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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How deep should we go to understand roots at the top of the world?

Harsh environmental conditions and the short summers of northern, high-latitude biomes impose unique constraints on the plants that live in the arctic tundra and the boreal forest. To escape the harsh aboveground environment, plants in these habitats often allocate a large portion of their biomass belowground to facilitate nutrient acquisition. In turn, the proximity of living plant roots to vast stores of sequestered soil carbon in these biomes means that shifts in rooting depth distribution and the size of the root–soil interface could significantly contribute to ongoing climate change. Indeed, plant ‘priming’ of rhizosphere decomposition via root exudation, particularly from shallowly distributed roots, can lead to losses of carbon from tundra soils. While we have a hard-won understanding of the distribution of plant communities across the arctic tundra and the boreal forest from direct field observations scaled to the landscape level using climate-informed mapping techniques (i.e. the Circumpolar Arctic Vegetation Map (CAVM); Walker et al. , 2005), these vegetation maps are only the tip of the iceberg (Iversen et al. , 2015). Root form and function remain hidden beneath the land surface. In an article recently published in New Phytologist , Blume-Werry et al. (2023, 10.1111/nph.18998) asked whether rooting depth distribution, and ensuing carbon emissions, could be inferred from commonly used vegetation mapping classifications across the pan-Arctic. An important question to guide our understanding, mapping, and prediction of belowground characteristics and ecosystem feedbacks at the top of the world. Unfortunately, they found that the answer was ‘not quite’. While rooting depth distribution varied demonstrably, in turn causing substantial changes in modeled carbon emissions via rhizosphere ‘priming’, variation across rooting depth profiles did not correspond with vegetation mapping classes. If we are unable to predict belowground rooting depth distributions across large spatial scales by leveraging aboveground vegetation community distributions, how then should belowground researchers proceed?

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Grid Optimization (GO) Competition Platform

A software for a multi-challenge power-flow grid optimization competition was developed. The platform brings together high performance computing clusters, webservers, databases, competition datasets, schedulers, evaluation codes, and a multitude of language compilers and optimization solvers to host the competition. The original video announcing the competition, from former Secretary Perry, at: https://www.youtube.com/watch?v=hZwX3P9vS8M

Veeramany, Arun↗
DOE OSTI2022

Going mobile to address emerging climate equity needs in the heterogeneous urban environment

We report the Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step towards this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods (IOPs).. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SMC2021 Where to go in atomic world

'Graphene_CrSi.npy' contains a scanning transmission electron microscopy (STEM) movie from graphene monolayer. The movie is a sequence of atom-resolved images from the same sample region that undergoes chemical and structural transformations due to interaction with electron beam (which is used to perform imaging). 'topo_defects.npy' contains coordinates of some of the objects of interest (topological defects in graphene). It is a dictionary, where keys are frame numbers and values are xy coordinates. These objects are usually localized in relatively small areas of the image and we are interested in identifying them without having to scan an entire grid (which leads to fast degradation of the sample).

36 MATERIALS SCIENCE↗
DOE OSTI2025

TCLK Must Stay! CAMAC Must Go! How Does Fermilab Move Forward

The current Timing System at Fermilab has been around for 40 years and currently relies on 7 CAMAC crates and over 100 CAMAC cards to produce the Tevatron Clock (TCLK). Thanks to the ingenuity of those before us, this has allowed Fermilab the flexibility to change the timing and Events for its accelerator as beamlines and projects have changed over the years. With the advent of the Proton Improvement Plan-II (PIP-II), the Timing System at Fermilab is being reimagined into a single chassis with even greater flexibility and functionality for decades to come while tackling the ever-challenging task of maintaining backwards compatibility.

Austin, M. R. [Fermilab]↗
DOE OSTI2022

When Do Efficiency and Demand Flexibility Go Hand-in-hand?

Utilities have been experimenting with integrated demand side management (IDSM) programs since the 2000s. The potential benefits of improved program cost-effectiveness and customer engagement from combining energy efficiency (EE), demand flexibility (DF), and other distributed energy resources into an integrated customer offering have been recognized although there are several known regulatory and program administrative challenges. In addition, as buildings adopt EE measures, the baseline load profile change generally reduces the potential load that can be shed or shifted. This has been a significant technical barrier for customers and program implementers. However, it is a myth that EE always reduces DF. Load change from EE can be time-varying. Therefore, whether EE improves or reduces DF should be evaluated on an individual measure basis accounting for weather dependencies and interactions. For IDSM program design purpose, it is useful to understand how common EE features influence DF and the underlying building physics. In this paper, we use parametric simulations of a prototype medium office building to evaluate how various EE features influence DF, measured by a “demand decrease intensity” (W/ft2) metric. These EE features cover envelope characteristics, internal loads, and airside HVAC system. The parametric analysis shows that some efficient HVAC control measures will increase DF but not the traditional building envelope, lighting, and ventilation-related efficiency measures. These findings contribute to the technical basis for achieving enhanced energy benefits by packaging appropriate HVAC control measures in IDSM program design. Program developers should further validate these results in targeted pilot projects.

