At a Glance: Electric Vehicles
This at a glance brochure provides an overview of electric vehicles, including battery electric vehicles and plug-in hybrid electric vehicles.
47 OTHER INSTRUMENTATION↗
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Results for “Plugs”
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 A Glance: Electric-Drive Vehicles
Electric-drive vehicles use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. With the range of styles and options available, there is likely one to meet your needs. Electric vehicles (EVs) include all-electric vehicles and plug-in hybrid electric vehicles (PHEVs).
all-electric vehicles↗
Electric-Drive Vehicles
Electric-drive vehicles use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. These vehicles can be divided into three categories: All-electric vehicles and Plug-in hybrid electric vehicles (PHEVs). Together, PHEVs and EVs can also be referred to as electric vehicles (EVs).
hybrid, HEV, plug-in, PHEV, electric vehicle, EV, ↗
Supporting Electric Vehicle Supply Equipment Deployment
The U.S. Department of Energy (DOE) Federal Energy Management Program (FEMP) helps federal agencies electrify their fleets and support the deployment of charging infrastructure. To assist agencies with the transition to zero-emission vehicles (ZEVs), including battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), FEMP offers technical guidance on electric vehicle supply equipment (EVSE) installations and site-specific planning through on-site and virtual EVSE Tiger Teams.
ADVANCED PROPULSION SYSTEMS↗
Higher Education Buildings Recognized for Integrated Controls for HVAC and Lighting Systems - Case Study: California State University Dominguez Hills and University of Minnesota
This case study describes connected lighting system projects on two college campuses; Cal State Dominguez Hills and the University of Minnesota both implemented lighting and controls upgrades at mixed-use buildings, including advanced sensors, HVAC integration, and plug load control. Both projects were recognized by DOE's Integrated Lighting Campaign.
integrated lighting, higher education, college, un↗
Analysis of ZTE MRI application to sandstone and carbonate
Micro-tomography (μCT) and nuclear magnetic resonance (NMR) have been used to characterize porous media for decades. Here, Magnetic Resonance Imaging (MRI) enables direct visualization of pore architecture and many pulse sequences exist. In this work, we tested the MRI pulse sequence Zero Echo Time (ZTE) to study sandstone and carbonate for its ability to address short relaxation times. We aimed at resolving two fluid conduit scales, i.e. pores and fractures. In this research, we study tighter porous systems than those previously reported using ZTE. Additionally, Pore Cluster Analysis (PCA), combined with ZTE, can be used to analyze pore-fracture connectivity of relatively large core plugs. We show that ZTE can resolve two-scale pore systems simultaneously, i.e. fractures and pores. By combining Time-Domain NMR pore-size analysis and PCA, we show that careful selection of resolution is necessary to understand transport in porous media.
02 PETROLEUM↗
Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future
Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.
59 BASIC BIOLOGICAL SCIENCES↗
OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems
This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions.
Zheng, Weijian↗
Fabrication and Radio Frequency Properties of 3-GHz SRF Cavities Coated with MgB 2
Magnesium diboride (MgB 2 ) is considered a potential material for superconducting radio frequency cavities. MgB 2 coated Cu cavity will allow for a higher operational temperature than a bulk Nb cavity because of the higher transition temperature of MgB 2 and the high thermal conductivity of Cu. Using the hybrid physical chemical vapor deposition technique, MgB 2 coatings were successfully achieved on the inner wall of 3-GHz Cu cavities. The surface and superconducting properties of the coatings were characterized using small samples on Cu plugs mounted at different locations of mock cavities. RF measurement of a MgB 2 coated single-cell 3-GHz test cavity was carried out and it showed superconducting transition at 36 K. Here, the quality factor of this test cavity was lower than expected due to poor connectivity and inclusion of Mg-Cu alloy in the MgB 2 coating.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Reaction Pathways and Energy Consumption in NH 3 Decomposition for H 2 Production by Low Temperature, Atmospheric Pressure Plasma
Pathways for NH 3 decomposition to N 2 and N 2 H 4 by atmospheric pressure nonthermal plasma are analyzed using a combination of molecular beam mass spectrometry measurements and zero-dimensional kinetic modeling. Experimental measurements show that NH 3 conversion and selectivity towards N 2 formation scale monotonically with the specific energy input into the plasma with ~ 100% selectivity to N 2 formation achieved at specific energy inputs above 0.12 J cm −3 (3.1 eV (molecule NH 3 ) −1 ). The kinetic model recovers these trends, although it underpredicts N 2 selectivity at low specific energy input. These discrepancies can be explained by the underestimation of reaction rate coefficients for reactions that consume N 2 H x species in collisions with H radicals and/or radial nonuniformities in power deposition, gas temperature, and species concentrations that are not represented by the plug flow approximation used in the model. The kinetic model shows that N 2 formation proceeds through N 2 H x decomposition pathways rather than NH x decomposition pathways in low temperature, atmospheric pressure plasma. Higher selectivity toward N 2 production can be achieved by operating at higher NH 3 conversion and with a higher gas temperature. Furthermore, the high energy cost of NH 3 decomposition by atmospheric pressure nonthermal plasma found in this work (25–50 eV (molecule NH 3 converted) −1 ; 17–33 eV (molecule H 2 formed) −1 ) is a result of the energy requirement for electron-impact dissociation of NH 3 and the significant re-formation of NH 3 by three-body recombination reactions between NH 2 and H.
