Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “System dependency”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-Time Dyson Expansion

Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on subfemtosecond time scales. The interpretation of experimental results is often enhanced by parallel numerical studies, which can provide insight and validation for experimental hypotheses. However, developing a theoretical framework for simulating time-resolved spectra remains a significant challenge. The most suitable approach involves the many-body nonequilibrium Green's function formalism, which accounts for crucial dynamical many-body correlations during time evolution. While these dynamical correlations are essential for observing emergent behavior in time-resolved spectra, they also render the formalism prohibitively expensive for large-scale simulations. Substantial effort has been devoted to reducing this computational cost─through approximations and numerical techniques─while preserving the key dynamical correlations. The ultimate goal is to enable first-principles simulations of time-dependent systems ranging from small molecules to large, periodic, multidimensional solids. Here, in this perspective, we outline key challenges in developing practical simulations for time-resolved spectroscopy, with a particular focus on Green's function methodologies. We highlight a recent advancement toward a scalable framework: the real-time Dyson expansion (RT-DE) [Phys. Rev. Lett. 2024, 133, 226902]. We introduce the theoretical foundation of RT-DE and discuss strategies for improving scalability, which have already enabled simulations of system sizes beyond the reach of previous fully dynamical approaches. We conclude with an outlook on future directions for extending RT-DE to first-principles studies of dynamically correlated, nonequilibrium systems.

Reeves, Cian C. [Univ. of California, Santa Barbar↗
DOE OSTI2026

Long-Range Fit: A Software Package for the Representation and Study of Long-Range Molecular Interactions

Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions─with electrostatic, induction, and dispersion as the main components─play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via a multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed long-range-fit (LRF), an interactive and user-friendly software package designed to automate the generation and fitting of long-range interaction terms for arbitrary molecules in nondegenerate (ground or excited) electronic states. We have derived and implemented all terms up to 15th order, without approximations, via a spherical tensor representation, with symmetry adaptation to all molecular point-group symmetries. The resulting potential energy surface is compatible with most representations of the close interaction region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Structural basis for C-degron selectivity across KLHDCX family E3 ubiquitin ligases

Abstract Specificity of the ubiquitin-proteasome system depends on E3 ligase-substrate interactions. Many such pairings depend on E3 ligases binding to peptide-like sequences - termed N- or C-degrons - at the termini of substrates. However, our knowledge of structural features distinguishing closely related C-degron substrate-E3 pairings is limited. Here, by systematically comparing ubiquitylation activities towards a suite of common model substrates, and defining interactions by biochemistry, crystallography, and cryo-EM, we reveal principles of C-degron recognition across the KLHDCX family of Cullin-RING ligases (CRLs). First, a motif common across these E3 ligases anchors a substrate’s C-terminus. However, distinct locations of this C-terminus anchor motif in different blades of the KLHDC2, KLHDC3, and KLHDC10 β-propellers establishes distinct relative positioning and molecular environments for substrate C-termini. Second, our structural data show KLHDC3 has a pre-formed pocket establishing preference for an Arg or Gln preceding a C-terminal Gly, whereas conformational malleability contributes to KLHDC10’s recognition of varying features adjacent to substrate C-termini. Finally, additional non-consensus interactions, mediated by C-degron binding grooves and/or by distal propeller surfaces and substrate globular domains, can substantially impact substrate binding and ubiquitylatability. Overall, the data reveal combinatorial mechanisms determining specificity and plasticity of substrate recognition by KLDCX-family C-degron E3 ligases.

Science & Technology - Other Topics↗
DOE OSTI2021

Surface characterization and methane activation on SnO x /Cu 2 O/Cu(111) inverse oxide/metal catalysts

