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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring the Potential of Rainwater Harvesting for Cooling Towers: A Systematic Review and Regional Feasibility Assessment

Water scarcity has driven interest in rainwater harvesting, especially for U.S. industries like power and manufacturing, which dedicate a large percentage of their water needs to cooling towers. This paper provides a comprehensive systematic review of studies on the use of rainwater in cooling tower applications, along with an assessment of the regional feasibility of integrating RWH in U.S. manufacturing cooling towers. The systematic review examines the technical, economic, environmental, and policy feasibility of utilizing harvested rainwater, and the regional feasibility analysis evaluates the practical implementation of RWH in manufacturing cooling towers, considering factors such as regional RWH potential, water costs, state policies, and the industrial water use of manufacturing facilities per state. This study supports the hypothesis that RWH for cooling towers is technically feasible, economically viable, and environmentally beneficial. Harvested rainwater is naturally less conductive and soft, and rainwater reuse minimizes the ecological footprint. Supportive state policies, regional RWH potential, and rising water costs across the U.S. are important variables that may impact RWH adoption. The review highlights rooftop RWH as the most studied method and notes that implementing RWH requires infrastructure changes and filtration techniques. While initial investment costs may be high, operational and maintenance costs are low, making RWHS economically feasible over time. Regions with higher water costs and supportive policies are more likely to benefit from RWH adoption. The study provides a foundation for understanding the potential for using RWH in industrial cooling towers.

Cooling towers↗
DOE OSTI2020

How do the research and public communities view biofuel development?

Understanding how public and research view biofuel development, especially the unsynchronized views, can explain the gaps between the actual biofuel production and consumption and the government mandates in the US. Applying a comparative text mining technique, these views are explored using 9924 news articles and 57,849 research abstracts. It is found both the public and research communities respond actively to major policy and incentive programs and market conditions and the public has been closely following important research advances. However, research and practice should be coordinated to achieve economies of scale for regular and alternative biofuels. Priority is particularly needed for policy research to support legislation and enforcement and remove technical barriers to commercialization; meanwhile, greater attention is needed to prompt the commercialization of mature technologies. In addition, sentiment analysis shows positive public perceptions in general and negative perceptions primarily stemming from fraud in biofuel tax-credit programs with minor concerns on unintended negative impacts of biofuel development and policy implementation issues. This study contributes to methodology by using cutting-edge text-mining techniques to the largest and most up-to-date datasets of public news articles and research publication abstracts so as to compare public and academic views on biofuel development. As a result, this study also contributes to knowledge discovery by providing new perspectives on biofuel development, which will support policy design and identify future research directions.

09 BIOMASS FUELS↗
DOE OSTI2020

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Detailed assessment of global transport-energy models’ structures and projections

This paper focuses on comparing the frameworks and projections from four major global transportation models with considerable transportation technology and behavioral detail. We analyze and compare the modeling frameworks, underlying data, assumptions, intermediate parameters, and projections to identify the sources of divergence or consistency, as well as key knowledge gaps. We find that there are significant differences in the base-year data and key parameters for future projections, especially for developing countries. These include passenger and freight activity, mode shares, vehicle ownership rates, and even energy consumption by mode, particularly for shipping, aviation and trucking. This may be due in part to a lack of previous efforts to do such consistency-checking and “bench-marking.” We find that the four models differ in terms of the relative roles of various mitigation strategies to achieve a 2°C / 450 ppm CO2e target: the economics-based integrated assessment models favor the use of low carbon fuels as the primary mitigation option followed by efficiency improvements, whereas transport-only and expert-based models favor efficiency improvements of vehicles followed by mode shifts. We offer recommendations for future modeling improvements focusing on (1) reducing data gaps; (2) translating the findings from this study into relevant policy implications such as feasibility of current policy goals, additional policy targets needed, regional vs. global reductions, etc.; (3) modeling strata of demographic groups to improve understanding of vehicle ownership levels, travel behavior, and urban vs. rural considerations; and (4) conducting coordinated efforts in aligning input assumptions and historical data, policy analysis, and modeling insights.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Building waste management core indicators through Spatial Material Flow Analysis: Net recovery and transport intensity indexes

