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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Impact of Parcel Size, Field Shape, Crop Yield, Storage Location, and Collection Equipment on the Performance of Single-Pass Cut-and-Chip Harvest System in Commercial Shrub Willow Fields

In this paper, an integrated system of harvesting, collecting, and transporting willow biomass crop to a storage site was modeled and evaluated using the IBSAL simulation model. A scenario analysis was used to quantify the impacts of five major input parameters on the performance of the integrated system. These parameters include parcel size, field shape, willow yield, distance to the storage site, and type of the collection equipment. Multiple performance indicators were identified to quantify the impacts on the system such as size of the equipment fleet, effective material and field capacity of the harvester, operating costs, and waiting times. The input data were collected from 36 commercial, short-rotation, shrub willow fields in northern New York State. The simulation results indicated that crop yield and type of collection equipment have the highest impact on operating costs and the equipment fleet size. As the size of equipment fleet increases in the system, variability in the system performance tends to increase. Field shape has the least impact on the overall system performance compared to the other four input parameters. The simulation results suggest that a combination of performance indicators need to be considered to evaluate the overall performance of the dynamic and complex system of harvesting, collection and transportation in commercial willow fields. The developed IBSAL model and scenario analysis approach can assist in planning this system based on the characteristics of field, crop, and logistical equipment to reach a high system performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Energy and emissions saving potential of additive manufacturing: the case of lightweight aircraft components

Additive manufacturing (AM) holds great potential for improving materials efficiency, reducing life-cycle impacts, and enabling greater engineering functionality compared to conventional manufacturing (CM), and AM has been increasingly adopted by aircraft component manufacturers for lightweight, cost-effective designs. This study estimates the net changes in life-cycle primary energy and greenhouse gas emissions associated with AM technologies for lightweight metallic aircraft components through the year 2050, to shed light on the environmental benefits of a shift from CM to AM processes in the U.S. aircraft industry. A systems modeling framework is presented, with integrates engineering criteria, life-cycle environmental data, aircraft fleet stock and fuel use models under different AM adoption scenarios. Estimated fleet-wide life-cycle primary energy savings at most reach 70-173 million GJ/year in 2050, with cumulative savings of 1.2–2.8 billion GJ. Associated cumulative GHG emission reductions were estimated at 92.1–215.0 million metric tons. In addition, thousands of tons of aluminum, titanium and nickel alloys could be potentially saved per year in 2050. The results indicate a significant role of AM technologies in helping society meet its long-term energy use and GHG emissions reduction goals, and highlight barriers and opportunities for AM adoption for the aircraft industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Modification of MELCOR for severe accident analysis of candidate accident tolerant cladding materials

A number of materials are currently under development as candidate accident tolerant fuel and cladding for application in the current fleet of commercial light water reactors (LWRs). The safe, reliable and economic operation of the nation’s nuclear power reactor fleet has always been a top priority for the nuclear industry. Continual improvement of technology, including advanced materials and nuclear fuels, remains central to the industry’s success. Enhancing the accident tolerance of light water reactors became a topic of serious discussion following the 2011 Great East Japan Earthquake, resulting tsunami, and subsequent damage to the Fukushima Daiichi nuclear power plant complex. The overall goal for the development of accident tolerant fuel (ATF) systems for LWRs is to identify alternative fuel system technologies to further enhance the safety, competitiveness, and economics of commercial nuclear power. Designed for use in the current fleet of commercial LWRs, or in reactor concepts with design certifications (GEN-III+), to achieve their goal enhanced ATF must endure loss of active cooling in the reactor core for a considerably longer period of time than the current fuel system, while maintaining or improving performance during normal operation. Many available nuclear fuel performance analysis tools are specifically developed for the current UO 2 – Zirconium alloy fuel system. The MELCOR severe-accident analysis code, under development at the Sandia National Laboratory in New Mexico (SNL-NM) for the US Nuclear Regulatory Commission (NRC), is one of these tools. This article describes modifications made to a version of the MELCOR code by the Idaho National Laboratory (INL) that allows its application to alternate fuel-cladding systems. Preliminary analysis results are presented in this paper for candidate silicon carbide and FeCrAl cladding concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Repositioning Method for Shared Autonomous Vehicles Operation

