Procurement Behavior to Achieve Energy Savings

Although federal policies regarding energy-efficient product procurement (EEPP) are long-standing and well-established, federal buyers do not typically request energy-efficient products when making purchases. This is a large missed opportunity: full compliance could save the U.S. federal government roughly $500 million in energy cost annually. In 2016, the U.S. Department of Energy’s Federal Energy Management Program (FEMP) began a new set of program activities to increase federal compliance with these procurement requirements. Program interventions included increased communication with contracting officers during the solicitation process; development of enhanced, targeted training; and collection and dissemination of procurement best practices across the federal sector. We have collected and analyzed ~2,500 solicitations from over 40 different federal agencies to evaluate the influence of these interventions. Compared to pre-intervention years (FY15 and FY11), annual compliance rates for FY16 and FY17 increased by about 10%. This paper provides an overview of the data collection process, analysis framework, intervention method, and results of our analysis. We discuss strategies to increase compliance rates by examining institutional factors that drive procurement behavior. By combining data collection with adjustment to program implementation, we have created an iterative process that is having a demonstrable effect in improving the impact of a long-running program.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Acquisition of Scientific Equipment

Whitworth University constructed a 63,00 sq. ft. biology and chemistry building which opened in the Fall of 2011. This project provided for new state-of-the-art science instrumentation enabling Whitworth students to develop skills and knowledge that are directly transferable to practical applications thus enhancing Whitworth student's ability to compete and perform in the scientific workforce. Additionally, STEM faculty undertake outreach programs in the area schools, bringing students to our campus to engage in activities with our science students. The ability to work with insturmentation that is current helps to make science exciting for middle school and high school students and gets them thinking about careers in science. 14 items were purchased following the university's purchasing policy, that benefit instruction and research in the departments of biology, chemistry, and health sciences. They are: Cadaver Dissection Tables with Exhaust Chamber and accessories, Research Microscope with DF DIC, Phase and Fluorescence illumination with DP72 Camera, Microscope with Fluorescence, Microcomputer controlled ultracentrifuge, Ultracentrifuge rotor, Variable Temperature steam pressure sterilizer, Alliance APLC System, DNA Speedvac, Gel Cocumentation System, BioPac MP150, Glovebox personal workstation,Lyophilizer, Nano Drop 2000/2000c Spectrophotometer, C02 Incubator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Workshop on Establishing and Operating a National Nuclear Security Support Centre Hypothetical Scenario: "Republic of Centralia Nuclear Security Support Centre Feasibility Report"

[This is part of a hypothetical, scenario-based exercise for workshop participants, based on the fictitious country "Centralia."] This report serves as official record of the coordinated process completed by competent authorities and other organizations with nuclear security responsibilities in the Republic of Centralia to determine the feasibility of establishing and operating a national nuclear security support centre (NSSC). The report summarizes all sustainability needs and available resources identified during the feasibility determination process, including consideration of possible NSSC institutional models, in line with the systematic approach recommended by the International Atomic Energy Agency (IAEA). Centralia Nuclear Regulatory Authority (CNRA), as the designated lead organization for coordinating the feasibility determination process, has prepared this report in collaboration with members of the Committee on Nuclear Security (CNS) and based on input gathered among relevant national stakeholders. The report has been presented to the National Security Advisor (NSA), for final review and decision-making as to whether Centralia should proceed with establishing an NSSC.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1992

Software-Engineering Process Simulation (SEPS) model

The Software Engineering Process Simulation (SEPS) model is described which was developed at JPL. SEPS is a dynamic simulation model of the software project development process. It uses the feedback principles of system dynamics to simulate the dynamic interactions among various software life cycle development activities and management decision making processes. The model is designed to be a planning tool to examine tradeoffs of cost, schedule, and functionality, and to test the implications of different managerial policies on a project's outcome. Furthermore, SEPS will enable software managers to gain a better understanding of the dynamics of software project development and perform postmodern assessments.

