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At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Monte Carlo toolkit for designing and validating step-range-filter spectrometer designs

Here, we present a Monte Carlo toolkit for validating step range filter (SRF) spectrometer designs. Geant4 is used to transport charged particles through the SRF filters to generate synthetic SRF data that include realistic CR-39 effects. Synthetic SRF spectra generated by this method inherently account for instrument response and allow for the quantification of SRF performance before shots. The usefulness of this toolkit is demonstrated through its application to a number of problems. A new broadband SRF for the ∼10 MeV wide 3He3He proton spectrum is validated, and an analysis method for analyzing 3He3He-p SRF data that accounts for instrument response is put forth. In addition, an SRF design for the compact recoil-proton spectrometer (CRS) on the Z-machine is validated. Finally, a new calibration technique for the DD-p SRF is proposed and validated.

Johnson, T. M. (ORCID:0000000193032949)↗

DOE OSTI2026

Tradeoffs in single stage drift step recovery diode generator design

A high-voltage generator of nanosecond pulses utilizing a drift step recovery diode (DSRD) as an opening switch in the compression section is designed to operate at peak voltages of 4 kV and repetition rates of 2 MHz in burst mode at reduced voltages. The generator is used to perform empirical studies on the output metrics of a DSRD generator with a focus on matching the circuit of the reverse current loop parameters and DSRD to the load. The dynamic impedance introduced by nonlinear elements in the DSRD was investigated with respect to the driving circuit by varying the number of DSRDs connected in series. Experimental results indicate that optimization of the driving circuit for the DSRD is key to minimizing losses and achieving optimal pulse compression. Furthermore, the results also indicate trends in DSRD performance as larger storage inductances and longer pumping times are used, limiting the pulse compression capabilities of a single stage DSRD generator.

Electrical properties and parameters↗

DOE OSTI2021

Mutations in artificial self-replicating tiles: A step toward Darwinian evolution

Significance In nature, mutation is the first step of evolution, where it provides the genetic variation for the natural selection to act. Here we take a system of artificial self-replicating tiles, DNA origami, that exhibit templated reproduction. We can generate a small fraction of mutations by introducing a mismatch in hybridization between parent and daughter. We can modify the origami functionality to affect the growth rate of the mutated species, giving it less or more evolutionary advantage, and to become dominant in several generations. The introduction of mutations into an artificial self-replicating system provides new directions for research into self-assembly processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Perturbative diffraction methods resolve a conformational switch that facilitates a two-step enzymatic mechanism

Enzymes catalyze biochemical reactions through precise positioning of substrates, cofactors, and amino acids to modulate the transition-state free energy. However, the role of conformational dynamics remains poorly understood due to poor experimental access. This shortcoming is evident with Escherichia coli dihydrofolate reductase (DHFR), a model system for the role of protein dynamics in catalysis, for which it is unknown how the enzyme regulates the different active site environments required to facilitate proton and hydride transfer. Here, we describe ligand-, temperature-, and electric-field-based perturbations during X-ray diffraction experiments to map the conformational dynamics of the Michaelis complex of DHFR. We resolve coupled global and local motions and find that these motions are engaged by the protonated substrate to promote efficient catalysis. This result suggests a fundamental design principle for multistep enzymes in which pre-existing dynamics enable intermediates to drive rapid electrostatic reorganization to facilitate subsequent chemical steps.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

CO2 upgrading into bioproducts using a two-step abiotic–biotic system

The valorization of CO2 to chemicals beyond C1-2 products is receiving significant interest; however, the direct electrosynthesis of Cn molecules (n > 4) remains a challenge. Here, we present a two-step abiotic-biotic system for upgrading CO2 into the biopolymer, poly(3-hydroxybutyrate). In the electrolysis system, CO2 is converted into C2 oxygenates using a Cu-Ag tandem electrocatalyst. The electrolysis process generates a liquid stream containing ~ 200 mM acetate in a bio-compatible electrolyte. This electrosynthesized acetate is then fed to a bioreactor, where the substrate is upgraded by Cupriavidus necator to biopolymer with a maximum rate of 32 ± 3.5 mg L-1 h-1. We further demonstrate the purification of the resulting biopolymer into a powder. The high productivity of the abiotic-biotic system demonstrates its feasibility for sustainable chemical manufacturing.

