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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Challenges and future perspectives on sodium and potassium ion batteries for grid-scale energy storage

The energy crisis and environmental pollution require the advancement of large-scale energy storage techniques. Among the various commercialized technologies, batteries have attracted enormous attention due to their relatively high energy density and long cycle life. Nevertheless, the limited supply and uneven distribution of lithium minerals, as well as their high cost, has greatly hindered the application of lithium-ion batteries in large-scale energy storage. Therefore, building next-generation alternative rechargeable batteries that feature low cost, long service life, and high safety is of the utmost importance. Over the past several years, we have witnessed many successful research outcomes for sodium and potassium ion batteries (SIBs and PIBs) in regard to electrode and electrolyte materials. In this Perspective, we summarize the current developments on SIBs/PIBs and their challenges when facing practical applications, including their cost, energy density, ion diffusivity in solids/electrolytes/interphases, cycle life, and safety concerns. Furthermore, we provide an overview of strategies to overcome the remaining challenges for achieving better electrochemical performance. Finally, we outline several possible directions for the future development of these two battery chemistries, with the hope of aiding the transition from the laboratory to next-generation practical applications for SIBs/PIBs batteries in grid-scale energy storage.

Energy Storage↗
DOE OSTI2019

Regional-scale Spatio-Temporal Analysis of Impacts of Weather on Traffic Speed in Chicago using Probe Data

Understanding a regional-scale impact of the weather on the transportation system and how that impact varies geographically, is important from a sustainability standpoint. In this work, we have performed a city-scale analysis on the impact of weather on the traffic speed for the City of Chicago. We have found that there is a significant variation in the average hourly speed due to different weather patterns which also varies geographically. We have also observed that the rainfall has an evident impact on the average hourly speed on the freeways that are influenced by urbanized residential and commercial areas, and non-urbanized areas. Also, low visibility has shown a significant reduction in the average hourly traffic speed during the congested hours on the freeways that are influenced by non urbanized areas. We anticipate the contributions of this work in estimating emission and fuel economy at a regional scale which are important sustainability measures for transportation.

Kurte, Kuldeep↗
DOE OSTI2020

Breaking Simple Scaling Relations through Metal–Oxide Interactions: Understanding Room-Temperature Activation of Methane on M/CeO 2 (M = Pt, Ni, or Co) Interfaces

The clean activation of methane at low temperatures remains an eminent challenge and a field of competitive research. In particular, on late transition metal surfaces such as Pt(111) or Ni(111), higher temperatures are necessary to activate the hydrocarbon molecule, but a massive deposition of carbon makes the metal surface useless for catalytic activity. However, on very low-loaded M/CeO 2 (M = Pt, Ni, or Co) surfaces, the dissociation of methane occurs at room temperature, which is unexpected considering simple linear scaling relationships. This intriguing phenomenon has been studied using a combination of experimental techniques (ambient-pressure X-ray photoelectron spectroscopy, time-resolved X-ray diffraction, and X-ray absorption spectroscopy) and density functional theory-based calculations. The experimental and theoretical studies show that the size and morphology of the supported nanoparticles together with strong metal–support interactions are behind the deviations from the scaling relations. These findings point toward a possible strategy for circumventing scaling relations, producing active and stable catalysts that can be employed for methane activation and conversion.

03 NATURAL GAS↗
DOE OSTI2021

Zonal‐Scale of the Madden‐Julian Oscillation and Its Propagation Speed on the Interannual Time‐Scale

Abstract While no significant long‐term trend in the propagation speed of the Madden‐Julian Oscillation (MJO) in boreal winter is found during the past decades, pronounced year‐to‐year variability of the MJO phase speed is illustrated by analyzing a century‐long record data set. During the winters when fast MJO propagation is observed, the MJO exhibits a much larger zonal‐scale than that during the winters with slow propagation. A broader extension in MJO circulation effectively induces stronger and broader lower‐tropospheric moistening (drying) to the east (west) of MJO through horizontal moisture advection, prompting a faster MJO phase speed. The larger MJO zonal‐scale during the fast MJO propagation winters is coincident with anomalously increased background sea surface temperatures and precipitable water over both the western Indian Ocean and central/eastern Pacific, reminiscent of an expansion of the Indo‐Pacific warm pool. A fundamental question remains open regarding the key processes that determine the zonal‐scale of MJO organization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Scale–Bridging in Three–Dimensional Fracture Networks: Characterizing the Effects of Variable Fracture Apertures on Network–Scale Flow Channelization