Yu, Lili↗
DOE OSTI2025

Four no-go theorems on the existence of spin and orbital angular momentum of massless bosons

The past decades have seen substantial interest in the so-called orbital angular momentum (OAM) of light, driven largely by its diverse range of applications. However, there are fundamental theoretical issues with decomposing the angular momentum of massless particles, such as photons, into spin (SAM) and orbital angular momentum parts. While the angular momentum of massive particles has a natural splitting into the Wigner SAM and OAM, there are numerous proposed splittings for photons and no consensus about which is correct. Moreover, it has been shown that most of the proposed SAM and OAM operators do not satisfy the defining commutation relations of angular momentum operators and are thus not legitimate splittings. Here, we prove that it is generally impossible to split the total angular momentum operator of massless bosons, such as photons and gravitons, into spin and orbital parts. We prove two further generalizations of this result, showing that there are no SAM-OAM splittings even if (1) the SAM operator generates non-internal symmetries or (2) if one allows the SAM and OAM operators to generate non-SO(3) symmetries.

Chern numbers↗
DOE OSTI2020

HEP ML/Optimization Go Quantum – QuantISED Pilot

This poster includes sections on: Quantum annealers for classification, Quantum gate models for classification, Software and Workflows, and Quanutm optimization for LHC physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Ready, Set, Getting to Go: America's Nuclear Test Readiness Posture

In a geopolitical environment once again dominated by Great Power competition, the stakes of maintaining a credible nuclear deterrent also returned to the forefront of national security. Yet, as nearly thirty years have passed without the United States conducting a nuclear test, there exists considerable uncertainty on how quickly, or even if, the United States could resume nuclear testing—a key component of a credible deterrent. However, there remains a legal requirement, with origins in hard-earned Cold War lessons, for the nation to return to underground nuclear testing if called upon to do so. This paper provides an overview of nuclear testing, the nation’s current state of preparedness, and offers recommendations on how to improve its test readiness posture.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Silicon Consortium Project: No-Go on Moir Interferometry for Measuring SEI Strain as a Probe for Calendar Life Testing

Silicon is a promising candidate as a next generation anode to replace or complement graphite electrodes due to its high energy density and low lithiation potential. When silicon is lithiated, it experiences over 300% expansion which stresses the silicon as well as its solid electrolyte interphase (SEI) leading to poor performance. The use of nano-sized silicon has helped to mitigate volume expansion and stress in the silicon, yet the silicon SEI is still both mechanically and chemically unstable. Identifying the mechanical failure mechanism of the SEI will help enhance calendar and cycle life performance through improved SEI design. In situ moiré interferometry was investigated to try and track the in-plane strain in the SEI and silicon electrode for this purpose. Moiré can detect on the order of 10 nm changes in displacement and is therefore a useful tool in the measurement of strain. As the sample undergoes small deformations, large changes in the moiré fringe allow for measurements of displacement below the diffraction limit of light. Figure 1a shows how the moiré fringe changes as the sample grating deforms. As the sample contracts or expands, the frequency of the moiré fringe changes, and this change is proportional to the strain in the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Go with the Radflow: Thermal radiation experiments in the high energy-density regime [Slides]

Los Alamos National Laboratory is a nuclear weapons laboratory supporting our nation's defense. In support of the mission is a high energy-density physics program in which we design and execute experiments to study radiation-hydrodynamics phenomena and improve the predictive capability of our large-scale multi-physics software codes on our supercomputers. The Radflow Project is maturing a unique spectroscopic measurement that yields a spatially dependent supersonic radiation wavefront profile. The spectroscopic measurement is much more constraining than the typically used radiography, which measures the density changes due to the shock as the radiation wave goes subsonic. Recently, the spectroscopic measurement has been applied to advanced targets with a detailed heterogeneity representing a single realization of a stochastic medium. The Radflow Project also conducts opacity experiments to explain the current conundrum of the experimental iron opacity differing from theory, which is important in modeling the sun. More than that, though, we need to be able to answer these science questions and be able to validate our theory and codes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