Highlights: Black-Right-Pointing-Pointer Sustainability and proximity principles have a key role in waste management. Black-Right-Pointing-Pointer Core indicators are needed in order to quantify and evaluate them. Black-Right-Pointing-Pointer A systematic, step-by-step approach is developed in this study for their development. Black-Right-Pointing-Pointer Transport may play a significant role in terms of environmental and economic costs. Black-Right-Pointing-Pointer Policy action is required in order to advance in the consecution of these principles. - Abstract: In this paper, the material and spatial characterization of the flows within a municipal solid waste (MSW) management system are combined through a Network-Based Spatial Material Flow Analysis. Using this information, two core indicators are developed for the bio-waste fraction, the Net Recovery Index (NRI) and the Transport Intensity Index (TII), which are aimed at assessing progress towards policy-related sustainable MSW management strategies and objectives. The NRI approaches the capacity of a MSW management system for converting waste into resources through a systematic metabolic approach, whereas the TII addresses efficiency in terms of the transport requirements to manage a specific waste flow throughout the entire MSW management life cycle. Therefore, both indicators could be useful in assessing key MSW management policy strategies, such as the consecution of higher recycling levels (sustainability principle) or the minimization of transport by locating treatment facilities closer to generation sources (proximity principle). To apply this methodological approach, the bio-waste management system of the region of Catalonia (Spain) has been chosen as a case study. Results show the adequacy of both indicators for identifying those points within the system with higher capacity to compromise its environmental, economic and social performance and therefore establishing clear targets for policy prioritization. Moreover, this methodological approach permits scenario building, which could be useful in assessing the outcomes of hypothetical scenarios, thus proving its adequacy for strategic planning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Bioenergy Supply and Environmental Impacts on Cropland: Insights from Multi‐market Forecasts in a Great Lakes Subregional Bioeconomic Model

Abstract Using subregional models of crop production choices in central Wisconsin and southwest Michigan, we predict biomass production, land use, and environmental impacts with details that are unavailable from national scale models. When biomass prices are raised exogenously, we find that the subregional models overestimate the supply, the land use, and the beneficial environmental aspects of perennial biomass crops. Multi‐market price feedbacks tied to realistic policy parameters predict high threshold absolute prices for biomass to enter production, resulting in intensified production of biomass from annual grain crops with damaging environmental impacts. Multi‐market feedbacks also predict regional specialization in energy biomass production in areas with lower yields of food crops. Policies promoting biofuels will not necessarily generate environmental benefits in the absence of environmental regulations.

Egbendewe‐Mondzozo, Aklesso↗
DOE OSTI2016

Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. Here, we argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as United Nations population projections.This makes current models likely to miss important feedbacks in the real Earth–Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. Lastly, the importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth–Human system models for devising effective science-based policies and measures to benefit current and future generations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Reliability-Informed Life-Cycle Warranty Cost Analysis: A Case Study on a Transmission in Agricultural Equipment

In agricultural and industrial equipment, both new and remanufactured systems are often available for warranty coverage. In such cases, it may be challenging for equipment manufacturers to properly trade-off between the system reliability and the cost associated with a replacement option (e.g., replace with a new or remanufactured system). To address this problem, we present a reliability-informed life-cycle warranty cost (LCWC) analysis framework that enables equipment manufacturers to evaluate different warranty policies. These warranty policies differ in whether a new or remanufactured system is used for replacement in the case of product failure. The novelty of this LCWC analysis framework lies in its ability to incorporate real-world field reliability data into warranty policy assessment using probabilistic warranty cost models that consider multiple life cycles. First, the reliability functions for the new and remanufactured systems are built as the time-to-failure distributions that provide the best-fit to the field reliability data. Then, these reliability functions and their corresponding warranty policies are used to build the LCWC models according to the specific warranty terms. Finally, Monte Carlo simulation is used to propagate the time-to-failure uncertainty of each system, modeled by its reliability function, through each LCWC model to produce a probability distribution of the LCWC. The effectiveness of the proposed reliability-informed LCWC analysis framework is demonstrated with a real-world case study on a transmission used in some agricultural equipment.