Lin, C. Y.↗
NASA NTRS2022

SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots

Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym ( So ft Mo tion Gym ), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.

Moritz A. Graule↗
DOE OSTI2014

Cost Control Strategies for Zero Energy Buildings: High-Performance Design and Construction on a Budget (Brochure)

There is mounting evidence that zero energy can, in many cases, be achieved within typical construction budgets. To ensure that the momentum behind zero energy buildings and other low-energy buildings will continue to grow, this guide assembles recommendations for replicating specific successes of early adopters who have met their energy goals while controlling costs. Contents include: discussion of recommended cost control strategies, which are grouped by project phase (acquisition and delivery, design, and construction) and accompanied by industry examples; recommendations for balancing key decision-making factors; and quick reference tables that can help teams apply strategies to specific projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

NREL's System Advisor Model Simplifies Complex Energy Analysis (Fact Sheet)

NREL has developed a tool -- the System Advisor Model (SAM) -- that can help decision makers analyze cost, performance, and financing of any size grid-connected solar, wind, or geothermal power project. Manufacturers, engineering and consulting firms, research and development firms, utilities, developers, venture capital firms, and international organizations use SAM for end-to-end analysis that helps determine whether and how to make investments in renewable energy projects.

14 SOLAR ENERGY↗
DOE OSTI2018

Proceedings of the Future Markets Workshop

The inaugural Future Markets Workshop on Flexibility, held on July 26, 2017 in Washington DC, addressed questions on how power markets must transform when energy flexibility becomes increasingly valuable relative to energy production. Researchers were asked to submit their original analyses that explored concepts for how to specify, obtain, and compensate flexibility in electricity power systems. After broad marketing of the call for abstracts, presentations were selected based by a panel of experts in their respective fields. Presenters were invited to make a presentation at the Workshop and to write a full paper for this publication. Specific topics included quantifying flexibility, market design consumer interactions, financial mechanisms, and policy and regulation. Inclusion of a presentation or paper in this document does not imply endorsement of those ideas by either the US Department of Energy or the Siebel Energy Institute. Rather, their selection reflects the review panel’s judgement that these ideas have the potential to materially improve the operation of the power grid. This proceedings documents includes the full presentations that were given at the National Press Club on July 26, 2017 as well as the optional papers that were submitted by September 30, 2017.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration: Preprint

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions, i.e., the reserve requirement, renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained off-line using historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine and battery. Case studies demonstrated that the proposed method outperforms other operating reserve determination methods.

distribution system↗
DOE OSTI2019

Inventory of Radioactive Waste in Canada - 19336

Since 1999, the Government of Canada has been conducting a regular exercise of gathering and compiling Canada's radioactive waste inventory and projection data from waste owners and making it available to the public in the form of a triennial Inventory Summary Report. The goal of this data collection exercise has been to support federal policy development, support the Canadian Nuclear Safety Commission's regulatory activities, provide consolidated radioactive waste information for the nuclear industry, promote transparency, and meet Canada's international commitments and growing demands internationally for radioactive waste data. The detailed national inventory report provides an overview of the production, accumulation and future projections of radioactive waste in Canada. Since the coming into force of the Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management in June 2001, the data collection and reporting cycle has been aligned to provide the waste inventory data required for Canada's triennial National Report for the Joint Convention. This paper presents Canada's radioactive waste inventory and projection data as of the end of December 2016, and summarizes key findings from the analysis of the data. For this reporting cycle, the waste data collected was expanded to include more detailed information on the decommissioning schedules and associated waste volume projections for nuclear power plants, and research and prototype reactors, as well as plans for transitioning waste inventories from storage to long-term management and disposal facilities, based on current schedules for establishing these facilities in Canada. With this additional information, the Government of Canada and Canadian stakeholders are better able to understand the waste disposal capacity and schedules required to accommodate all nuclear fuel waste and radioactive waste that will be produced by Canada's existing fleet of nuclear power plants and research reactors. The collection and analysis of projected waste volumes to be emplaced in long-term management facilities is in line with and will support the proposed methodology of presenting waste inventories in terms of management strategy and disposal routes by the Organization for Economic Cooperation and Development Nuclear Energy Agency Expert Group on Waste Inventorying and Reporting Methodology. The paper describes some of the uses that are made of the waste inventory information, as well as Canada's approach to national inventory data collection and its experience and lessons learned. Canada continues to modernize the format of the Inventory Summary Report with the goal of disseminating the technical information into an attractive and public-friendly report. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

The changing Chinese SEA indicator guidelines: Top-down or bottom-up?