To activate methane at low or medium temperatures is a difficult task and a pre-requisite for the conversion of this light alkane into high value chemicals. In this work, we report the preparation and characterizations of novel SnO x /Cu 2 O/Cu(111) interfaces that enable low-temperature methane activation. Scanning tunneling microscopy identified small, well-dispersed SnO x nanoclusters on the Cu 2 O/Cu(111) substrate with an average size of 8 Å, and such morphology was sustained up to 450 K in UHV annealing. Ambient pressure X-ray photoelectron spectroscopy showed that hydrocarbon species (CH x groups), the product of methane activation, were formed on SnO x /Cu 2 O/Cu(111) at a temperature as low as 300 K. An essential role of the SnO x –Cu 2 O interface was evinced by the SnO x coverage dependence. Systems with a small amount of tin oxide, 0.1–0.2 ML coverage, produced the highest concentration of adsorbed CH x groups. Calculations based on density functional theory showed a drastic reduction in the activation barrier for C–H bond cleavage when going from Cu 2 O/Cu(111) to SnO x /Cu 2 O/Cu(111). On the supported SnO x , the dissociation of methane was highly exothermic (ΔE ~ –35 kcal mol –1 ) and the calculated barrier for activation (~20 kcal mol –1 ) could be overcome at 300–500 K, target temperatures for the conversion of methane to high value chemicals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Exact-two-component block-localized wave function: A simple scheme for the automatic computation of relativistic ΔSCF

Block-localized wave function is a useful method for optimizing constrained determinants. In this article, we extend the generalized block-localized wave function technique to relativistic two-component framework. Optimization of excited state determinants for two-component wave functions presents a unique challenge because the excited state manifold is often quite dense with degenerate states. Furthermore, we test the degree to which certain symmetries result naturally from the ΔSCF optimization such as time reversal symmetry and symmetry with respect to the total angular momentum operator on a series of atomic systems. Variational optimizations may often break symmetry in order to lower the overall energy, just as unrestricted Hartree-Fock breaks spin symmetry. Overall, we demonstrate that time reversal symmetry is roughly maintained when using Hartree-Fock, but less so when using Kohn-Sham density functional theory. Additionally, maintaining total angular momentum symmetry appears to be system dependent and not guaranteed. Finally, we were able to trace the breaking of total angular momentum symmetry to the relaxation of core electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Thomson scattering on the large plasma device

We have developed a non-collective Thomson scattering diagnostic for measurements of electron density and temperature on the Large Plasma Device. A triple grating spectrometer with a tunable notch filter is used to discriminate the faint scattering signal from the stray light. In this paper, we describe the diagnostic and its calibration via Raman scattering and present the first measurements performed with the fully commissioned system. Depending on the discharge conditions, the measured densities and temperatures range from 4.0 × 10 12 to 2.8 × 10 13 cm -3 and from 1.2 to 6.8 eV, respectively. The variation of the measurement error with plasma parameters and discharges averaged is also discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task

ABSTRACT Motivation Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model’s performance. Results We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets—BioASQ-7b, BioASQ-8b and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability and implementation BioADAPT-MRC is freely available as an open-source project at https://github.com/mmahbub/BioADAPT-MRC. Supplementary information Supplementary data are available at Bioinformatics online.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The impact of light polarization effects on weak lensing systematics

ABSTRACT A fraction of the light observed from edge-on disc galaxies is polarized due to two physical effects: selective extinction by dust grains aligned with the magnetic field and scattering of the anisotropic starlight field. Since the reflection and transmission coefficients of the reflecting and refracting surfaces in an optical system depend on the polarization of incoming rays, this optical polarization produces both (a) a selection bias in favour of galaxies with specific orientations and (b) a polarization-dependent point spread function (PSF). In this work, we build toy models to obtain for the first time an estimate for the impact of polarization on PSF shapes and the impact of the selection bias due to the polarization effect on the measurement of the ellipticity used in shear measurements. In particular, we are interested in determining if this effect will be significant for Wide-Field Infrared Survey Telescope (WFIRST). We show that the systematic uncertainties in the ellipticity components are 8 × 10−5 and 1.1 × 10−4 due to the selection bias and PSF errors respectively. Compared to the overall requirements on knowledge of the WFIRST PSF ellipticity (4.7 × 10−4 per component), both of these systematic uncertainties are sufficiently close to the WFIRST tolerance level that more detailed studies of the polarization effects or more stringent requirements on polarization-sensitive instrumentation for WFIRST are required.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Structural and biochemical insights into NEIL2’s preference for abasic sites

