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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Policy and Regulatory Environment for Utility-Scale Energy Storage: Bangladesh

Bangladesh has experienced significant economic growth and poverty reduction over the past several decades. Recognizing the central role electricity plays in economic development, the Government of Bangladesh (GOB) has established policies to accelerate the growth of the electric power sector. The Bangladesh power grid is transforming into one marked by declining reliance on domestic natural gas reserves and oil-based rental power plants, increasing renewable energy contribution, and shifting demand patterns. The GOB is now reconsidering its prior plans to increase the share of coal capacity in the generation mix to meet demand, shifting its focus instead to electricity imports from neighboring countries, nuclear generation, liquified natural gas imports, and domestic renewable resources such as wind and solar. However, investments in the transmission and distribution system, as well as ancillary services, have not kept pace with investments in generation resources over the past decade. Thus, Bangladesh electricity consumers still experience outages and poor power quality despite adequate installed capacity. On the demand side, population growth and industrialization have fueled steady growth in electricity consumption as efforts to expand access to electricity enabled near-universal electricity access by mid-2020. The combined changes in the mix of generation resources and patterns of electricity demand present new challenges and opportunities in operating and maintaining a reliable power system. Energy storage has the potential to help meet these challenges and accelerate Bangladesh’s energy transition. Declining costs for some energy storage technologies make them increasingly cost-effective solutions to provide a wide range of grid services. Previous analyses of energy storage in the region have identified several potential applications for storage at the bulk system level, including energy arbitrage, ancillary services, and transmission network support. The potential for storage to meet these needs depends on many factors, including physical characteristics of the power system and the policy and regulatory environments in which these energy storage assets would operate. This report applies an Energy Storage Readiness Assessment the National Renewable Energy Laboratory developed for policy makers and regulators to identify priority areas of focus as they continue to develop the appropriate suite of policies, programs, and regulations to enable storage deployment. This assessment uses a simple evaluation scheme to identify the barriers and opportunities for utility-scale energy storage within Bangladesh’s policy and regulatory environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Influence of Geography on Prostate Cancer Treatment

Several definitive treatment options are available for prostate cancer, but geographic access to those options is not uniform. We created maps illustrating provider practice patterns relation to patients and assessed the influence of distance to treatment receipt.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Improving the performance of first- and last-mile mobility services through transit coordination, real-time demand prediction, advanced reservations, and trip prioritization

Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Native Top-Down Mass Spectrometry Characterization of Model Integral Membrane Protein Bacteriorhodopsin

Bacteriorhodopsin (bR) from Halobacterium salinarum has been a model system for structural biology and is a structural template for the characterization of membrane G-protein couple receptors (GPCRs) in particular. Here, in this study, wild-type bacteriorhodopsin and two single-residue mutants were characterized by native top-down mass spectrometry (nTD-MS) with Orbitrap-based high-energy collision dissociation (HCD) and electron capture dissociation (ECD). After in-source dissociation ejected the membrane protein from detergent micelles, high-resolution native MS measurement allowed for identification of multiple proteoforms as well as lipid-bound forms. Further top-down MS measurements by HCD produced a large number of product ions for in-depth sequencing and unambiguous localization of post-translational modifications. For the first time, native TD-MS with ECD was used to characterize an integral membrane protein. ECD yielded fragments originating from all helices and loop regions, even accessing a sequence stretch that HCD could not. Combining HCD and ECD fragmentation patterns significantly enhanced the sequence coverage of bR. We propose bR to be a model analyte for testing nTD-MS performance for membrane proteins.

crystal cleavage↗
DOE OSTI2022

Micromobility Integrated Transit and Infrastructure for Efficiency (MITIE)

Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: (1) What are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? (2) Which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? (3) To what degree can micromobility supplement/complement transit system operations? (4) What are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? (5) What are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: (1) Energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel. (2) Multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations. (3) Mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis. (4) Energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams. (5) Micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2023

Micromobility Integrated Transit and Infrastructure for Efficiency (MITIE)

Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: 1) what are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? 2) which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? 3) to what degree can micromobility supplement/complement transit system operations? 4) what are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? 5) what are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: 1) energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel; 2) multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations; 3) mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis; 4) energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams; 5) micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

58 GEOSCIENCES↗
DOE OSTI2020

Phase I Closeout Report: Invoking Artificial Neural Networks to Measure Insider Threat Mitigation