Nonthermal plasma↗
The effective number of parameters in kernel density estimation
We devise a new formula for measuring the effective degrees of freedom (EDoF) in kernel density estimation (KDE). Starting from the orthogonal polynomial sequence (OPS) expansion for the ratio of the empirical to the oracle density, we show how convolution with the kernel leads to a new OPS with respect to which one may express the resulting KDE. The expansion coefficients of the two OPS systems can then be related via a kernel sensitivity matrix, which leads to a natural oracle definition of EDoF through the trace operator. Asymptotic properties of the (empirical) plug-in EDoF are worked out through influence functions, and connections with other empirical EDoFs are established. Minimization of Kullback-Leibler divergence is investigated as an alternative to integrated squared error based bandwidth selection rules, yielding a new normal scale rule. The methodology, which arises from a proper oracle formulation and is not restricted to convolution kernels, suggests the possibility of a new bandwidth selection rule based on an information criterion such as AIC.
bandwidth selection↗
Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with U.S. medium office buildings
Quantifying the energy savings of various energy efficiency measures (EEMs) for an energy retrofit project often necessitates an energy audit and detailed whole building energy modeling to evaluate the EEMs; however, this is often cost-prohibitive for small and medium buildings. In order to provide a defined guideline for projects with assumed common baseline characteristics, this paper applies a sensitivity analysis method to evaluate the impact of individual EEMs and groups these into packages to produce deep energy savings for a sample prototype medium office building across 15 climate zones in the United States. We start with one baseline model for each climate zone and nine candidate EEMs with a range of efficiency levels for each EEM. Three energy performance indicators (EPIs) are defined, which are annual electricity use intensity, annual natural gas use intensity, and annual energy cost. Then, a Standard Regression Coefficient (SRC) sensitivity analysis method is applied to determine the sensitivity of each EEM with respect to the three EPIs, and the relative sensitivity of all EEMs are calculated to evaluate their energy impacts. For the selected range of efficiency levels, the results indicate that the EEMs with higher energy impacts (i.e., higher sensitivity) in most climate zones are high-performance windows, reduced interior lighting power, and reduced interior plug and process loads. However, the sensitivity of the EEMs also vary by climate zone and EPI; for example, improved opaque envelope insulation and efficiency of cooling and heating systems are found to have a high energy impact in cold and hot climates.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed
Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.
15 GEOTHERMAL ENERGY↗
A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty
Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. Finally, the mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.
97 MATHEMATICS AND COMPUTING↗
A spatial-statistical investigation of surface expressions associated with cyclic steaming in the Midway-Sunset Oil Field, California
In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to enhance heavy oil recovery through imbibition, wettability alteration, and viscosity reduction, among other mechanisms. The injected steam, however, does not always remain in the reservoir or return through the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called “surface expressions,” pose safety and environmental hazards. Even though these surface expressions are a widespread problem in Central California, they are not well documented and understood. Possible causes of the surface expressions include: high injection pressure, structurally controlled flow patterns, leakage of steam through old improperly abandoned wells, high injection volumes, or flow along naturally occurring faults, among other possible factors. This work examines attributes of the zones with surface expressions in order to determine factors that may contribute to their occurrence. Spatial statistical analysis using logistic regression, random forests, and classification trees is used to explore the relationship between the surface expressions and geological and production-related attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and geologic seal thickness. The results guide follow-up studies to further investigate the role of well abandonment and seal thickness in the occurrence of surface expressions.