Cellular DNA is subject to damage from a multitude of sources and repair or bypass of sites of damage utilize an array of context or cell cycle dependent systems. The recognition and removal of oxidatively damaged bases is the task of DNA glycosylases from the base excision repair pathway utilizing two structural families that excise base lesions in a wide range of DNA contexts including duplex, single-stranded and bubble structures arising during transcription. The mammalian NEIL2 glycosylase of the Fpg/Nei family excises lesions from each of these DNA contexts favoring the latter two with a preference for oxidized cytosine products and abasic sites. We have determined the first liganded crystal structure of mammalian NEIL2 in complex with an abasic site analog containing DNA duplex at 2.08 Å resolution. Comparison to the unliganded structure revealed a large interdomain conformational shift upon binding the DNA substrate accompanied by local conformational changes in the C-terminal domain zinc finger and N-terminal domain void-filling loop necessary to position the enzyme on the DNA. The detailed biochemical analysis of NEIL2 with an array of oxidized base lesions indicates a significant preference for its lyase activity likely to be paramount when interpreting the biological consequences of variants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Analysis of intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites

The evolution of recognition specificities by the immune system depends on the generation of receptor diversity and on connecting the binding of new antigens with the initiation of downstream signaling. In plant immunity, the innate Nucleotide-Binding Leucine-Rich Repeat (NLR) receptor family enables antigen binding and immune signaling. In this study, we surveyed the NLR complements of 62 ecotypes of Arabidopsis thaliana and 54 lines of Brachypodium distachyon and identified a limited number of NLR subfamilies that show high allelic diversity. We show that the predicted specificity-determining residues cluster on the surfaces of Leucine-Rich Repeat domains, but the locations of the clusters vary among NLR subfamilies. By comparing NLR phylogeny, allelic diversity, and known functions of the Arabidopsis NLRs, we formulate a hypothesis for the emergence of direct and indirect pathogen-sensing receptors and of the autoimmune NLRs. These findings reveal the recurring patterns of evolution of innate immunity and can inform NLR engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scaling approach to nuclear structure in high-energy heavy-ion collisions

In high-energy heavy-ion collisions, the initial condition of the produced quark-gluon plasma (QGP) and its evolution are sensitive to collective nuclear structure parameters describing the shape and radial profiles of the nuclei. Here, we find a general scaling relation between these parameters and many experimental observables such as elliptic flow, triangular flow, and particle multiplicity distribution. In particular, the ratios of observables between two isobar systems depend only on the differences of these parameters, but not on the details of the final state interactions, hence offering a new way to constrain the QGP initial condition. Using this scaling relation, we show how the structure parameters of $^{96}_{44}$Ru and $^{96}_{40}$Zr conspire to produce the rich centrality dependences of these ratios, as measured by the STAR Collaboration. Our scaling approach demonstrates that isobar collisions are a precision tool to probe the initial condition of heavy-ion collisions, as well as the collective nuclear structures, including the neutron skin, of the atomic nuclei across energy scales.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Self-Generated Plasma Rotation in a Z-Pinch Implosion with Preembedded Axial Magnetic Field

Using detailed spectroscopic measurements, highly resolved in both time and space, a self-generated plasma rotation is demonstrated using a cylindrical implosion with a preembedded axial magnetic field (B z0 ). The rotation direction is found to depend on the direction of B z0 and its velocity is found comparable to the peak implosion velocity, considerably affecting the force and energy balance throughout the implosion. Moreover, the evolution of the rotation is consistent with magnetic flux surface isorotation, a novel observation in a Z pinch, which is a prototypical time dependent system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Incorporating FLEX Strategies in Multi-Unit Probabilistic Risk Assessment

The catastrophic Fukushima nuclear accident reminded nuclear community about the possibilities of extreme accident scenarios, including those involving multiple reactor units on the same site. As the responses to the Fukushima accident, the nuclear power industry developed and implemented a series of strategies, including the Diverse and Flexible Coping Strategies (FLEX), to enhance the coping capacities of nuclear power plants (NPPs) under extreme accidents. This study intends to examine the impact of FLEX strategies on the risk from all the reactor units on the same NPP site, including risks from accidents involving a single unit and from accidents involving multiple units. The fundamental, methodological element of this study is Multi-Unit Probabilistic Risk Assessment (MUPRA), which requires a shift in Probabilistic Risk Assessment (PRA) from a “one-reactor-at-a-time” mindset to a “considering all reactors sharing a site as a unity” one. An integrated modeling approach for multi-unit event sequence development will be leveraged to develop MUPRA model and address the intra-unit and inter-unit dependencies. Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE), a PRA software developed and maintained by Idaho National Laboratory (INL) for the U.S. Nuclear Regulatory Commission (NRC), will serve as the platform for MUPRA modeling. This study selects Loss Of Offsite Power (LOOP) as a representative initiating event, which may occur on a generic two-unit NPP site and impacts both reactor units on the site. First, an MUPRA model, including multi-unit event trees, will be developed to obtain single-unit and multi-unit accident scenarios. Next, different FLEX strategies will be assumed, including FLEX equipment for a unit cannot be used for another unit, FLEX equipment for multiple units can be used in a cross-connected manner, etc. Lastly, the effectiveness of each postulated FLEX strategy will be evaluated by incorporating the corresponding FLEX equipment and deployment logic in the MUPRA model.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Materials challenges and opportunities for quantum computing hardware