CO2 upgrading↗

DOE OSTI2023

Deep Learning for Multigroup Cross-Section Representation in Two-Step Core Calculations

Here we investigate using deep learning, a type of machine-learning algorithm employing multiple layers of artificial neurons, for the mathematical representation of multigroup cross sections for use in the Griffin reactor multiphysics code for two-step deterministic neutronics calculations. A three-dimensional fuel element typical of a high-temperature gas reactor as well as a two-dimensional sodium-cooled fast reactor lattice are modeled using the Serpent Monte Carlo code, and multigroup macroscopic cross sections are generated for various state parameters to produce a training data set and a separate validation data set. A fully connected, feedforward neural network is trained using the open-source PyTorch machine-learning framework, and its accuracy is compared against the standard piecewise linear interpolation model. Additionally, we provide in this work a generic technique for propagating the cross-section model errors up to the k eff using sensitivity coefficients with the first-order uncertainty propagation rule. Quantifying the eigenvalue error due to the cross-section regression errors is especially practical for appropriately selecting the mathematical representation of the cross sections. We demonstrate that the artificial neural network model produces lower errors and therefore enables better accuracy relative to the piecewise linear model when the cross sections exhibit nonlinear dependencies; especially when a coarse grid is employed, where the errors can be halved by the artificial neural network. However, for linearly dependent multigroup cross sections as found for the sodium-cooled fast reactor case, a simpler linear regression outperforms deeper networks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Free-boundary MRxMHD equilibrium calculations using the stepped-pressure equilibrium code

Here the stepped-pressure equilibrium code (SPEC) (Hudson et al 2012 Phys. Plasmas 19 , 112 502) is extended to enable free-boundary multi-region relaxed magnetohydrodynamic (MRxMHD) equilibrium calculations. The vacuum field surrounding the plasma inside an arbitrary 'computational boundary', , is computed, and the virtual-casing principle is used iteratively to compute the normal field on so that the equilibrium is consistent with an externally produced magnetic field. Recent modifications to SPEC are described, such as the use of Chebyshev polynomials to describe the radial dependence of the magnetic vector potential, and a variety of free-boundary verification calculations are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak

Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021–22 experimental campaign.

integrated data analysis↗

DOE OSTI2022

The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps

Abstract Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

Abstract The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Soni, Vivak (ORCID:0000000294969562)↗

DOE OSTI2020

High-Energy Coherent X-Ray Dffraction Microscopy of Polycrystal Grains: First Steps Towards a Multi-Scale Approach

Here, we present proof-of-concept imaging measurements of a polycrystalline material that integrate the elements of conventional high-energy x-ray diffraction microscopy with coherent diffraction-imaging techniques, and that can enable in situ strain-sensitive imaging of lattice structure in ensembles of deeply embedded crystals over five decades of length scale upon full realization. We demonstrate that the combination of these two characterization methods enables a more complete picture of the crystal-lattice strain than either method is capable of providing individually. Such complementary imaging capabilities are critical to addressing questions in a variety of research areas such as materials science and engineering, chemistry, and solid-state physics. Towards this eventual goal, the following key aspects are demonstrated: (1) high-energy Bragg coherent diffraction imaging (HE BCDI) of submicron-scale crystallites at 52 keV at current third-generation synchrotron light sources, (2) HE BCDI performed in conjunction with far-field high-energy diffraction microscopy (FF HEDM) on the grains of a polycrystalline sample in a smoothly integrated manner, and (3) the orientation information of an ensemble of grains obtained via FF HEDM used to perform complementary HE BCDI on multiple Bragg reflections of a single targeted grain. The imaged structures are seen to be 477 ± 50 nm or smaller in size, with an estimated strain resolution of 2.5 x 10 -4 . These steps lay the foundation for integration of HE BCDI, which typically provides a spatial resolution tens of nanometers, into a broad suite of well-established HEDM methods, extending HEDM beyond the few-micrometer resolution bound and into the nanoscale, and positioning the approach to take full advantage of the orders-of-magnitude improvement of x-ray coherence expected at fourth-generation light sources presently being built and commissioned worldwide.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Decoding polymer self-dynamics using a two-step approach

The self-correlation function and corresponding self-intermediate scattering function in Fourier space are important quantities for describing the molecular motions of liquids. This work draws attention to a largely overlooked issue concerning the analysis of these space-time density-density correlation functions of polymers. Here, we show that the interpretation of non-Gaussian behavior of polymers is generally complicated by intrachain averaging of distinct self-dynamics of different segments. By the very nature of the mathematics involved, the averaging process not only conceals critical dynamical information, but also contributes to the observed non-Gaussian dynamics. To fully expose this issue and provide a thorough benchmark of polymer self-dynamics, we perform analyses of coarse-grained molecular dynamics simulations of linear and ring polymer melts as well as several theoretical models using a “two-step” approach, where interchain and intrachain averagings of segmental self-dynamics are separated. While past investigations primarily focused on the average behavior, our results indicate that a more nuanced approach to polymer self-dynamics is clearly required.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Step-stool conformation of a cyclic Schiff base derived from ethylenediamine and heptane-2,4,6-trione: evidence for partial hydrolysis in metal coordination

A Schiff base derived from ethylenediamine and heptane-2,4,6-trione, namely, 5,9,14,18-tetramethyl-1,4,10,13-tetraazacyclooctadeca-5,8,14,17-tetraene-7,16- dione (C 18 H 28 N 4 O 2 ), abbreviated H 4 daaden, was prepared and characterized for the first time by single-crystal X-ray diffraction. The atoms of the Schiff base occupy two different planes and thus the molecule is essentially nonplanar. An axis running through the C–C atoms of the ethylenediamine groups separate the two planes and these two planes are connected by bridging ethylene groups showing an angle of 117.34 (8)°. As a result, the side view of the molecule shows a ‘step-stool’ conformation. The nonplanar nature of the Schiff base plays an important role in metal coordination, which leads to partial hydrolysis of the ring structure

crystal structure↗

DOE OSTI2024

Power System Resilience Considering Hurricane Storms and Generator Step-up Transformers