Here, we incorporate observations of real fracture aperture variability observed in laboratory experiments into an ensemble of three-dimensional discrete fracture network (DFN) simulations to characterize how variations of this micro-scale feature can influence flow and transport behavior at the network scale. A shear fracture is created within a Marcellus shale sample, and the fracture aperture is measured using a triaxial direct-shear device coupled with real-time X-ray imaging at in-situ stress conditions. We construct an ensemble of fracture networks based on natural fractures in Marcellus shale and project regions of the experimental aperture field onto each fracture in the networks. Our calculations demonstrate that the degree of flow channelization, a network-scale flow field structure, is dramatically increased by local changes in the aperture field that in turn affects flow and transport properties.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Correlation between micrometer-scale ripple alignment and atomic-scale crystallographic orientation of monolayer graphene

Deformation normal to the surface is intrinsic in two-dimensional materials due to phononic thermal fluctuations at finite temperatures. Graphene's negative thermal expansion coefficient is generally explained by such an intrinsic property. Recently, friction measurements on graphene exfoliated on a silicon oxide surface revealed an anomalous anisotropy whose origin was believed to be the formation of ripple domains. Here, we uncover the atomistic origin of the observed friction domains using a cantilever torsion microscopy in conjunction with angle-resolved photoemission spectroscopy. We experimentally demonstrate that ripples on graphene are formed along the zigzag direction of the hexagonal lattice. The formation of zigzag directional ripple is consistent with our theoretical model that takes account of the atomic-scale bending stiffness of carbon-carbon bonds and the interaction of graphene with the substrate. Lastly, the correlation between micrometer-scale ripple alignment and atomic-scale arrangement of exfoliated monolayer graphene is first discovered and suggests a practical tool for measuring lattice orientation of graphene.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Meso-scale magnetic signatures for nuclear reactor steel irradiation embrittlement monitoring

Verifying the structural integrity of passive components in light water and advanced reactors will be necessary to ensure safe, long-term operations of the existing U.S. nuclear fleet. This objective can be achieved through nondestructive condition monitoring techniques, which can be integrated with plant operations to quantify the “state of health” of structural materials in real-time. While nondestructive methods for monitoring many classes of degradation (such as fatigue or stress corrosion cracking) are relatively advanced, this is not the case for degradation caused by irradiation. The development of nondestructive evaluation technologies for these types of degradation will require advanced materials characterization techniques and tools that enable comprehensive understanding of nuclear reactor material microstructural and behavioral changes under extreme operating environments. Irradiation-induced degradation of reactor steels causes changes in their microstructure that impacts their micro-magnetic properties. In this paper, we describe preliminary results of integrating advanced material characterization techniques with meso-scale computational models. In the future, this will help to provide an interpretive understanding of the state of degradation in structural materials. Microstructural data are presented from monocrystalline Fe and are correlated with variable-field magnetic force microscopy and micro-magnetic measurements. Ongoing research is focused on extending the measurements and models on thin films to gain insights into the structural state of irradiated materials and the resulting impact on magnetic properties. Preliminary conclusions from these correlations are presented, and next steps described.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Atomic-scale thermocapillary flow in focused ion beam milling

Focused ion beams provide a means of nanometer-scale manufacturing and material processing, which is used for applications such as forming nanometer-scale pores in thin films for DNA sequencing. We investigate such a configuration with Ga{sup +} bombardment of a Si thin-film target using molecular dynamics simulation. For a range of ion intensities in a realistic configuration, a recirculating melt region develops, which is seen to flow with a symmetrical pattern, counter to how it would flow were it driven by the ion momentum flux. Such flow is potentially important for the shape and composition of the formed structures. Relevant stress scales and estimated physical properties of silicon under these extreme conditions support the importance thermocapillary effects. A flow model with Marangoni forcing, based upon the temperature gradient and geometry from the atomistic simulation, indeed reproduces the flow and thus could be used to anticipate such flows and their influence in applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Theory of Trotter Error with Commutator Scaling