agricultural equipment↗
DOE OSTI2019

Toward unrestricted use of public genomic data

Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Transferable Reinforcement Learning for Smart Homes: Preprint

To harness the great amount of untapped resources at the demand side, smart home technology plays a vital role in solving the "last mile" problem in smart grid. Reinforcement learning (RL), which has demonstrated an outstanding performance in solving many sequential decision-making problems, can be a great candidate to be used in smart home control. For instance, many studies have started investigating the load scheduling problem under dynamic pricing scheme. Based on those, this study aims at providing an affordable solution to encourage a higher smart home adoption rate. Specifically, we investigate combining transfer learning (TL) with RL to reduce the training cost of an optimal RL control policy. Given an optimal policy for a benchmark home, TL can jump-start the RL training of a policy for a new home, which has different appliances and user preferences. Simulation results show that by leveraging TL, RL training converges faster and requires much less computing time for new homes that are similar to the benchmark home. In all, this study proposes a cost-effective approach for training RL control policies for homes at scale, which ultimately reduces the controller's implementation costs, increases the adoption rate of RL controllers, and makes more homes grid-interactive.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2026

Data for Filling the Cellulosic Bio-economy Gap by Utilizing a Wedge Approach Combined with Stakeholder Collaboration

The price gap between the market and breakeven prices of cellulosic biomass for farmers represents a significant barrier to the development of a low-carbon cellulosic bioeconomy. Using a bottom-up, agent-based modeling tool that replicates the behaviors and interactions of key stakeholders, this study analyzes the emergence of a cellulosic bioeconomy at the local scale through a wedge approach that examines an integrated portfolio of multiple policy options, including subsidies for small-scale bioproducts and environmental credits. The role of collaboration among multiple stakeholders, such as biomass producers (farmers), bio-refinery industry, government, and society, is assessed for filling the price gap. Using the Sangamon River Basin as a case study site, we evaluate the effectiveness of the wedge approach by comparing simulation results from multiple scenarios, each incorporating different combinations of bioeconomy wedges, with and without stakeholder collaboration. Results underscore that active collaboration among stakeholders acts as a catalyst enlarging the effectiveness of bioeconomy wedges. Including the carbon credits and environmental value in the policy portfolio is found to bridge the price gap through collective contributions from diverse stakeholders, where the cellulosic biofuel and bioproduct industry plays a pivotal role. Although this study is conducted at the local watershed scale, the methodology and findings offer valuable insights for market development in other watersheds and the potential scaling of local markets to regional and national levels.

Economics↗
DOE OSTI2014

Understanding the Complexities of Subnational Incentives in Supporting a National Market for Distributed Photovoltaics

Subnational policies pertaining to photovoltaic (PV) systems have increased in volume in recent years and federal incentives are set to be phased out over the next few. Understanding how subnational policies function within and across jurisdictions, thereby impacting PV market development, informs policy decision making. This report was developed for subnational policy-makers and researchers in order to aid the analysis on the function of PV system incentives within the emerging PV deployment market. The analysis presented is based on a 'logic engine,' a database tool using existing state, utility, and local incentives allowing users to see the interrelationships between PV system incentives and parameters, such as geographic location, technology specifications, and financial factors. Depending on how it is queried, the database can yield insights into which combinations of incentives are available and most advantageous to the PV system owner or developer under particular circumstances. This is useful both for individual system developers to identify the most advantageous incentive packages that they qualify for as well as for researchers and policymakers to better understand the patch work of incentives nationwide as well as how they drive the market.

14 SOLAR ENERGY↗
DOE OSTI2015

Milestones for Selection, Characterization, and Analysis of the Performance of a Repository for Spent Nuclear Fuel and High-Level Radioactive Waste at Yucca Mountain.