Shared fully-automated, or autonomous, mobility (SAVs) is anticipated to be the likely choice for future urban travel. SAVs boast many operational benefits but will add congestion in the form of unoccupied miles. The fleet's success further depends on service measures like the wait times for pickup trips. Agent-based simulation tools have closely looked at SAV operations but typically lack the integration between the supply and demand sides when simulating a population at scale. This paper focuses on the impact of SAV relocation on traveler wait times using a novel algorithm for repositioning. POLARIS, an agent-based tool, is used for a case study of Bloomington, Illinois to quantify the benefits of allowing SAV repositioning. On average, the wait times were lower with repositioning for all adequate fleet sizes. SAVs were available more uniformly across the region's zones, and proportional to trip-making at different times of day. In addition, with repositioning enable a higher share of demands were served. Finally, the increase in empty fleet miles from SAV repositioning may be justified with more trips being served, and an overall improvement in SAV wait times. (C) 2020 The Authors. Published by Elsevier B.V.

Agent-Based Simulation↗
DOE OSTI2022

Developing a heavy-duty vehicle activity database to estimate start and idle emissions

Heavy-duty vehicle start and idling activities were characterized from two datasets to improve emission estimates in the MOtor Vehicle Emission Simulator (MOVES): 1. Fleet DNA from the National Renewable Energy Laboratory (NREL) and 2. A dataset collected by the University of California, Riverside for the California Air Resources Board. Furthermore, the combined dataset includes 564 commercial vehicles, over 23,000 vehicle days of operation and covers seven of the nine heavy-duty source types defined by MOVES. The start and idle activities are characterized and illustrated across MOVES source types, vocations, fleets, days, and hours. This study provides the most comprehensive analysis yet made publicly available to characterize start and idle activity for heavy-duty vehicles within the United States. The results also show there is significant uncertainty in the average heavy-duty idle and start activity due to the large variation in activity across fleets and vocations, and sparsity of nation-wide vehicle population data by vocation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Supporting mobile WEEE collection on demand: A method for multi-criteria vehicle routing, loading and cost optimisation

Highlights: • Multi-criteria method of WEEE mobile collection on demand. • Vehicle routing and loading problem included in the model and algorithm. • Optimisation of collecting vehicles routes including genetic algorithms and fuzzy logic. • Case study for heterogeneous fleet of collection vehicles for medium size city in Poland. • Possible application in a software supporting waste collection on demand. - Abstract: The task of increasing collection rates of waste electrical and electronic equipment (WEEE) is an important challenge in the global economy, and especially in the European Union where stiffer collection targets set out in a new WEEE directive are to be effective by 2019. As the circular economy approach replaces the linear model, resource recycling activities become a priority in waste management policy. As new techniques and possibilities of waste collection systems emerge, opportunities are created for improving efficiency for collection companies and affording benefits for the environment. A model proposed for mobile WEEE collection in this study considers a multi-criteria approach in developing a cost efficient method for pick up on demand from residents or electrical and electronic equipment (EEE) stores. The algorithm used in this model optimises vehicle routes and helps in selecting a number of vehicles from a heterogeneous fleet, incorporating the WEEE loading problem. Using genetic algorithm and fuzzy logic, this model optimises costs and resources required to complete the WEEE collection assuring timely pick up of the waste equipment. The numerical model is verified in a case study in Opole, a city in the south of Poland. The results show that the proposed model can handle the multiple parameter optimisation problem including operational costs, efficient use of vehicles from a fleet, efficient waste loading in vehicles and residents' satisfaction with timely pick up of the waste equipment from a household. Such system can be successfully applied even for large cities. The algorithm provides an opportunity for writing software or mobile apps design to be used by WEEE collection companies.

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

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Safety and Management Framework to Enable Automated Mobility Districts in Urban Areas

Automated mobility technology is beginning to emerge as a viable means to create sustainable and effective public mobility systems within denser urban environments. Automated mobility districts (AMDs) describe major urban districts or activity centers in which deployments of multiple automated vehicle (AV) transit and ride-hailing fleets are supported to meet public mobility needs. The authors put forward a framework to enable AMDs and their governing and management jurisdictional authorities to manage safety of AV operations based on lessons learned from the last century of automated guideway transit and roadway intersection traffic control systems. The essential concept is that of operational management and safety-critical control of multiple AV fleets using a “system-of-systems” approach to system safety analysis. The safety analysis would focus on safe passage of the AV fleet vehicles through complex roadway intersections and junctions, especially in the presence of other non-automated modes such as pedestrians and manually operated vehicles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

System-level performance and degradation of 21 GWDC of utility-scale PV plants in the United States

