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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

AN ANALYSIS OF MAGNETOHYDRODYNAMIC INVARIANTS OF MAGNETIC FLUCTUATIONS WITHIN INTERPLANETARY FLUX ROPES

A statistical analysis of magnetic flux ropes, identified by large-amplitude, smooth rotations of the magnetic field vector and a low level of both proton density and temperature, has been performed by computing the invariants of the ideal magnetohydrodynamic (MHD) equations, namely the magnetic helicity, the cross-helicity, and the total energy, via magnetic field and plasma fluctuations in the interplanetary medium. A technique based on the wavelet spectrograms of the MHD invariants allows the localization and characterization of those structures in both scales and time: it has been observed that flux ropes show, as expected, high magnetic helicity states (|σ{sub m}| in [0.6: 1]), but extremely variable cross-helicity states (|σ{sub c}| in [0: 0.8]), which, however, are not independent of the magnetic helicity content of the flux rope itself. The two normalized MHD invariants observed within the flux ropes tend indeed to distribute, neither trivially nor automatically, along the √(σ{sub m}{sup 2}+σ{sub c}{sup 2})=1 curve, thus suggesting that some constraint should exist between the magnetic and cross-helicity content of the structures. The analysis carried out has further showed that the flux rope properties are totally independent of their time duration and that they are detected either as a sort of interface between different portions of solar wind or as isolated structures embedded in the same stream.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

Obata, Yoshihiro (ORCID:0000000303659129)↗
DOE OSTI2022

Soil pore network response to freeze-thaw cycles in permafrost aggregates

This dataset contains data used for the paper "Pore network response to freeze-thaw cycles in permafrost aggregates". The Related References field will be updated with a full citation when available.Climate change in Arctic landscapes may increase freeze-thaw frequency within the active layer as well as newly thawed permafrost. A highly disruptive process, freeze-thaw can deform soil pores and alter the architecture of the soil pore network with varied impacts to water transport and retention, redox conditions, and microbial activity. Our objective was to investigate how freeze-thaw cycles impacted the pore network of newly thawed permafrost aggregates to improve understanding of what type of transformations can be expected from warming Arctic landscapes. We measured the impact of freeze-thaw on pore morphology, pore throat diameter distribution, and pore connectivity with X-ray computed tomography (XCT) using six permafrost aggregates with sizes of 2.5 cm3 from a mineral soil horizon (Bw; 28-50 cm depths) in Toolik, Alaska. Freeze-thaw cycles were performed using a laboratory incubation consisting of five freeze-thaw cycles (-10˚C to 20˚C) over five weeks. Our findings indicated decreasing spatial connectivity of the pore network across all aggregates with higher frequencies of singly connected pores following freeze-thaw. Water-filled pores that were connected to the pore network decreased in volume while the overall connected pore volumetric fraction was not affected. Shifts in the pore throat diameter distribution were mostly observed in pore throats ranges of 100 microns or less with no corresponding changes to the pore shape factor of pore throats. Responses of the pore network to freeze-thaw varied with aggregate, suggesting that initial pore morphology may play a role in driving freeze-thaw response. Our research suggests that freeze-thaw alters the microenvironment of permafrost aggregates during the incipient stage of deformation following permafrost thaw, impacting soil properties and function in Arctic landscapes undergoing transition. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/Erin-Rooney/XCT-freezethaw (DOI: 10.5281/zenodo.5816355).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Dust survival rates in clumps passing through the Cas A reverse shock – I. Results for a range of clump densities