Power system resilience describes the system’s ability to withstand and recover quickly from unexpected power outages due to extreme events. As society’s electrification continues at a rapid rate, it is imperative electric infrastructure is designed and operated with extra security. This work studies the impact of severe hurricane storms on the power system located along the United States’ Eastern Seaboard. Specific focus is designated to steady state system operation and loss of large generation step up power transformers. A series of phasor domain power system simulations are conducted to determine the critical point at which the system loses steady state operability. Additionally, active power load shedding and generator dispatching are studied as mitigation and recovery strategies. Results indicate generator dispatching as an effective proactive mitigation strategy and load shedding as an effective recovery strategy.

Liu, Yilu↗

DOE OSTI2020

A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid

A dynamic neural network (NN) based multi-class classifier is proposed for improving online prediction of coherent generator groups (CGGs), following the occurrences of various contingencies in the power grid. This is motivated by the increasing availability of the measurements from phasor measurement units (PMUs) and the number of grouping schemes is limited. The proposed method consists of three steps. First, by performing offline simulations, a library of system dynamic responses characterized by post-contingency rotor angles and speeds of individual generators is obtained. To generate sufficient data, up to N-2 contingencies and the uncertain parameters associated with the power grid including type and location of disturbance and fault clearing times are modeled. Secondly, the training data-set is produced by generating labels for individual contingencies using a hierarchical clustering method based on rotor angle and speed data. Lastly, the dynamic NN models are trained for online applications such as emergency controls and controlled islanding. The proposed method is tested on the standard 16-generator 68-bus system to demonstrate its performance. Furthermore, the impact of the sample data lengths on the CGG numbers is evaluated. It is interesting to observe that the time domain stability behaviors can be determined by examining the changes in the CGG numbers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Modeling Grid Data Flows for Transmission and Distribution Operations: Review, Design, Next Steps

Operational scenarios of the power grids grow multifold to accommodate the diverse needs of both the utilities and end consumers, and the various other stakeholders in-between. To comprehensively model and apply analytics to support objectives and business functions of grid sectors, a reliable approach to characterize and design data flows is crucial. The flows bridge business functions with communications protocols, stakeholders such as the grid actors, and data interfaces comprising different data objects. Additionally, constraints applied to the flow such as cybersecurity, trust, privacy, and ownership among others intersect these entities, requiring the delineation of their interactions under different scenarios. This paper aims to not only highlight relevant research in the space of grid data flows, but also proposes, for the transmission-distribution sector, a novel modeling approach that marries the aforementioned entities: objectives, business functions, data interfaces, communication protocols, data stakeholders, and flow constraints. It elaborates on the design philosophy and the significance of each entity within the model and applies it to an example function of fault location, isolation and service restoration (FLISR). Finally, the next steps to extend the application of this data flow model for other practical operational scenarios are discussed.

Sundararajan, Aditya [ORNL] (ORCID:000000033577854↗

DOE OSTI2022

Multilevel Switched-Capacitor AC–DC Step-Down Rectifier for Wireless Charging With Reduced Conduction Loss and Harmonic Content

In this paper, a wireless charging architecture employing a multilevel switched-capacitor (MSC) AC-DC rectifier is investigated. The proposed MSC rectifier features a multilevel design which is scalable to accommodate different power ratings and load ranges. The topology showcases advantages for wireless power transfer (WPT) systems in terms of compactness, efficiency, impedance tunability, and harmonic attenuation. The single-stage active topology is capable of varying its low-distortion staircase input voltage to tune the wireless power transfer system for high system-wide efficiency. A 7-level, 20 W prototype is used to verify the WPT loading and loss analysis. The prototype operates at 150 kHz with up to 3:1 step-down conversion ratio to an output voltage of 5.0 V. The experimental peak DC-to-DC efficiency is 93.8% and the rectifier peak efficiency is 98.3%. As a result. the rectifier demonstrates low waveform distortion and high efficiency across many WPT loading conditions, solidifying its place as a strong candidate for wireless power applications.

42 ENGINEERING↗

DOE OSTI2021

CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers—both of which are superior to previous approaches in precision and recall in three different test scenarios—and the first signedness classification algorithm, which exhibits a $>$ 97% F-score. Altogether, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average $\ell ^1$ error is five times better (81% less) than all previous methods and exhibits lower average error, even when considering only signals that meet prior methods’ assumptions. Finally, CAN-D is implemented in lightweight hardware, allowing for an on-board diagnostic (OBD-II) plugin for real-time in-vehicle CAN decoding.

42 ENGINEERING↗