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At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Thermally unstable roosts influence winter torpor patterns in a threatened bat species

Abstract Many hibernating bats in thermally stable, subterranean roosts have experienced precipitous declines from white-nose syndrome (WNS). However, some WNS-affected species also use thermally unstable roosts during winter that may impact their torpor patterns and WNS susceptibility. From November to March 2017–19, we used temperature-sensitive transmitters to document winter torpor patterns of tricolored bats (Perimyotis subflavus) using thermally unstable roosts in the upper Coastal Plain of South Carolina. Daily mean roost temperature was 12.9 ± 4.9°C SD in bridges and 11.0 ± 4.6°C in accessible cavities with daily fluctuations of 4.8 ± 2°C in bridges and 4.0 ± 1.9°C in accessible cavities and maximum fluctuations of 13.8 and 10.5°C, respectively. Mean torpor bout duration was 2.7 ± 2.8 days and was negatively related to ambient temperature and positively related to precipitation. Bats maintained non-random arousal patterns focused near dusk and were active on 33.6% of tracked days. Fifty-one percent of arousals contained passive rewarming. Normothermic bout duration, general activity and activity away from the roost were positively related to ambient temperature, and activity away from the roost was negatively related to barometric pressure. Our results suggest ambient weather conditions influence winter torpor patterns of tricolored bats using thermally unstable roosts. Short torpor bout durations and potential nighttime foraging during winter by tricolored bats in thermally unstable roosts contrasts with behaviors of tricolored bats in thermally stable roosts. Therefore, tricolored bat using thermally unstable roosts may be less susceptible to WNS. More broadly, these results highlight the importance of understanding the effect of roost thermal stability on winter torpor patterns and the physiological flexibility of broadly distributed hibernating species.

Biodiversity & Conservation↗

DOE OSTI2022

3D Time-Lapse Electrical Resistivity Imaging of Rock Damage Patterns and Gas Flow Paths Resulting from Two Underground Chemical Explosions

Abstract Rock damage from underground nuclear explosions (UNEs) has a strong influence on sub-surface gas movement and on seismic waveform characteristics, both of which are used to detect UNEs. Although advanced numerical simulation capabilities exist to predict rock damage patterns and corresponding detection signals, those predictions are dependent on (generally) unknown properties of the host rock. For example, the effects of in-situ mechanical heterogeneities on the explosively generated damage/fractures that provide gas flow pathways to the surface are not well understood, due largely to the difficulty in accessing and characterizing the near-source region. In this paper we demonstrate the emerging use of electrical resistivity tomography (ERT) for imaging rock damage and gas flow patterns resulting from two relatively small-scale underground chemical explosions. Pre-explosion ERT and crosshole seismic imaging revealed a natural fracture zone within the test bed. Post-explosion imaging revealed that the damage zone was non-symmetric and was focused primarily within the pre-existing fracture zone, located 10 m above the first explosion and 5 m above the second explosion. Time-lapse ERT imaging of heated air injected into the detonation borehole revealed the primary gas flow paths to be within the upper margin of the same primary damage zone. These results point to the utility of ERT imaging for understanding rock damage and gas flow patterns under experimental conditions, and to the importance of understanding the effects of geologic heterogeneity on UNE detection signals, particularly gas surface breakthrough times.

58 GEOSCIENCES↗

DOE OSTI2022

Scalable probabilistic estimates of electric vehicle charging given observed driver behavior