The Lie-Trotter formula, together with its higher-order generalizations, provides a simple approach to decomposing the exponential of a sum of operators. Despite significant effort, the error scaling of such product formulas remains poorly understood. We develop a theory of Trotter error that overcomes the limitations of truncating the Baker-Campbell-Hausdorff expansion. Our analysis directly exploits the commutativity of operator summands, producing tighter error bounds for both real- and imaginary-time evolutions. Whereas previous work achieves similar goals for systems with geometric locality or Lie-algebraic structure, our approach holds in general. We give a host of improved algorithms for digital quantum simulation and quantum Monte Carlo methods, nearly matching or even outperforming the best previous results. Our applications include: (i) a simulation of second-quantized plane-wave electronic structure, nearly matching the interaction-picture algorithm of Low and Wiebe; (ii) a simulation of $k$-local Hamiltonians almost with induced one-norm scaling, faster than the qubitization algorithm of Low and Chuang; (iii) a simulation of rapidly decaying power-law interactions, outperforming the Lieb-Robinson-based approach of Tran et al.; (iv) a hybrid simulation of clustered Hamiltonians, dramatically improving the result of Peng, Harrow, Ozols, and Wu; and (v) quantum Monte Carlo simulations of the transverse field Ising model and quantum ferromagnets, tightening previous analyses of Bravyi and Gosset. We obtain further speedups using the fact that product formulas can preserve the locality of the simulated system. Specifically, we show that local observables can be simulated with complexity independent of the system size for power-law interacting systems, which implies a Lieb-Robinson bound nearly matching a recent result of Tran et al. Our analysis reproduces known tight bounds for first- and second-order formulas. We further investigate the tightness of our bounds for higher-order formulas. For quantum simulation of a one-dimensional Heisenberg model with an even-odd ordering of terms, our result overestimates the complexity by only a factor of $5$. Our bound is also close to tight for power-law interactions and other orderings of terms. This suggests that our theory can accurately characterize Trotter error in terms of both the asymptotic scaling and the constant prefactor.

quantum computing, numerical analysis↗
DOE OSTI2020

Scaled-up Neuromorphic Array Communications Controller (SNACC) for Large-scale Neural Networks

Neuromorphic computing is one promising post-Moore’s law era technology, which takes inspiration from biological brains to perform computing tasks. The human brain contains billions of neurons with trillions of synapses and as neuromorphic hardware systems scale to larger and larger sizes, the communication system used to transfer information between neuromorphic elements and traditional computers must scale to keep up. In prior work, we describe the use of a separate neuromorphic array communications controller to support low-latency, high-throughput communication between our neuromorphic systems and a traditional computer. In this work, the neuromorphic array communications controller is used to support the scaling of a neuromorphic development system which uses multiple neuromorphic processors arranged in a two-dimensional array. The neuromorphic array communications controller, along with scalable local connections, is used to create a scalable neuromorphic platform to enable the development and testing of large neuromorphic network arrays.

Young, Aaron↗
DOE OSTI2017

Scaling deep learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors --- including NVIDIA, Intel, AMD, and IBM --- have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling --- sometimes encouraged by restricted GPU memory --- NVLink is less important.

Gawande, Nitin A.↗
DOE OSTI2017

Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data

New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

The Anatomy of Mr. Scan: A Dissection of Performance of an Extreme Scale GPU-Based Clustering Algorithm, In: 2014 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems

The emergence of leadership class systems with GPU-equipped nodes has the potential to vastly increase the performance of existing distributed applications. However, the inclusion of GPU computation into existing extreme scale distributed applications can reveal scalability issues that were absent in the CPU version. The issues exposed in scaling by a GPU can become limiting factors to overall application performance. We developed an extreme scale GPU-based application to perform data clustering on multi-billion point datasets. In this application, called Mr. Scan, we ran into several of these performance limiting issues. Through the use of complete end-to-end benchmarking of Mr. Scan (measuring time from reading and distribution to final output), we were able to identify three major sources of real world performance issues: data distribution, GPU load balancing, and system specific issues such as start-up time. These issues comprised a vast majority of the run time of Mr. Scan. Data distribution alone accounted for 68% of the total run time of Mr. Scan when processing 6.5 billion points on Cray Titan at 8192 nodes. With improvements in these areas, we have been able able to cut total run time of Mr. Scan from 17.5 minutes to 8.3 minutes when clustering 6.5 billion points.

Computer Science↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Basin‐Scale Testing of Alternative Data‐Driven Approaches

Abstract Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology

Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.

59 BASIC BIOLOGICAL SCIENCES↗