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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

LLNL FESP Theory Highlights: March 2024

Predictions of heat load widths λ q based on particle orbits alone are very pessimistic. This paper shows that pedestal peeling-ballooning (P-B) MHD turbulence broadens the stable SOL by the transport, or spreading, of fluctuation energy from the pedestal. λ q is seen to increase with Γ ε , the fluctuation energy density flux. We elucidate the fundamental physics of the spreading process. Γ ε increases with pressure fluctuation correlation length. P-B turbulence is seen to be especially effective at spreading, on account of its large effective mixing length. Spreading is shown to be a multiscale process, which is enhanced by the synergy of large and small-scale modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

End-of-Century Changes in Orographic Precipitation with the Intermediate Complexity Atmospheric Research Model over the Western United States

Abstract Downscaled precipitation projections were created using the Intermediate Complexity Atmospheric Research (ICAR) model over the western United States to increase the physical realism in orographic precipitation changes. End-of-century simulations from eight models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) were downscaled with ICAR and compared to the widely utilized statistically downscaled dataset, localized constructed analogs (LOCAs), to understand where and why projections of cool-season (September–May) precipitation differed. ICAR and LOCA precipitation projections were similar, but their sign differed in hydrologically relevant regions likely due to ICAR’s simulation of microphysics and mesoscale dynamics with high-resolution topography (6 km). In the Pacific Northwest, cool-season precipitation projections from ICAR showed an increase on the windward side of the Cascades and no significant change within the lee. This difference between the windward and leeward side was attributed to reduced zonal wind speeds, allowing more time for microphysical processes within ICAR. This contrast is enhanced by rain’s faster fall speed compared to snow, limiting transport into the lee. Meanwhile, LOCA projected an increase in precipitation across the Cascades. In the Upper Colorado River basin, LOCA projected an increase in precipitation in high elevation regions (>3000 m), but ICAR projected no significant change or a decrease in precipitation. High elevation differences were most evident in the spring and fall and were also attributed to a snow-to-rain transition and dynamical processes that impacted orographic enhancement within ICAR. Idealized, controlled studies are needed to better isolate individual processes, but these results underscore the importance of including microphysics and mesoscale dynamics within regional-scale precipitation projections. Significance Statement A set of global climate model simulations was downscaled using an atmospheric model that contains key physical equations, referred to as Intermediate Complexity Atmospheric Research (ICAR). ICAR was used to examine projected changes in end-of-century cool-season precipitation over mountains in the western United States. Precipitation projections from ICAR were similar to projections that used statistical relationships to downscale climate projections. However, projections differed between ICAR and statistically downscaled datasets in whether they increased, decreased, or stayed the same in specific, hydrologically relevant regions such as the eastern Cascades and high elevation areas of the Upper Colorado River basin. These differences were attributed to the simulation of physical processes in ICAR. The results highlight the importance of kilometer-scale atmospheric processes in regional climate projections.

Currier, William Ryan [NOAA/Physical Sciences Labo↗
DOE OSTI2021

Massively Parallel Capability in Sierra/SD for Simulation Vibration with Piezoelectrics

Sierra/SD is an engineering structural dynamics code that provides Sandia and other customers a tool to model structural and acoustic physics on large complex physical systems using massively parallel processing. This report provides a detailed overview on Sierra/SD’s most recent physics package: coupled electro-mechanical physics. This capability uses the finite element method to model coupled electro-mechanical physics exhibited by piezoelectric materials. This report provides an applications overview, theory overview, and verification examples demonstrating the electro-mechanical physics modeling capabilities of Sierra/SD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Small Angle X-Ray Scattering and Polymers

Polymers are some of the most versatile and useful materials on this planet. Properties of polymers are a result of their processing history, chemical makeup, and physical structure. Therefore, characterization is necessary to develop new polymer formulations and processes. Many established techniques for polymer characterization are destructive, and alternative methods that don’t destroy samples are in high demand. Thermal analysis techniques such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) yield important characteristic information for polymer samples while destroying the sample. Non-destructive strategies such as X ray diffraction (XRD) or small angle X-ray scattering (SAXS) can also find polymer characteristics while preserving a sample’s attributes. Sample preservation is desirable when investigating novel formulation and processing methods. SAXS analysis will be done on two different polymers: polyethylene, and a triblock copolymer. Using these two polymers allows for confidence between tests as polyethylene is very common, and well understood. Polyethylene analysis will be done first, and when methods have been refined so that the SAXS data matches accepted thermal analysis values for HDPE, there will be confidence to use the same analysis on block copolymers. Block copolymers remain relatively unexplored, so SAXS data will yield valuable information on their characteristics. Understanding the mechanical properties of these polymers is paramount to their application in upcycling, a process by which waste plastic can be converted into a higher value commodity. For this project, the upcycling process will be done via 3-dimensional (3d) printing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Gaseous Iodine Sorbents: A Comparison between Ag-Loaded Aerogel and Xerogel Scaffolds