To prepare for rapid growth in global electric vehicle adoption, grid and policy planners depend on detailed forecasts of future charging demand. In this paper we propose a novel holistic, scalable, probabilistic framework to produce large-scale estimates of electric vehicle charging load for long-term planning that capture real drivers’ charging patterns. Our framework captures the uncertainty and stochasticity in charging demand by taking a graphical modeling approach. It has three core elements: driver groups, charging segment choices, and charging session time and energy requirements. The framework uses hierarchical clustering to group drivers by their charging histories, capturing their heterogeneous behaviors and preferences across different segments or types of charging. The framework uses probabilistic mixture models for each driver group’s sessions to identify the unique charging behaviors observed within each segment. We illustrate its application with a large data set from California, profiling the charging patterns and unique driver clusters it identifies. Using the model knobs representing drivers’ battery capacities, behavior, and segment access we present scenarios for California’s charging demand in 2030 with 8 million passenger electric vehicles. Peak charging demand ranged from 3.3 to 8.7 GW across scenarios. Furthermore, each was calculated in under 45 s on a laptop computer.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Energy-Dispersive X-ray Diffraction: Operando Visualization of Electrochemical Activity of Thick Electrodes

Abstract not provided

Bruck, Andrea M.↗

DOE OSTI2021

Cylinders and jets in crossflow: Wake formations as a result of varying geometric proximities

The combined flow physics of several canonical flow configurations is experimentally studied. Here, we analyze an array of jets issuing into a crossflow, then immediately navigating past an array of cylinders. This is achieved with a 2 × 3 triangular pattern of jets and symmetric cylinders at three jets to crossflow velocity ratios, enabling near-complete optical access of each jet, with velocities measured by time-resolved particle image velocimetry. Jet trajectories reveal that each configuration adheres to a power-law trend and that greater penetration is achieved by the downstream and confined jets compared to the more conventional upstream one. Recirculation regions of the upstream and downstream jets are nearly independent, with the confined jet encompassing regions of overlap with both. Turbulent statistics reveal the influence of geometric placement and velocity ratio on the time-averaged velocity, anisotropy, and Reynolds stresses incurred by each jet. Galilean decomposition utilizes a supplemental crossflow-only velocity field to delineate the influence of each jet's low- and high-pressure regions on the otherwise uniform stream. Proper orthogonal decomposition suggests that increased jet penetration decreases the number of modes required for truncation in the investigated spanwise plane. Vortex identification algorithms are applied to the reconstructed flow fields, reaffirming that with increasing velocity ratio, the jets generate vortices of their own in similar statistical formations as the cylinders. This investigation provides a foundation to aid future modeling efforts toward characterizing flow physics of importance in designing and passively controlling transverse jets with varying blockage proximities in a crossflow.

Kristo, Paul J. (ORCID:0000000225605175)↗

DOE OSTI2013

Transportation Energy Futures Series: Effects of the Built Environment on Transportation: Energy Use, Greenhouse Gas Emissions, and Other Factors

Planning initiatives in many regions and communities aim to reduce transportation energy use, decrease emissions, and achieve related environmental benefits by changing land use. This report reviews and summarizes findings from existing literature on the relationship between the built environment and transportation energy use and greenhouse gas emissions, identifying results trends as well as potential future actions. The indirect influence of federal transportation and housing policies, as well as the direct impact of municipal regulation on land use are examined for their effect on transportation patterns and energy use. Special attention is given to the 'four D' factors of density, diversity, design and accessibility. The report concludes that policy-driven changes to the built environment could reduce transportation energy and GHG emissions from less than 1% to as much as 10% by 2050, the equivalent of 16%-18% of present-day urban light-duty-vehicle travel. This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a Department of Energy-sponsored multi-agency project initiated to pinpoint underexplored strategies for abating GHGs and reducing petroleum dependence related to transportation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

Transportation Energy Futures Series: Effects of the Built Environment on Transportation: Energy Use, Greenhouse Gas Emissions, and Other Factors

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2015

Superstrings

The quest to find the ultimate building blocks of nature is one of the oldest in all of physics. While we are far from knowing the answer to that question, one intriguing proposed answer is that all matter is composed of tiny “strings.” The known particles are simply different vibrational patterns of these strings. In this video, Fermilab’s Dr. Don Lincoln explains this idea, using interesting and accessible examples of real-world vibrations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗

DOE OSTI2022

Root Foraging Alters Global Patterns of Ecosystem Legacy From Climate Perturbations