02 PETROLEUM↗
Olefin methylation over iron zeolites and the methanol to hydrocarbons reaction
The effect of olefin addition to a stream of dimethyl ether on the methanol homologation reaction is investigated using iron-substituted zeolites Fe-beta and Fe-ZSM-5. The reaction was investigated using plug-flow microreactors in the temperature range of 240–400 °C, at a total pressure of 0.239 MPa and a WHSV of 6.12 (g DME/ gcat -hr). For Fe-beta (Si/Fe= 9.2) catalysts, isobutene co-feeding almost doubles dimethyl ether (DME) consumption rate and shifts selectivity towards larger olefins with carbon numbers from 5 to 7. Addition of isobutene above 6.3%, however, resulted in a reduction of DME consumption rates, an effect assigned to the replacement of surface methoxy groups for adsorbed olefins in the zeolite pores. Below a temperature of 340 °C hydride-transfer rates are negligible; reaction rates are stable for over 5.5 h and the products consist almost exclusively of olefins and a small amount of methane. Above 360 °C the onset of catalytic hydride transfer processes is observed leading to fast catalyst deactivation rates and an increase in the concentration of aromatic species. Iron ZSM-5 (Si/Fe = 21.4) catalysts under similar reaction conditions consumes methanol faster than Fe-beta at approximately three times the TOF (on a per iron basis). The Fe-ZSM-5 catalyst was selective to a distribution of products (C5 to C8) as compared to Fe-beta which was selective to primarily C5 and C7. Co-feeding larger olefins (2-methyl-2-butene, 2,3-dimethyl-2-butene, 2,3,3-trimethyl-1-butene, and 2,4,4-trimethyl-2-pentene) at a 3.9% olefin concentration over Fe-beta changed selectivity towards cracking products (C4 compounds such as isobutene). As the size of the olefin increases, a reduction of DME consumption rate is also observed. Here these results show that co-feeding olefins with DME over Fe-zeolites is a promising route to increase methylation rates at relatively low temperatures producing larger branched olefins and that the product distribution is highly dependent on the zeolite pore size and structure of the olefin.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Thermochemical reduction modeling in a high-temperature moving-bed reactor for energy storage: 1D model
The design of robust and efficient high-temperature thermochemical reactors and determination of operating conditions are critical steps toward enabling high-efficiency long-duration solar energy storage. This work presents a computational model for the thermal reduction of a metal oxide material (Mg-Mn-O) up to 1450 °C and the coupled complex transport phenomena in a novel tubular thermal reactor design that features the capability for a high extent-of-reduction (high energy storage density) and inherent heat recuperation. A one-dimensional model coupling counter-current gas–solid flow, two-phase heat transfer, thermochemical redox reactions, and species transport in a moving-bed reactor is developed. Simplified versions of the model are validated with published results in the literature for packed beds with both inert and reactive particles; the fully coupled model is also validated with experimental measurements of a moving-bed reactor in terms of local temperatures and oxygen release at the exit. Detailed comparisons on the effects of different boundary conditions in the reaction zone (prescribed wall temperature vs. heat flux conditions) and formulations based on a simple uniform flow assumption vs. plug flow using Ergun equation for gas flow are investigated. The results are compared with experimental measurements, and for all cases, the energy flow components in the reactor system and the thermal to chemical conversion efficiency and overall system efficiency are computed. Finally, the predicted high thermal-to-chemical efficiency ~95% and system efficiency ~30% agree with experimental measurements.
25 ENERGY STORAGE↗
Characterization and microbial mitigation of fugitive methane emissions from oil and gas wells: Example from Indiana, USA
Hydrocarbon gas emissions from active, inactive, and improperly sealed or abandoned oil/gas wells significantly contribute to anthropogenically emitted greenhouse gases, predominantly in the form of methane (CH 4 ). We explored the extent of hydrocarbon gas emissions from 20 active, inactive, plugged and abandoned oil/gas wells in Indiana (USA), where it is estimated that there are more than 80,000 well sites throughout the state. After this initial survey, using a static flux tent, we quantified fugitive CH4 emissions from an active gas well to approximately 2 L h -1 . To evaluate the potential for microbial mitigation of hydrocarbon emissions to the atmosphere, we conducted laboratory microcosm experiments to quantify the CH 4 oxidizing potential of soils collected from sites with varying distances to the leaking gas well. Soils in close proximity to the well (0.5 m) efficiently consumed nearly all (97 %) of the added CH 4 , while only 14 % of added CH 4 was consumed by soils that were more distant from the well (20 m). These results suggest that fugitive CH 4 emissions enrich methanotrophic bacteria in soils immediately adjacent to the well. Consistent with this view, we found that prolonged exposure of soils to elevated concentrations of CH 4 enhanced the methanotrophic activity. Together, these findings prompted us to design a “methanotrophic soil mound” to assess the feasibility of mitigating point sources of CH 4 by harnessing the natural methanotrophic capacity of soil microbial communities. We found that a methanotrophic soil mound from a landfill could sustainably mitigate the CH 4 emission from the artificial source, providing a promising low-cost solution to ameliorate fugitive CH 4 emissions from abandoned oil and gas wells to the atmosphere. Finally, the effectiveness of microbe-based remediation is limited in cold climates and arid environments.
54 ENVIRONMENTAL SCIENCES↗