Researchers from Sandia National Laboratories (Sandia) and the University of Texas at Austin (UT) conducted this study to explore the effectiveness of commercial artificial neural network (ANN) software to improve insider threat detection and mitigation (ITDM). This study hypothesized that ANNs could be "trainee to learn patterns of organizational behaviors, detect off-normal (or anomalous) deviations from these patterns, and alert when certain types, frequencies, or quantities of deviations emerge. The ReconaSense ANN system was installed at UT's Nuclear Engineering Teaching Laboratory (NETL) and collected 13,653 access control data points and 694 intrusion sensor data points over a three-month period. Preliminary analysis of this baseline data demonstrated regularized patterns of life in the facility, and that off-normal behaviors are detectable under certain situations -- even for a facility with anticipated highly non-routine, operational behaviors. Completion of this pilot study demonstrated how the ReconaSense ANN could be used to identify expected operational patterns and detect unexpected anomalous behaviors in support of a data-analytic approach to ITDM. While additional studies are needed to fully understand and characterize this system, the results of this initial study are overall very promising for demonstrating a new framework for ITDM utilizing ANNs and data analysis techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Additionally, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

42 ENGINEERING↗
DOE OSTI2023

Spontaneous shock waves in pulse-stimulated flocks of Quincke rollers

Active matter demonstrates complex spatiotemporal self-organization not accessible at equilibrium and the emergence of collective behavior. Fluids comprised of microscopic Quincke rollers represent a popular realization of synthetic active matter. Temporal activity modulations, realized by modulated external electric fields, represent an effective tool to expand the variety of accessible dynamic states in active ensembles. Here, we report on the emergence of shockwave patterns composed of coherently moving particles energized by a pulsed electric field. The shockwaves emerge spontaneously and move faster than the average particle speed. Combining experiments, theory, and simulations, we demonstrate that the shockwaves originate from intermittent spontaneous vortex cores due to a vortex meandering instability. They occur when the rollers’ translational and rotational decoherence times, regulated by the electric pulse durations, become comparable. The phenomenon does not rely on the presence of confinement, and multiple shock waves continuously arise and vanish in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Thermodynamic stability of a spin microemulsion in Rashba spin-orbit-coupled bosons

Recent finite-temperature numerical simulations have unveiled a quantum “spin” microemulsion analog, found by raising the temperature of a stripe supersolid phase in a Rashba spin-orbit coupled Bose gas. This microemulsion state is a highly correlated, isotropic normal fluid where atoms self-arrange based on their internal pseudospin into patterns that resemble bicontinuous microemulsions. This finding leaves several open questions regarding the broader accessibility of this phase in experiments. Here, we use equilibrium finite-temperature numerical simulations based on a coherent-state path integral representation to perform a computational investigation into the thermodynamic stability of the spin microemulsion state. Numerical simulations emphasize the requirement of a nearly, but not perfectly, isotropic spin-orbit coupling in order to achieve the microemulsion phase in cold-atom experiments. Moreover, the microemulsion state exists independent of miscibility of the pseudospin components and for a wide range of pseudospin population imbalance, suggesting a high degree of flexibility in choosing the atom and hyperfine states in an experimental realization. Lastly, we demonstrate this feasibility by mimicking a Rashba spin-orbit-coupled 87 Rb experiment in an isotropic harmonic trap, where we confirm the microemulsion's existence via its density profile and equilibrium quasimomentum distribution.

Complex Langevin dynamics↗
DOE OSTI2022

Depth-resolved Laue microdiffraction with coded apertures

A rapid data acquisition and reconstruction method is introduced to image the crystalline structure of materials and the associated strain and orientations at micrometre resolution using Laue diffraction. The method relies on scanning a coded aperture across the diffracted X-ray beam from broadband illumination and a reconstruction algorithm to resolve Laue microdiffraction patterns as a function of depth along the incident illumination path. It provides rapid access to full diffraction information for sub-micrometre volume elements in bulk materials. Both the theory and the experimental validation of this imaging approach are presented in this report.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-Kernel Adaptive Support Vector Machine for Scalable Predictive Maintenance

Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. In addition, it is highly unlikely that all potential data patterns are captured in a single data source. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train the ML model at each source and develop a distributed knowledge discovery and aggregation approach to build global knowledge. In this paper, we develop and demonstrate a distributed ML model, federated transfer learning (FTL), using a multi-kernel-based adaptive support vector machine (MK-A-SVM). For federated learning (FL), the multi-kernel (MK) approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning (TL) the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant (NPP) vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.