The response of terrestrial ecosystems to climate perturbations typically persist longer than the timescale of the forcing, a phenomenon broadly referred to as legacy. Understanding the strength of legacy is critical for predicting ecosystem sensitivity to climate extremes and the extent that persistent changes in surface-atmosphere exchange might feedback onto climate. The cause of ecosystem legacy has been associated with myriad factors such as changes in aboveground biomass, however, few studies have tested how changes in the depth distribution of fine roots in response to perturbation might alter an ecosystem's recovery time. We explore this question using an Earth System Model that includes a dynamic root module where vegetation can forage for water and nutrients by altering their root profiles. The global simulations presented here show that in response to water stress events most ecosystems deepen their root profiles. In semi-arid ecosystems, this response expedites recovery (i.e., less legacy) relative to simulations without dynamics roots because access to deeper water pools after the initial event remains favorable. In wetter ecosystems, the development of deeper root profiles slows down the recovery timescale (i.e., more legacy) because the deeper root profile reduces access to nutrients and is thus unfavorable. Here, the simulations show that while the inclusion of dynamic roots might only minimally affect global patterns of Gross Primary Productivity and Evapotranspiration, the shift in root profile alters the timescale of recovery. Studies interested in the sustained response of land surfaces fluxes to climate disturbances should consider models that include dynamic root capability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Efficient Aho-Corasick String Matching on Emerging Multicore Architectures

String matching algorithms are critical to several scientific fields. Beside text processing and databases, emerging applications such as DNA protein sequence analysis, data mining, information security software, antivirus, ma- chine learning, all exploit string matching algorithms [3]. All these applica- tions usually process large quantity of textual data, require high performance and/or predictable execution times. Among all the string matching algorithms, one of the most studied, especially for text processing and security applica- tions, is the Aho-Corasick algorithm. 1 2 Book title goes here Aho-Corasick is an exact, multi-pattern string matching algorithm which performs the search in a time linearly proportional to the length of the input text independently from pattern set size. However, depending on the imple- mentation, when the number of patterns increase, the memory occupation may raise drastically. In turn, this can lead to significant variability in the performance, due to the memory access times and the caching effects. This is a significant concern for many mission critical applications and modern high performance architectures. For example, security applications such as Network Intrusion Detection Systems (NIDS), must be able to scan network traffic against very large dictionaries in real time. Modern Ethernet links reach up to 10 Gbps, and malicious threats are already well over 1 million, and expo- nentially growing [28]. When performing the search, a NIDS should not slow down the network, or let network packets pass unchecked. Nevertheless, on the current state-of-the-art cache based processors, there may be a large per- formance variability when dealing with big dictionaries and inputs that have different frequencies of matching patterns. In particular, when few patterns are matched and they are all in the cache, the procedure is fast. Instead, when they are not in the cache, often because many patterns are matched and the caches are continuously thrashed, they should be retrieved from the system memory and the procedure is slowed down by the increased latency. Efficient implementations of string matching algorithms have been the fo- cus of several works, targeting Field Programmable Gate Arrays [4, 25, 15, 5], highly multi-threaded solutions like the Cray XMT [34], multicore proces- sors [19] or heterogeneous processors like the Cell Broadband Engine [35, 22]. Recently, several researchers have also started to investigate the use Graphic Processing Units (GPUs) for string matching algorithms in security applica- tions [20, 10, 32, 33]. Most of these approaches mainly focus on reaching high peak performance, or try to optimize the memory occupation, rather than looking at performance stability. However, hardware solutions supports only small dictionary sizes due to lack of memory and are difficult to customize, while platforms such as the Cell/B.E. are very complex to program.