Combatting noise on the platform The potential of quantum computers to solve problems that are intractable for classical computers has driven advances in hardware fabrication. In practice, the main challenge in realizing quantum computers is that general, many-particle quantum states are highly sensitive to noise, which inevitably causes errors in quantum algorithms. Some noise sources are inherent to the current materials platforms. de Leon et al. review some of the materials challenges for five platforms for quantum computers and propose directions for their solution. Science , this issue p. eabb2823

Science & Technology - Other Topics↗
DOE OSTI2024

GP-BayesOpInf

SAND2025-01851O GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2026

Software Control Program For Transportable Microgrid State-of-charge Balancing And Frequency Stability Controls

A deterministic state-of-charge (SOC) balancing approach software control code is introduced as an integral secondary management to primary control layer of an islanded small microgrid or nanogrid system made up of multiple grid-forming inverter/battery/solar combination systems, where each set of batteries with each inverter are on independent DC buses (i.e. non-paralleled on the DC sides). A DERMS-level control approach, algorithm and automation controller program was developed to improve coordination and enable microgrid asset compliance and SOC balancing, enabling provision of a system-level power stability support architecture, load support, and asset scalability. The architecture is configured to treat each unit or micro/nano-grid as a node in a microgrid network, allowing for autonomous DERMS control regarding load and SOC balancing and power stability. As the network grows with the addition of units, greater coordination efforts may be required. The ideal small network microgrid ranges from 2-10 inverter/battery units before additional control parameters must be considered in the existing architecture. The control approach focuses on a deterministic state-of-charge analysis as the primary level control process followed by a secondary control loop using a forced frequency-watt droop strategy to conform off-the-shelf components into behaving under a leader-follower configuration. Adopting this control scheme has been shown to allow for a balanced, unit-coordinated microgrid network, enabling stable power flow. The deterministic state-of-charge approach is introduced as an integral primary control layer of an islanded small network microgrid. A standard strategy for SOC balancing is implementing a battery management system (BMS) to control SOC on the DC side. An alternative approach is to determine how to coordinate sending and receiving power on the AC side with multiple units. The latter approach assesses all the integrated units in the microgrid network. Once the individual units are identified, further system data is required to calculate each unit's total kWh, provided information about its capability to supply or consume kWh and availability. The secondary control layer in the multi-layered small network microgrid methodology uses the primary layer’s decision to initiate frequency setpoint changes, initializing the SOC balancing. The secondary control layer considers numerous system-dependent variables to enable a charging and discharging profile based on adjustable frequency setpoints. The combined architecture will result in stable, coordinated power flow enhancing an AC microgrid's functionalities.

Myers, KurtS [Idaho National Laboratory (INL), Ida↗
DOE OSTI2022

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗
DOE OSTI2021

Expansion of Machine-Learning Method for Classifying Neutron Resonances

The understanding of astrophysics processes and the performance of nuclear reactors and other nuclear systems depend on a precise description of the neutron interaction cross sections for materials and nuclei present in these environments. At low neutron energies, these cross sections exhibit resonance structure represented by sharp enhancements when the neutron energy is sufficiently close to excited levels in a compound nucleus. Such resonances can be characterized by their quantum numbers relative to angular momenta, which are often deduced in an ad hoc and irreproducible manner from the shape of the cross sections. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. To address this we have developed a machine-learning method to automate the identification and correction of these spin assignments. The algorithm is trained from simulated data, generated from statistical properties of resonance data for a given nucleus, to mimic the errors found in real data. In this project we describe five independent approaches to further develop and expand the applicability of the machine-learning spin classifier: i) Feature impact; ii) Integration with the Atlas; iii) Training optimization; iv) Spacings systematics; and v) Validation with polarized data. The premises, methods, results, and future perspectives are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