42 ENGINEERING↗
DOE OSTI2020

Impact of particle arrays on phase separation composition patterns

We examine the symmetry-breaking effect of fixed constellations of particles on the surface-directed spinodal decomposition of binary blends in the presence of particles whose surfaces have a preferential affinity for one of the components. Our phase-field simulations indicate that the phase separation morphology in the presence of particle arrays can be tuned to have a continuous, droplet, lamellar, or hybrid morphology depending on the interparticle spacing, blend composition, and time. In particular, when the interparticle spacing is large compared to the spinodal wavelength, a transient target pattern composed of alternate rings of preferred and non-preferred phases emerges at early times, tending to adopt the symmetry of the particle configuration. We reveal that such target patterns stabilize for certain characteristic length, time, and composition scales characteristic of the pure phase-separating mixture. To illustrate the general range of phenomena exhibited by mixture-particle systems, we simulate the effects of single-particle, multi-particle, and cluster–particle systems having multiple geometrical configurations of the particle characteristic of pattern substrates on phase separation. Our simulations show that tailoring the particle configuration, or substrate pattern configuration, a relative fluid-particle composition should allow the desirable control of the phase separation morphology as in block copolymer materials, but where the scales accessible to this approach of organizing phase-separated fluids usually are significantly larger. Limited experiments confirm the trends observed in our simulations, which should provide some guidance in engineering patterned blend and other mixtures of technological interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Visualization of large-scale charged domain Walls in hexagonal manganites

A ferroelectric charged domain wall (CDW) carries bound charges, originating from the variation of the normal components of polarization across the domain boundary, leading to a possible two-dimensional conductive interface in insulating materials. The ferroelectric CDW can be precisely created, erased, and manipulated, therefore offering an intriguing pathway toward the design of nano-devices. However, due to rather large energy costs, the size of the CDW is usually on the nanoscale. Here, taking flux-grown ferroelectric hexagonal manganites (h-RMnO 3 ) as an example, we creatively adopted an accessible inclined polishing process to reveal the domain evolution of stripe, loop, and vortex domain patterns by depth profiling. Interestingly, we observed an unexpected large-scale straight CDW in as-grown LuMnO 3 with length up to a millimeter size, which may result from the “polar catastrophe” during the flux growth. The large-scale CDW has a residual influence on the formation of the loop domain when the crystal anneals below the ferroelectric transition temperature, but completely disappears as the topological vortices emerge. The observed large-scale CDWs make h-RMnO 3 a potential candidate for advanced electronic devices, leading to a panoply of desired properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Preserving isohydricity: vertical environmental variability explains Amazon forest water-use strategies

Abstract Increases in hydrological extremes, including drought, are expected for Amazon forests. A fundamental challenge for predicting forest responses lies in identifying ecological strategies which underlie such responses. Characterization of species-specific hydraulic strategies for regulating water-use, thought to be arrayed along an ‘isohydric–anisohydric’ spectrum, is a widely used approach. However, recent studies have questioned the usefulness of this classification scheme, because its metrics are strongly influenced by environments, and hence can lead to divergent classifications even within the same species. Here, we propose an alternative approach positing that individual hydraulic regulation strategies emerge from the interaction of environments with traits. Specifically, we hypothesize that the vertical forest profile represents a key gradient in drought-related environments (atmospheric vapor pressure deficit, soil water availability) that drives divergent tree water-use strategies for coordinated regulation of stomatal conductance (gs) and leaf water potentials (ΨL) with tree rooting depth, a proxy for water availability. Testing this hypothesis in a seasonal eastern Amazon forest in Brazil, we found that hydraulic strategies indeed depend on height-associated environments. Upper canopy trees, experiencing high vapor pressure deficit (VPD), but stable soil water access through deep rooting, exhibited isohydric strategies, defined by little seasonal change in the diurnal pattern of gs and steady seasonal minimum ΨL. In contrast, understory trees, exposed to less variable VPD but highly variable soil water availability, exhibited anisohydric strategies, with fluctuations in diurnal gs that increased in the dry season along with increasing variation in ΨL. Our finding that canopy height structures the coordination between drought-related environmental stressors and hydraulic traits provides a basis for preserving the applicability of the isohydric-to-anisohydric spectrum, which we show here may consistently emerge from environmental context. Our work highlights the importance of understanding how environmental heterogeneity structures forest responses to climate change, providing a mechanistic basis for improving models of tropical ecosystems.

Forestry↗
DOE OSTI2022

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