ABSTRACT The reverse shock in the ejecta of core-collapse supernovae is potentially able to destroy newly formed dust material. In order to determine dust survival rates, we have performed a set of hydrodynamic simulations using the grid-based code astrobear in order to model a shock wave interacting with clumpy supernova ejecta. Dust motions and destruction rates were computed using our newly developed external, post-processing code paperboats, which includes gas drag, grain charging, sputtering, and grain–grain collisions. We have determined dust destruction rates for the oxygen-rich supernova remnant Cassiopeia A as a function of initial grain sizes and clump gas density. We found that up to $30\,\mathrm{{{\ \rm per\ cent}}}$ of the carbon dust mass is able to survive the passage of the reverse shock if the initial grain size distribution is narrow with radii around ∼10–50 nm for high gas densities, or with radii around $\sim 0.5\!-\!1.5\,\mathrm{\mu m}$ for low and medium gas densities. Silicate grains with initial radii around 10–30 nm show survival rates of up to $40\,\mathrm{{{\ \rm per\ cent}}}$ for medium- and high-density contrasts, while silicate material with micron-sized distributions is mostly destroyed. For both materials, the surviving dust mass is rearranged into a new size distribution that can be approximated by two components: a power-law distribution of small grains and a lognormal distribution of grains having the same size range as the initial distribution. Our results show that grain–grain collisions and sputtering are synergistic and that grain–grain collisions can play a crucial role in determining the surviving dust budget in supernova remnants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Continuous Security and Configuration Monitoring of HPC Clusters

Continuous security and configuration monitoring of information systems has been a time consuming and laborious task for system administrators at the High Performance Computing (HPC) center. Prior to this project, system administrators had to manually check the settings of thousands of nodes, which required a significant number of hours rendering the old process ineffective and inefficient. This paper explains the application of Splunk Enterprise, a software agent, and a reporting tool in the development of a user application interface to track and report on critical system updates and security compliance status of HPC Clusters. In conjunction with other configuration management systems, the reporting tool is to provide continuous situational awareness to system administrators of the compliance state of information systems. Our approach consisted of the development, testing, and deployment of an agent to collect any arbitrary information across a massively distributed computing center, and organize that information into a human-readable format. Using Splunk Enterprise, this raw data was then gathered into a central repository and indexed for search, analysis, and correlation. Following acquisition and accumulation, the reporting tool generated and presented actionable information by filtering the data according to command line parameters passed at run time. Preliminary data showed results for over six thousand nodes. Further research and expansion of this tool could lead to the development of a series of agents to gather and report critical system parameters. However, in order to make use of the flexibility and resourcefulness of the reporting tool the agent must conform to specifications set forth in this paper. This project has simplified the way system administrators gather, analyze, and report on the configuration and security state of HPC clusters, maintaining ongoing situational awareness. Rather than querying each cluster independently, compliance checking can be managed from one central location.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗
DOE OSTI2012

Scalla: Structured Cluster Architecture for Low Latency Access

Scalla is a distributed low-latency file access system that incorporates novel techniques that minimize latency and maximize scalability over a large distributed system with a distributed namespace. Scalla's techniques have shown to be effective in nearly a decade of service for the high-energy physics community using commodity hardware and interconnects. We describe the two components used in Scalla that are instrumental in its ability to provide low-latency, fault-tolerant name resolution and load distribution, and enable its use as a high-throughput, low-latency communication layer in the Qserv system, the Large Synoptic Survey Telescope's (LSST's) prototype astronomical query system. Scalla arguably exceeded its three main design objectives: low latency, scaling, and recoverability. In retrospect, these objectives were met using a simple but effective design. Low latency was met by uniformly using linear or constant time algorithms in all high-use paths, avoiding locks whenever possible, and using compact data structures to maximize the memory caching efficiency. Scaling was achieved by architecting the system as a 64-ary tree. Nodes can be added easily and as the number of nodes increases, search performance increases at an exponential rate. Recoverability is inherent in that no permanent state information is maintained and whatever state information is needed it can be quickly constructed or reconstructed in real time. This allows dynamic changes in a cluster of servers with little impact on over-all performance or usability. Today, Scalla is being deployed in environments and for uses that were never conceived in 2001. This speaks well for the systems adaptability but the underlying reason is that the system can meet its three fundamental objectives at the same time.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

SU-E-J-204: Can a Commercial System for MR-IGRT Be Used to Treat Patients Without Acquiring a CT Scan?