The general properties of aluminosilicate aerogels and xerogels with silver nanoparticles as sorbents for capturing iodine gas [I2(g)] were investigated. The structures, morphologies, compositions, and pore structures of aerogel and xerogel were compared using powder X-ray diffraction (PXRD), scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray spectroscopy, as well as specific surface area (SSA) and pore size analyses. The as-made aerogels, xerogels, and heat-treated aerogels were impregnated with Ag by aqueous ion exchange using AgNO3 solutions, and PXRD patterns showed the presence of nanocrystalline Ag0 after Ag-impregnation. Iodine loadings of aerogels and xerogels were 0.33–0.41 g g-1. The Ag-impregnated aerogels without heat-treatment showed an 8 mass% higher iodine loading than Ag-impregnated xerogels and 3 mass% higher than heat-treated Ag-impregnated aerogels. All gels after iodine uptake showed the presence of AgI, indicating chemisorption of iodine to silver. The SSA values of the as-made gels were 419–598 m2 g-1 but decreased significantly to 34–120 m2 g-1 after Ag-impregnation and iodine uptake processes. Overall, changes in physical and chemical properties of aerogels and xerogels after iodine uptake were similar, and the difference in iodine loading capacities of the aerogels and xerogels was not significant while the synthesis process of xerogel is more practical because it does not require supercritical drying.

aerogel, xerogel, iodine capture↗
DOE OSTI2023

The Forward Physics Facility at the High-Luminosity LHC

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Electrochemical Reaction Engineering: A Lost (and Found) Art

The heart (or, perhaps more appropriately, the stomach) of any chemical manufacturing process is the reactor, where chemical and physical transformations occur under precisely controlled conditions. For non-electrochemical industrial processes, the design, optimization and operation of chemical reactors are extremely well developed, drawing upon the principles of chemistry, physics, calculus and economics. However, electrolytic processes face a dearth of well documented reaction engineering principles and design practices. This tutorial-style poster presentation introduces electrochemistry students and industrial practitioners to the "lost art" of reaction engineering and its organic extensions to electrochemical devices. Electrochemical reactor fundamentals and illustrative design equations are presented alongside a proposed five-step generalizable methodology for electrochemical reactor scale-up. Overall, this contribution seeks to educate electrochemistry researchers on practical means of reducing laboratory technologies to commercial practice.

electrochemistry↗
DOE OSTI2022

Electron Weibel instability induced magnetic fields in optical-field ionized plasmas

Generation and amplification of magnetic fields in plasmas is a long-standing topic that is of great interest to both plasma and space physics. The electron Weibel instability is a well-known mechanism responsible for self-generating magnetic fields in plasmas with temperature anisotropy and has been extensively investigated in both theory and simulations, yet experimental verification of this instability has been challenging. Recently, we demonstrated a new experimental platform that enables controlled initialization of highly nonthermal and/or anisotropic plasma electron velocity distributions via optical-field ionization. Using an external electron probe bunch from a linear accelerator, the onset, saturation, and decay of the self-generated magnetic fields due to electron Weibel instability were measured for the first time to our knowledge. In this paper, we will first present experimental results on time-resolved measurements of the Weibel magnetic fields in non-relativistic plasmas produced by Ti:Sapphire laser pulses (0.8 μm) and then discuss the feasibility of extending the study to a quasi-relativistic regime by using intense CO 2 (e.g., 9.2 μm) lasers to produce much hotter plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Building Datasets and Training Methods for ML Based Magnet Quench Detection

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

Khan, Maira [Fermilab]↗
DOE OSTI2023

SCALE depletion capabilities for molten salt reactors and other liquid-fueled systems