In the last decades, China has introduced a set of indicators to guide the Strategic Environmental Assessment (SEA) practice. The most recent indicator system proposed in 2009 is based on sector-specific guidelines and it found its justification in past negative experiences with more general guidelines (from 2003), which were mostly inspired by, or copied from, international experiences. Based on interviews with practitioners, researchers and administrators, we map and analyse the change in the national guidelines. This analysis is based on a description of the indicators that makes it possible to discern different aggregation levels of indicators and then trace the changes occurring under two sets of guidelines. The analysis also reveals the reasons and rationales behind the changes found in the guidelines. This analysis is inspired by implementation theory and a description of some of the more general trends in the development of SEA and other environmental policies in a recent Chinese context. Beside a more top-down, intentional approach specifying indicators for different sectors based on Chinese experiences from the preceding years, another significant change, following the new guidelines, is a more bottom-up approach which gives more discretion to practitioners. This entails a call for practitioners to make decisions on indicators, which involves an interpretation of the ones present in sector guidance. Highlights: • Focusing on the new Chinese national SEA guidelines proposed in 2009 • Mapping and analysing the most recent change in the indicator system • Revealing the reasons and rationales behind the changes found in the new guidelines • A top-down intention specifying indicators for different sectors • A bottom-up effect in giving discretion and interpretation of using indicators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Demand forecasting for automotive sector in Malaysia by system dynamics approach

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

TRANSFORM: Catalyzing the Transition to a 21st Century Energy Economy - JISEA 2019 Annual Report

The Joint Institute for Strategic Energy Analysis (JISEA) provides leading-edge, objective, high impact research and analysis to guide transformative global energy investment and policy decisions. Through strategic insights and worldwide dialogue, JISEA explores the intersections of the environmental, social, financial, technological, and political elements of energy systems on the path to a clean energy economy. JISEA's work informs innovative solutions that advance the goals of sustainability, economic prosperity, and resilience. JISEA research and analysis provided decision-making support to industry, the financial sector, and government with a focus on the following strategic areas: Energy Systems Integration and Transformation, Energy in the Global Water-Food Nexus, and Circular Energy Economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Integrated Decision-Making Tool to Develop Spent Fuel Strategies for Research Reactors

IAEA Member States operating or having previously operated a Research Reactor are responsible for the safe and sustainable management and disposal of associated radioactive waste, including research reactor spent nuclear fuel (RRSNF). This includes the safe disposal of RRSNF or the corresponding equivalent waste returned after spent fuel reprocessing. One key challenge to developing general recommendations lies in the diversity of spent fuel types, locations and national/regional circumstances rather than mass or volume alone. This is especially true given that RRSNF inventories are relatively small, and research reactors are rarely operated at a high power level or duration typical of commercial power plants. Presently, many countries lack an effective long-term policy for managing RRSNF. This paper presents results of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP) #T33001 on Options and Technologies for Managing the Back End of the Research Reactor Nuclear Fuel Cycle which includes an Integrated Decision Making Tool called BRIDE (Back-end Research reactor Integrated Decision Evaluation). This is a multi-attribute decision-making tool that combines the Total Estimated Cost of each life-cycle scenario with Non-economic factors such as public acceptance, technical maturity etc and ranks optional back-end scenarios specific to member states situations in order to develop a specific member state strategic plan with a preferred or recommended option for managing spent fuel from Research Reactors.

Beatty, Randy L.↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