Patients treated using a magnetic-resonance image guided radiation therapy (MR-IGRT) system received both CT and MR simulations. During planning, the CT is used to determine relative electron density (RED) using a calibration table. This study aims to investigate the feasibility of MR-only treatments by comparing CT-computed dose distributions to those computed with combinations of water (1.0), lung (0.26), tissue (1.02), and bone (1.12) bulk RED overrides, and to identify the effects of the magnetic field on the RED-overridden doses. Methods: Four patients who received treatment using a commercial MR-IGRT system were analyzed (1 lung, 2 abdomen, and 1 pelvis). The clinical plans were computed using the first fraction MRI as primary, and the simulation CT as secondary for REDs. Plans were reoptimized using default bulk RED overrides (water/lung and tissue/lung for the lung patient, water/bone, tissue/bone, water only, and tissue only for the abdomen and pelvis patients). Additionally, each plan was re-optimized to include the static magnetic field. All plans were normalized to the same PTV coverage as the clinical plan. Dose-difference volumes and DVHs were computed for bulk density override plans, and 3D gamma analyses between each plan and its accompanying magnetic field plan were performed using 3%/3 mm dose difference and distance-to-agreement criteria using the PTV and Skin as masking structures. Results: The average differences in PTV and organs-at-risk mean dose for all RED combinations tested were −0.19 Gy (−0.62 – 0.06 Gy) and −0.34 Gy (−1.76 – 0.33 Gy), respectively. The average PTV and Skin gamma pass rates for all RED combinations tested were 99.88% (99.5% – 100%) and 98. 35% (96.3% – 99.6%). No systematic differences in DVHs or isodoses were observed. Conclusions: It is likely that that a commercial MR-IGRT system may produce high quality treatment plans without the need for CT scans. Authors of this abstract are members of the Washington University Radiation Oncology department, which has a research agreement with ViewRay, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by the so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data assimilation and for Bayesian estimation. They also open a perspective for optimal experimental design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Impact of Time-Dependent Reactor and Sensor Physics on Core Power Synthesis (Rev.1)

Online synthesis of power distribution is critical in the operation and control of nuclear power reactors to ensure that the core is operating within safety margins and to provide essential knowledge associated with the burnup of the fuel. In light-water reactors, power synthesis is achieved by using some a priori knowledge of the state of the reactor core and updating based on the signals coming from in-core sensors—namely, self-powered neutron detectors (SPNDs). This report examines the effects of fuel burnup and sensor degradation on the ability to accurately synthesize the power distribution in a pressurized water reactor (PWR), considering the typical low-enriched uranium (LEU, 3%-5% enrichment) fuel cycle as well as the higher enrichment LEU+ (5%-8% enrichment) fuel cycle. Several modeling tools were used to simulate power synthesis based on the responses of SPNDs, with emitters made out of Rh or V. A representative PWR LEU core was modeled using the Polaris/Purdue Advanced Reactor Core Simulator (PARCS) approach. The Monte Carlo N-Particle Transport 6 (MCNP6) code was used as well to calculate response functions between different segments of fuel to individual SPNDs; this is a crucial parameter for power synthesis. The Oak Ridge Isotope GENeration (ORIGEN) package in the Standardized Computer Analyses for Licensing Evaluation (SCALE) code was used to model the time-dependent isotopic transmutation in the SPND emitters. All these data were fed into a custom code that enacted the point-based iterative method to simulate power synthesis. Developmental work was also performed on high-fidelity SPND models in the GEometry ANd Tracking 4 (Geant4) code, which enables higher-accuracy modeling of the current responses from SPNDs. In this work, five sets of time-dependent power synthesis test cases were conducted. In these test cases, systematic changes in the input conditions enabled an analysis of the effect of (1) slightly inaccurate a priori power distribution assumptions with respect to fuel burnup, (2) highly inaccurate a priori power distribution assumptions with respect to fuel burnup (such that burnup is not included in the a priori assumed distribution), and (3) differences between Rh and V SPNDs in terms of downstream consequences of the transmutation in the emitters and the extended nature of the LEU+ fuel cycle in comparison with LEU. The authors discovered that one may permissibly have slightly inaccurate a priori assumptions of the fuel burnup (such that the level of burnup may be slightly underapproximated or overapproximated by the accumulated burnup in approximately 9.3 full power days), but to not account for burnup at all in the a priori assumptions leads to severe levels of error, approaching 25% at maximum (for LEU). The authors also discovered that V SPNDs are extraordinarily robust in both the LEU and LEU+ fuel cycles considered in this modeling work, whereas Rh SPNDs undergo significant transmutation that can result in large errors in the synthesized power distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