Nuclear reactor systems that use fuel dissolved in a liquid have the potential for enhanced safety characteristics, improved fuel-cycle outcomes, and more efficient isotope-production configurations. In these reactor systems, the fueled liquid may simultaneously undergo irradiation, physical and chemical removal processes, and fueling. The modeling and simulation of this transmutation and decay with material additions and removals is an ongoing research area. An accurate simulation tool is critical to the reactor and fuel-cycle design, reactor deployment, and source-term characterization for these advanced reactor systems. The work described herein involved implementing, testing, and applying the capability to perform reactor physics simulations within the Oak Ridge National Laboratory-developed SCALE suite for nuclear systems analyses and design, leveraging much of its pedigree in quality-assurance and reactor-analysis capabilities. The functionalities to simulate irradiation with material feeds and removals had been added in ORIGEN, and the TRITON reactor physics sequence was extended to calculate the total removed material and track external nonirradiated mixtures to estimate separate processing or waste streams. Results from these capabilities align with analytical expectations obtained from ORIGEN for simplified test cases and with expectations for a molten salt reactor application. This implementation, available with the SCALE 6.3 release, provides for a more efficient and accurate material accountability methodology, allowing for the characterization, design, and analysis of the complete isotopic material inventory of advanced liquid-fueled systems for a variety of applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Cryogenic Detector Development for SuperCDMS

The dark matter (DM) problem is currently one of the most pressing issues facing physics. This thesis presents some of my work and contributions to the detectors employed by the Super Cryogenic Dark Matter Search (SuperCDMS), one of the many experiments currently investigating DM. First, we present an overview of astrophysical and cosmological observations to give insight into our current understanding of DM and its role in the universe. Some potential particle DM candidates are also discussed. Then we describe the SuperCDMS experiment and relevant detector technologies. We go into particular detail in discussing the phonon sensors, deriving the relationships that define their fundamental working principles and describing their practical deployment. We also discuss the main noise contributions to the phonon sensors which can limit experimental sensitivity. We then present some work related to reconstruction of event position in the detectors, including new analysis techniques and a n ovel cryogenic calibration source mover we have developed and tested. We also present tests of improving experimental scalability by increasing individual detector masses using the largest-diameter cryogenic Si detectors yet operated. We present first experimental results from the novel use of $(n,\gamma)$ processes to calibrate the low energy nuclear recoil energy scale. This work is essential to understand how DM-nuclear interactions will manifest in semiconductor detectors. We also take a short look at the newest, most sensitive detectors currently operated by SuperCDMS and discuss how they are allowing new insights into critical physics processes in all SuperCDMS detectors. Finally, we demonstrate how some of the various improvements discussed in this work can improve the experimental reach of the next generation of SuperCDMS at SNOLAB.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

The understudied winter: Evidence of how precipitation differences affect stream metabolism in a headwater

Climate change is causing pronounced shifts during winter in the US, including shortening the snow season, reducing snowpack, and altering the timing and volume of snowmelt-related runoff. These changes in winter precipitation patterns affect in-stream freeze-thaw cycles, including ice and snow cover, and can trigger direct and indirect effects on in-stream physical, chemical, and biological processes in ~60% of river basins in the Northern Hemisphere. We used high-resolution, multi-parameter data collected in a headwater stream and its local environment (climate and soil) to determine interannual variability in physical, chemical, and biological signals in a montane stream during the winter of an El Niño and a La Niña year. We observed ~77% greater snow accumulation during the El Niño year, which caused the formation of an ice dam that shifted the system from a primarily lotic to a lentic environment. Water chemistry and stream metabolism parameters varied widely between years. They featured anoxic conditions lasting over a month, with no observable gross primary production (GPP) occurring under the ice and snow cover in the El Niño year. In contrast, dissolved oxygen and GPP remained relatively high during the winter months of the La Niña year. These redox and metabolic changes driven by changes in winter precipitation have significant implications for water chemistry and biological functioning beyond the winter. Our study suggests that as snow accumulation and hydrologic conditions shift during the winter due to climate change, hot-spots and hot-moments for biogeochemical processing may be reduced, with implications for the downstream movement of nutrients and transported materials.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order–disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. Here, we propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order–disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗
DOE OSTI2023

Forward Physics Facility

High-energy collisions at the High-Luminosity Large Hadron Collider will produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located 617--682 meters from the ATLAS interaction point along the beam collision axis and shielded by concrete and rock, will host a suite of experiments to probe Standard Model processes and search for physics beyond the Standard Model. The FPF is supported by a rapidly growing user community of 400 physicists, and four pathfinder detectors that are currently operating at the LHC have already yielded promising results, including the first direct detection of collider neutrinos. Here we provide an Executive Summary, and then briefly summarize the status of the FPF, including its rich science case, the facility itself, the proposed FPF experiments and their technical readiness, the preliminary schedule and budget profile, and plans for international participation. Submitted to the DOE/NSF HEPAP Particle Physics Projects Prioritization Panel~(P5)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

End-to-End Pipeline for Trigger Detection on Hit and Track Graphs

There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. Here, the triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.

97 MATHEMATICS AND COMPUTING↗