We assess the performance of a fleet of 411 utility-scale (i.e., >5 MWAC and ground-mounted) photovoltaic (PV) projects totaling 21.1 GWDC (16.3 GWAC) of capacity, which achieved commercial operations in the United States from 2007 to 2016. This fleet of projects contributed more than 50% of all solar electricity generated in the United States in 2017. Using detailed information on individual project characteristics, in conjunction with modeled irradiance data, we assess the extent to which actual first-year performance has lived up to both modeled and stated expectations. We then analyze system-level performance degradation in subsequent years by employing a “fixed effects” regression model to statistically isolate the impact of age on system performance. We find that this fleet of utility-scale PV projects has generally lived up to ex ante expectations for first-year performance but that subsequent system-level degradation—found to be −1.3%/year (±0.2%)—has, on average, been worse than both ex ante expectations (commonly −0.5%/year) and results from past studies (ranging from −0.8%/year to −1.0%/year). We emphasize that −1.3%/year is a system-level estimate that captures more than just module degradation (e.g., including soiling, balance of plant degradation, and downtime for maintenance and/or other events). A side analysis of a variety of project characteristics suggests that system-level degradation rates tend to be of lower magnitude among newer projects and larger projects and at sites with lower long-term average temperatures.

14 SOLAR ENERGY↗
DOE OSTI2015

Modeling indications of technology in planetary transit light curves—Dark-side illumination

We analyze potential effects of an extraterrestrial civilization’s use of orbiting mirrors to illuminate the dark side of a synchronously rotating planet on planetary transit light curves. Previous efforts to detect civilizations based on side effects of planetary-scale engineering have focused on structures affecting the host star output (e.g., Dyson spheres). However, younger civilizations are likely to be less advanced in their engineering efforts, yet still capable of sending small spacecraft into orbit. Since M dwarfs are the most common type of star in the solar neighborhood, it seems plausible that many of the nearest habitable planets orbit dim, low-mass M stars, and will be in synchronous rotation. Logically, a civilization evolving on such a planet may be inspired to illuminate their planet’s dark side by placing a single large mirror at the L2 Lagrangian point, or launching a fleet of small thin mirrors into planetary orbit. We briefly examine the requirements and engineering challenges of such a collection of orbiting mirrors, then explore their impact on transit light curves. We incorporate stellar limb darkening and model a simplistic mirror fleet’s effects for transits of Earth-like (R = 0.5 to 2 R{sub Earth}) planets which would be synchronously rotating for orbits within the habitable zone of their host star. Although such an installation is undetectable in Kepler data, the James Webb Space Telescope will provide the sensitivity necessary to detect a fleet of mirrors orbiting Earth-like habitable planets around nearby stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Decarbonizing US passenger vehicle transport under electrification and automation uncertainty has a travel budget

Abstract The transportation sector is at the beginning of a transition represented by electrification, shared mobility, and automation, which could lead to either increases or decreases in total travel and energy use. Understanding the factors enabling deep decarbonization of the passenger vehicle sector is essential for planning the required infrastructure investments and technology adoption policies. We examine the requirements for meeting carbon reduction targets of 80% and higher for passenger vehicle transport in the United States (US) by midcentury under uncertainty. We model the changes needed in vehicle electrification, electricity carbon intensity, and travel demand. Since growth in fleet penetration of electric vehicles (EVs) is constrained by fleet stock turnover, we estimate the EV penetration rates needed to meet climate targets. We find for a base case level of passenger vehicle travel, midcentury deep decarbonization of US passenger transport is conditional on reducing the electricity generation carbon intensity to close to zero along with electrification of about 67% or 84% of vehicle travel to meet decarbonization targets of 80% or 90%, respectively. Higher electricity generation carbon intensity and degraded EV fuel economy due to automation would require higher levels of fleet electrification and/or further constrain the total vehicle travel allowable. Transportation deep decarbonization not only depends on electricity decarbonization, but also has a total travel budget, representing a maximum total vehicle travel threshold that still enables meeting a midcentury climate target. This makes encouraging ride sharing, reducing total vehicle travel, and increasing fuel economy in both human-driven and future automated vehicles increasingly important to deep decarbonization.