The coyote universe extended: Precision emulation of the matter power spectrum

Modern sky surveys are returning precision measurements of cosmological statistics such as weak lensing shear correlations, the distribution of galaxies, and cluster abundance. To fully exploit these observations, theorists must provide predictions that are at least as accurate as the measurements, as well as robust estimates of systematic errors that are inherent to the modeling process. In the nonlinear regime of structure formation, this challenge can only be overcome by developing a large-scale, multi-physics simulation capability covering a range of cosmological models and astrophysical processes. As a first step to achieving this goal, we have recently developed a prediction scheme for the matter power spectrum (a so-called emulator), accurate at the 1% level out to k ∼ 1 Mpc{sup –1} and z = 1 for wCDM cosmologies based on a set of high-accuracy N-body simulations. It is highly desirable to increase the range in both redshift and wavenumber and to extend the reach in cosmological parameter space. To make progress in this direction, while minimizing computational cost, we present a strategy that maximally reuses the original simulations. We demonstrate improvement over the original spatial dynamic range by an order of magnitude, reaching k ∼ 10 h Mpc{sup –1}, a four-fold increase in redshift coverage, to z = 4, and now include the Hubble parameter as a new independent variable. To further the range in k and z, a new set of nested simulations run at modest cost is added to the original set. The extension in h is performed by including perturbation theory results within a multi-scale procedure for building the emulator. This economical methodology still gives excellent error control, ∼5% near the edges of the domain of applicability of the emulator. A public domain code for the new emulator is released as part of the work presented in this paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Multi-Scale Modeling Framework for Mercury Biogeochemistry

Multi-Scale modeling of mercury (Hg) geochemical speciation and reactions has been performed by integrating atomistic quantum chemical calculations with continuum scale speciation models. Major progress has been made in the improvement of quantum chemical models to calculate critical thermodynamic data for Hg complexes in aquatic environments. Rapid and reliable quantum chemical approaches have been developed for calculating acid dissociation constants (pK a ) and stability constants (log K), with calculated mean unsigned errors of 0.5 and 1.5 log units, respectively for ligand molecules and Hg complexes. At the continuum scale, systematic analysis of uncertainty propagation in mercury (Hg) speciation modeling has been conducted and was used to identify environmental conditions under which thermodynamic constant uncertainties are significant and recommended to be accounted for. The integrated framework for multi-scale modeling of mercury geochemistry is open to the research community through the web-based multiscale modeling aqueous speciation resource, AQUA-MER. The improved quantum chemical approaches for thermodynamic constant calculations are accessible through AQUA-MER and can be used to provide the missing constants in the continuum scale speciation calculations. In addition to low molecular mass Hg complex speciation, modeling natural aquatic environments also involve the transport of high molecular weight dissolved organic matter (DOM) in reactive flows simultaneously with equilibrium and kinetic reactions. To this end, atomistic MD simulations were performed to capture the details of aggregation, mechanisms and distribution of functional groups in DOM at the molecular level. The elemental composition and calculated bulk properties of the DOM models are in close agreement with experimental measurements. A travel-time based reactive transport model in the hyporheic zone of stream corridors was established for the multicomponent Hg-DOM-S system and implemented through PFLOTRAN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Impacts of high resolution data on traveler compliance levels in emergency evacuation simulations

In this article, we conducted a comparison study of evacuation assignment based on Traffic Analysis Zones (TAZ) and high resolution LandScan USA Population Cells (LPC) with detailed real world roads network. A platform for evacuation modeling built on high resolution population distribution data and activity-based microscopic traffic simulation was proposed. This platform can be extended to any cities in the world. The results indicated that evacuee compliance behavior affects evacuation efficiency with traditional TAZ assignment, but it did not significantly compromise the performance with high resolution LPC assignment. The TAZ assignment also underestimated the real travel time during evacuation. This suggests that high data resolution can improve the accuracy of traffic modeling and simulation. The evacuation manager should consider more diverse assignment during emergency evacuation to avoid congestions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fuel cycle depletion validation and code-to-code verification studies for High Flux Isotope Reactor highly and low-enriched uranium fuel designs