Tumeo, Antonino↗

DOE OSTI2024

Anomalous localization in a kicked quasicrystal

Abstract Quantum transport can distinguish between dynamical phases of matter. For instance, ballistic propagation characterizes the absence of disorder, whereas in many-body localized phases, particles do not propagate for exponentially long times. Additional possibilities include states of matter exhibiting anomalous transport in which particles propagate with a non-trivial exponent. Here we report the experimental observation of anomalous transport across a broad range of the phase diagram of a kicked quasicrystal. The Hamiltonian of our system has been predicted to exhibit a rich phase diagram, including not only fully localized and fully delocalized phases but also an extended region comprising a nested pattern of localized, delocalized and multifractal states, which gives rise to anomalous transport. Our cold-atom realization is enabled by new Floquet engineering techniques, which expand the accessible phase diagram by five orders of magnitude. Mapping transport properties throughout the phase diagram, we observe disorder-driven re-entrant delocalization and sub-ballistic transport, and we present a theoretical explanation of these phenomena based on eigenstate multifractality.

Physics↗

DOE OSTI2025

Roadmap for Photonics with 2D Materials

Triggered by advances in atomic-layer exfoliation and growth techniques, along with the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or a few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals now constitute a broad research field expanding in multiple directions through the combination of layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary subset of those directions, where 2D materials contribute remarkable nonlinearities, long-lived and ultraconfined polaritons, strong excitons, topological and chiral effects, susceptibility to external stimuli, accessibility, robustness, and a completely new range of photonic materials based on layer stacking, gating, and the formation of moiré patterns. These properties are being leveraged to develop applications in electro-optical modulation, light emission and detection, imaging and metasurfaces, integrated optics, sensing, and quantum physics across a broad spectral range extending from the far-infrared to the ultraviolet, as well as enabling hybridization with spin and momentum textures of electronic band structures and magnetic degrees of freedom. The rapid expansion of photonics with 2D materials as a dynamic research arena is yielding breakthroughs, which this Roadmap summarizes while identifying challenges and opportunities for future goals and how to meet them through a wide collection of topical sections prepared by leading practitioners.

2D materials↗

DOE OSTI2021

Isolation and characterization of a californium metallocene

Californium (Cf) is currently the heaviest element accessible above microgram quantities. Cf isotopes impose severe experimental challenges due to their scarcity and radiological hazards. Consequently, chemical secrets ranging from the accessibility of 5f/6d valence orbitals to engage in bonding, the role of spin–orbit coupling in electronic structure, and reactivity patterns compared to other f elements, remain locked. Organometallic molecules were foundational in elucidating periodicity and bonding trends across the periodic table, with a twenty-first-century renaissance of organometallic thorium (Th) through plutonium (Pu) chemistry, and to a smaller extent americium (Am), transforming chemical understanding. Yet, analogous curium (Cm) to Cf chemistry has lain dormant since the 1970s. Here, we revive air-/moisture-sensitive Cf chemistry through the synthesis and characterization of [Cf(C 5 Me 4 H) 2 Cl 2 K(OEt 2 )] n from two milligrams of 249 Cf. This bent metallocene motif, not previously structurally authenticated beyond uranium (U), contains the first crystallographically characterized Cf–C bond. Analysis suggests the Cf–C bond is largely ionic with a small covalent contribution. Lowered Cf 5f orbital energy versus dysprosium (Dy) 4f in the colourless, isoelectronic and isostructural [Dy(C 5 Me 4 H) 2 Cl 2 K(OEt 2 )] n results in an orange Cf compound, contrasting with the light-green colour typically associated with Cf compounds.

Chemical bonding↗

DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Double diffraction imaging of x-ray induced structural dynamics in single free nanoparticles