This report presents a concise history in tabular form of events leading up to site identification in 1978, site selection in 1987, subsequent characterization, and ongoing analysis through 2009 of the performance of a repository for spent nuclear fuel and high - level radioactive waste at Yucca Mountain in southern Nevada. The tabulated events generally occurred in five periods: (1) commitment to mined geologic disposal and identification of sites; (2) site selection and analysis, based on regional geologic characterization through literature and analogous data; (3) feasibility analysis demonstrating calculation procedures and importance of system components, based on rough measures of performance using surface exploration, waste process knowledge, and general laboratory experiments; (4) suitability analysis demonstrating viability of disposal system, based on environment - specific laboratory experiments, in - situ experiments, and underground disposal system characterization; and (5) compliance analysis, based on completed site - specific characterization . The current sixth period beyond 2010 represents a new effort to set waste management policy in the United States. Because the relationship is important to understanding the evolution of the Yucca Mountain Project , the tabulation also shows the interaction between the policy realm and technical realm using four broad categories of events : (a) Regulatory requirements and related federal policy in laws and court decisions, (c) Presidential and agency directives, (c) technical milestones of implementing institutions, and (d) critiques of the Yucca Mountain Project and pertinent national and world events related to nuclear energy and radioactive waste. Preface The historical progression of technical milestones for the Yucca Mountain Project was originally developed for 10 journal articles in a special issue of Reliability Engineering System Safety on the performance assessment for the Yucca Mountain license application [1-10]. The listing of mile stones, a distinct feature of those articles, has been collected and tabulated here. A lthough a brief description is presented here (based on the summaries in the 10 journal articles), the emphasis remains on the tabulation because of its usefulness in providing a comprehensive but concise history of the Yucca Mountain Project. The tabulation presented here is more elaborate than originally presented in that many of the interactions that occurred between the technical realm and policy realm can be depicted in separate columns. The usefulness of the milestones table is due in part to L.A. Connolly, for editorial and reference support, and S.K. Best, Raytheon, and L. Mays, Sandia National Laboratories (SNL), for illustration support. Reviewers P.N. Swift, SNL, and K. Gupta, University of Oklahoma, helped improve the discussion. The historical perspective presented is that of the author and is not necessarily held by reviewers, Sandia National Laboratories , and the US Department of Energy. As a historic perspective, the author is reporting on the work of others; however, any interpretative error s of the documentation are those of the author alone. The characterization and modeling of the Yucca Mountain disposal system required numerous participants with expertise in many areas of science and technology, as evident from the extensive reference list. Their diligent efforts are generally acknowledged here and through the many references to their impressive work, but the 10 journal articles acknowledge by name many of the numerous participants that contributed to the Yucca Mountain Project .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Modeling the Capacity and Emissions Impacts of Reduced Electricity Demand. Part 1. Methodology and Preliminary Results

Policies aimed at energy conservation and efficiency have broad environmental and economic impacts. Even if these impacts are relatively small, they may be significant compared to the cost of implementing the policy. Methodologies that quantify the marginal impacts of reduced demand for energy have an important role to play in developing accurate measures of both the benefits and costs of a given policy choice. This report presents a methodology for estimating the impacts of reduced demand for electricity on the electric power sector as a whole. The approach uses the National Energy Modeling System (NEMS), a mid-range energy forecast model developed and maintained by the U.S. Department of Energy, Energy Information Administration (EIA)(DOE EIA 2013). The report is organized as follows: In the rest of this section the traditional NEMS-BT approach is reviewed and an outline of the new reduced form NEMS methodology is presented. Section 2 provides an overview of how the NEMS model works, and describes the set of NEMS-BT runs that are used as input to the reduced form approach. Section 3 presents our NEMS-BT simulation results and post-processing methods. In Section 4 we show how the NEMS-BT output can be generalized to apply to a broader set of end-uses. In Section 5 we disuss the application of this approach to policy analysis, and summarize some of the issues that will be further investigated in Part 2 of this study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