Alarfaj, Abdullah F. (ORCID:0000000157360662)↗
DOE OSTI2021

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗
DOE OSTI2024

Using Mobile Charging Drones to Mitigate Battery Disruptions of Electric Vehicles on Highways

Our research explores innovative solutions to address the challenge of battery disruptions in electric vehicles (EVs) on highways. We propose a centralized fleet ownership model where a company manages a fleet of Mobile Charging Drones (MCDs) guided by a k-VRP (Vehicle Routing Problem) framework. This model is designed to tackle a multi-objective optimization issue with three primary goals: reduction of the overall operating costs, decrease in the cumulative waiting time, and minimization of the combined operating costs and waiting times. This approach extends beyond the usual VRP constraints, encompassing specific limitations for both MCDs and disrupted EVs (DEVs). Additionally, our study delves into the concept of decentralized fleet ownership through the lens of crowdsourcing. Preliminary numerical analyses indicate that the capital cost of MCDs is a significant factor on the charging service, and the system performance is sensitive to DEV owner's value of time (VOT) when VOTs are relatively low.

battery disruption↗
DOE OSTI2019

The Social Cost of Individual Privacy in Aggregated Residential Demand Response

There is an increasing body of experimental and theoretical research related to demand response using smart, connected home equipment in smart grid. However, as technologies are deployed at scale, privacy has become a major concern. One possible approach to address this concern is to model an entire home as an aggregated unit of resource for engaging in demand response. Such an approach would allow residents to participate in demand response while abstracting the specifics of the appliance usage pattern from the utility provider. The benefits of privacy-preserving demand response notwithstanding, it implies a cost in terms of wasted capacity. This paper aims to explore the privacy/capacity tradeoff by simulating a fleet of homes and comparing the results with a fleet of individual appliances. The results show that a fleet of homes only bids about 70% of available capacity, and in the presence of an aggregator this number declines to 50%. Thus, privacy of individual homes comes at a cost of sacrificing part of otherwise available capacity.

Tsybina, Eve↗
DOE OSTI2021

ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services

Rapid and sustainable development of the electric vehicle (EV) industry places the requirement for the plan of EV deployment. For public EV, existing models mainly focus on the charging facility design and fail to capture the multi-modal scenarios. In this work, we develop an agent-based decision-support system for multi-modal electric transits to locate the optimal combinations of key parameters, including the fleet size, the transit schedule, the charging facility design, and the routing strategy. We demonstrate the utilities of our system by simulating public EV services deployed to serve travel needs related to a transportation hub in New York City. To support the decision of the fleet size, we summarize system-level performances including the total satisfied demand, passengers' waiting time, vehicle idling time. The spatial and temporal patterns are extracted to serve a deeper understanding of system dynamics and service quality. Finally, we investigate the interaction between the fleet size design and the routing strategy. The results suggest a necessity of integrating the operation strategies into the planning phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Charging-management And Infrastructure-planning (cmip) Model

CMIP model explores various charging infrastructure network designs to serve a free-floating car-sharing fleet and determine the charging downtime experienced by the fleet for each design. Development of the CMIP model had two major steps: (1) describing modeling assumptions and (2) developing an integer program (IP) that jointly optimizes decisions about locations to install DC fast chargers and EV-to-charger assignments. The CMIP model integrates an EV charging model, EV energy consumption model, and heterogeneous, real-world vehicle use data with an integer programming optimization model to identify optimal location of new charging stations and calculate vehicle downtime for charging. The CMIP model can be applied to understand: (a) the reduction of EV fleet downtime if an additional fast-charging station is added to the current infrastructure and (b) to what extent total vehicle downtime would be sensitive to additional charging infrastructure.

Roni, MohammadS↗
DOE OSTI2020

Athena - Shuttle Bus Optimization and Event Driven Simulator [SWR 20-105]

The primary purpose of the software is to optimize shuttle routes to and from terminals, given some airport campus location like the rental car center. This code was developed as part of the DOE funded Athena project. One aspect of the Athena project was to investigate if the optimization of shuttle routes that move passengers to and from the five DFW terminals and the rental car center could result in lower energy consumption from the shuttle fleet while still maintaining an acceptable level of service to passengers. Hence, the optimization done by the code determines a set of shuttle routes, the type of shuttle on each route, and the number of shuttles servicing each route. The optimization aims to make these choices in a way that the energy consumption per hour by the fleet is minimized. Additionally, the software can fast simulate airport shuttle operations with collected data. The simulation inputs are passenger arrival rates, shuttle routes and frequencies, and simulation configurations. The outputs include: time dependent shuttle energy level, time dependent charging station usage, time dependent number of passengers on each bus, time dependent number of passengers at each bus stop, history of number of passengers left at each bus stop, route history of fleet shuttle buses and on-demand buses, and time dependent bus distance traveled.

Kelly, Kenneth↗
DOE OSTI2022

HIVE™ [SWR-19-36]

The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.

Rames, Clement↗