Abstract Because of their high photon flux, x-ray free-electron lasers (FEL) allow to resolve the structure of individual nanoparticles via coherent diffractive imaging (CDI) within a single x-ray pulse. Since the inevitable rapid destruction of the sample limits the achievable resolution, a thorough understanding of the spatiotemporal evolution of matter on the nanoscale following the irradiation is crucial. We present a technique to track x-ray induced structural changes in time and space by recording two consecutive diffraction patterns of the same single, free-flying nanoparticle, acquired separately on two large-area detectors opposite to each other, thus examining both the initial and evolved particle structure. We demonstrate the method at the extreme ultraviolet (XUV) and soft x-ray Free-electron LASer in Hamburg (FLASH), investigating xenon clusters as model systems. By splitting a single XUV pulse, two diffraction patterns from the same particle can be obtained. For focus intensities of about 2 × 10 12 W cm −2 we observe still largely intact clusters even at the longest delays of up to 650 picoseconds of the second pulse, indicating that in the highly absorbing systems the damage remains confined to one side of the cluster. Instead, in case of five times higher flux, the diffraction patterns show clear signatures of disintegration, namely increased diameters and density fluctuations in the fragmenting clusters. Future improvements to the accessible range of dynamics and time resolution of the approach are discussed.

Sauppe, M. (ORCID:0000000163123102)↗

DOE OSTI2026

Intrinsic nonlocality of spin- and polarization-resolved probabilities in strong-field quantum electrodynamics

Spin and polarization are central to precision tests of fundamental physics and for interpreting radiation from astrophysical sources and ultraintense laser-matter experiments. Here, focusing on the fundamental process of nonlinear Compton scattering, we demonstrate that a key assumption underlying current strong-field quantum electrodynamics models, i.e., that emission can be treated as an instantaneous random event sampled from a local differential rate, is inconsistent once emission angles, electron spin, and/or photon polarization are resolved. Namely, even in strictly constant and uniform fields , the resulting fully differential distribution is sign indefinite, yielding negative inferred probabilities. The physical reason is that the photon emission probability builds up over a finite length of the electron trajectory, the formation region, during which the electron direction changes by roughly the same small angle that defines the radiation cone. Therefore, we put forward a new method where we integrate over this formation region analytically to obtain a physically consistent electron spin and photon polarization model. We show that the implementation of our model is compatible with existing Monte Carlo and particle-in-cell workflows. Simulations of a GeV-class electron-laser collision accessible at current petawatt facilities and of emission in a pulsarlike magnetic field are shown to reveal spin and polarization patterns that differ even qualitatively from state-of-the-art local models. In particular, our new model predicts substantial angle-dependent circular photon polarization where the well-known collinear-emission approach yields none, and a pronounced helicity bias in the recoiling electrons absent from current predictions. These findings have direct implications for upcoming strong-field QED experiments and for interpreting polarized radiation from extreme astrophysical environments.

astrophysical electromagnetic fields↗

DOE OSTI2014

SU-E-J-129: A Strategy to Consolidate the Image Database of a VERO Unit Into a Radiotherapy Management System

Purpose: To present a strategy to integrate the imaging database of a VERO unit with a treatment management system (TMS) to improve clinical workflow and consolidate image data to facilitate clinical quality control and documentation. Methods: A VERO unit is equipped with both kV and MV imaging capabilities for IGRT treatments. It has its own imaging database behind a firewall. It has been a challenge to transfer images on this unit to a TMS in a radiation therapy clinic so that registered images can be reviewed remotely with an approval or rejection record. In this study, a software system, iPump-VERO, was developed to connect VERO and a TMS in our clinic. The patient database folder on the VERO unit was mapped to a read-only folder on a file server outside VERO firewall. The application runs on a regular computer with the read access to the patient database folder. It finds the latest registered images and fuses them in one of six predefined patterns before sends them via DICOM connection to the TMS. The residual image registration errors will be overlaid on the fused image to facilitate image review. Results: The fused images of either registered kV planar images or CBCT images are fully DICOM compatible. A sentinel module is built to sense new registered images with negligible computing resources from the VERO ExacTrac imaging computer. It takes a few seconds to fuse registered images and send them to the TMS. The whole process is automated without any human intervention. Conclusion: Transferring images in DICOM connection is the easiest way to consolidate images of various sources in your TMS. Technically the attending does not have to go to the VERO treatment console to review image registration prior delivery. It is a useful tool for a busy clinic with a VERO unit.

60 APPLIED LIFE SCIENCES↗