Here, this paper documents fuel cycle depletion validation and code-to-code verification studies for the High Flux Isotope Reactor (HFIR) highly enriched uranium (HEU) and proposed low-enriched uranium (LEU) fuel designs. In support of HFIR’s world-leading performance, transport and depletion simulations are performed to ensure safe operations, design and qualify irradiation experiments, enhance core components and irradiation facilities, and design and characterize LEU fuel designs. Identifying well-validated, computationally efficient codes is required for the success of these efforts. The HFIR Controller, Shift, and VESTA codes were deployed to simulate HEU uranium–oxide dispersion fuel cycles at 85, 95, and 100 MW operations, as well as LEU fuel cycles operating at 95 MW with uranium–silicide dispersion and uranium–molybdenum monolithic alloy fuel forms. Excellent agreement between the codes and with experimentally obtained 235 U enrichment distributions provides increased confidence in the ability of these codes to model and simulate HFIR’s unique core design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Cooperative fault management for resilient integration of renewable energy

Cooperative fault management (CFM) is designed herein to control different types of renewable energy resources cooperatively during electrical faults. This paper studies systems with a high penetration of photovoltaic (PV) energy and wind energy. First, CFM leverages power converters of PV farms to boost the ride-through capability of nearby doubly-fed induction generators (DFIGs). By controlling PV farms’ output voltages to change smoothly during both fault initiation and fault clearance, the widely used crowbar in DFIGs is less likely to be activated. Crowbar activation adversely makes DFIGs lose controllability and absorb reactive power. The second contribution is the development of a software-defined CFM controller and a controller in-the-loop demonstration of the real-time performance of this optimization-based CFM. CFM capitalizes on distributed optimization formulation to enable flexibility, plug-and-play, and privacy-preserving. Computation time, however, is a major concern for optimization-based dynamics control. Here, real-time controller-in-the-loop simulation results show optimization-based CFM can output reference values around 60 ms and is quick enough for dynamic control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Sequence-Dependent Structure/Function Relationships of Catalytic Peptide-Enabled Gold Nanoparticles Generated under Ambient Synthetic Conditions

Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.

Bedford, Nicholas M.↗
DOE OSTI2013

NEW TECHNIQUES FOR HIGH-CONTRAST IMAGING WITH ADI: THE ACORNS-ADI SEEDS DATA REDUCTION PIPELINE

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to {approx}20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field of view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a Berkeley Software Distribution (BSD) license.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

FitCache: A Transparent Drop-In Framework for Multi-Tier Caching to Accelerate Distributed Deep Learning Workloads

Training in Deep learning (DL) remains highly compute- and data-intensive, with I/O becoming a critical bottleneck as models and datasets scale. Recent studies report that data loading can dominate training time, especially on large-scale HPC systems with shared parallel file systems (PFS). Existing caching approaches either rely on single-tier designs or require intrusive modifications to training pipelines, limiting their portability and effectiveness. In this work, we present FitCache, a transparent drop-in framework for multi-tier caching to accelerate distributed DL training by coordinating fast local memory (e.g., DRAM, Persistent Memory (PMem)) and NVMe as hierarchical caches atop PFS. Our design adapts to hardware diversity, i.e., if NVMe is missing, memory transparently acts as a caching tier, ensuring stable performance. FitCache transparently intercepts I/O requests and issues concurrent fetches across all tiers, returning data from the fastest responder without centralized metadata or static redirection paths. FitCache adapts to dynamic workloads and heterogeneous clusters while maintaining POSIX compatibility. Experiments on Frontier (2048 GPUs) and smaller research clusters show that FitCache reduces training time by up to 40% and per-batch I/O latency by up to 71.6% compared to Lustre Orion PFS, offering a drop-in solution for scalable DL training.

Hu, Guangxing [ORNL] (ORCID:0009000283203614)↗