Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data dependencies”

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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A description of structured waves in shock compressed particulate composites

Dynamic compression of composite materials is of scientific interest because the mechanical mismatch between internal phases challenges continuum theories. Typical assumptions about steady wave propagation and quasi-instantaneous state changes require reexamination along with the need for time-dependent models. To that end, data and models are presented here for the shock compression of an idealized particulate composite. To serve as a generic representative of this material class, a polymer matrix was filled with tungsten particles, ranging from 1 to 50 vol. %. This creates a simple microstructure containing randomly scattered particles with an extreme impedance mismatch to the binding matrix. These materials were parallel plate impact loaded by Al flyers traveling at 1.8–5.0 km/s. Velocimetry provided records of the equilibrium state and the compression wave structure for each case with trends quantified by an empirical fit. The same quantities were also studied as a function of the wave's propagation distance. A homogenized viscoelastic model then made it possible to progress from cataloging trends to making predictions. Starting from a Mie–Gruneisen equation of state, additional time varying terms were added to capture the transient response. After calibration, accurate predictions of the steady wave structure were possible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

The Scattered Light Time-history Diagnostic suite at the National Ignition Facility

The Scattered Light Time-history Diagnostic (SLTD) is being implemented at the National Ignition Facility (NIF) to greatly expand the angular coverage of absolute scattered-light measurements for direct- and indirect-drive inertial confinement fusion (ICF) experiments. The SLTD array will ultimately consist of 15 units mounted at a variety of polar and azimuthal angles on the NIF target chamber, complementing the existing NIF backscatter suite. Each SLTD unit collects and diffuses scattered light onto a set of three optical fibers, which transport the light to filtered photodiodes to measure scattered light in different wavelength bands: stimulated Brillouin scattering (350 nm–352 nm), stimulated Raman scattering (430 nm–760 nm), and ω/2 (695 nm–745 nm). SLTD measures scattered light with a time resolution of ~1 ns and a signal-to-noise ratio of up to 500. Currently, six units are operational and recording data. Measurements of the angular dependence of scattered light will strongly constrain models of laser energy coupling in ICF experiments and allow for a more robust inference of the total laser energy coupled to implosions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Direct fabrication and characterization of vertically stacked Graphene/h-BN/Graphene tunnel junctions

We have used a lithography free technique for the direct fabrication of vertically stacked two-dimensional (2D) material-based tunnel junctions and characterized by Raman, AFM, XPS. We fabricated Graphene/h-BN/Graphene devices by direct deposition of graphene (bottom layer), h-BN (insulating barrier) and graphene (top layer) sequentially using a plasma enhanced chemical vapor deposition on Si/SiO 2 substrates. The thickness of the h-BN insulating layer was varied by tuning the plasma power and the deposition time. Samples were characterized by Raman, AFM, and XPS. The I-V data follows the barrier thickness dependent quantum tunneling behavior for equally doped graphene layers. The resonant tunneling behavior was observed at room temperature for oppositely doped graphene layers where hydrazine and ammonia were used for n-doping of one of the graphene layers. The resonance with negative differential conductance occurs when the band structures of the two electrodes are aligned. The doping effect of the resonant peak is observed for varying doping levels. The results are explained according to the Bardeen tunneling model.

2D-materials↗
DOE OSTI2026

Recurrent convolutional neural networks for modeling nonadiabatic dynamics of quantum-classical systems

Recurrent neural networks (RNNs) have recently been extensively applied to model the time evolution in fluid dynamics, weather predictions, and even chaotic systems due to their ability to capture temporal dependencies and sequential patterns in data. Here we present an RNN model based on convolutional neural networks for modeling the nonlinear nonadiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The Physics-Aware Recurrent Convolution (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. Here, we apply our RNN approach to learn the space-time evolution of a one-dimensional semiclassical Holstein model after an interaction quench. For shallow quenches (small changes in electron-lattice coupling), the deterministic dynamics can be accurately captured using a single-CNN-based recurrent network. In contrast, deep quenches induce chaotic evolution, making long-term trajectory prediction significantly more challenging. Nonetheless, we demonstrate that the PARC-CNN architecture can effectively learn the statistical climate of the Holstein model under deep-quench conditions.

Holstein model↗
DOE OSTI2022

Anomalous symmetry breaking in the Weyl semimetal CeAlGe

CeAlGe, a proposed type-II Weyl semimetal, orders antiferromagnetically below 5 K. At 2 K, spin-flop and spin-flip transitions to less than 1 μ B /Ce are observed in the M ⁡(H) data below 30 kOe, H∥a and b, and 4.3 kOe, H∥ $\langle$110$\rangle$, respectively, indicating a fourfold symmetry of the M ⁡(H) data along the principal directions in the tetragonal ab plane with $\langle$110$\rangle$ set of easy directions. However, anomalously robust and complex twofold symmetry is observed in the angular dependence of resistivity and magnetic torque data in the magnetically ordered state once the field is swept in the ab plane. This twofold symmetry is independent of temperature and field hystereses and suggests a magnetic phase transition that separates two different magnetic structures in the ab plane. Further, the boundary of this magnetic phase transition and possibly the type of low-field magnetic structure can be tuned by an Al deficiency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

First-order structural phase transition at low temperature in GaPt 5 P and its rapid enhancement with pressure

Single crystals of X Pt 5 ⁢P (X = Al, Ga, and In), belonging to the 1-5-1 family of compounds, were grown from a Pt-P solution at high temperatures, and measurements of the ambient pressure, temperature-dependent magnetization, resistivity, and x-ray diffraction were made. Additionally, the ambient-pressure Hall resistivity and temperature-dependent resistance under pressure were measured on GaPt 5 ⁢P. All three compounds have a tetragonal P4/mmm crystal structure at room temperature with metallic transport and weak diamagnetism over the 2–300 K temperature range. Surprisingly, at ambient pressure, both the transport and magnetization measurements on GaPt 5 ⁢P show a steplike feature in the 70–90 K region, suggesting a possible structural phase transition. Neither AlPt 5 ⁢P nor InPt 5 ⁢P have any signatures of a phase transition in their temperature-dependent electrical resistance and magnetization data. Both the hysteretic nature and sharpness of the features in the GaPt 5 ⁢P data suggest that the transition is first-order. Further, single-crystal x-ray diffraction measurements provided further details of the structural transition with a possibility of a crystal symmetry different from P⁢4/mmm below the transition temperature. The transition is characterized by anisotropic changes in the lattice parameters and a volume collapse with respect to the high-temperature tetragonal crystal structure. Furthermore, satellite peaks are observed at two distinct and nonequivalent wave vectors (0, 0, 0.5) and (0.5, 0.5, 0.5), and density functional theory calculations present phonon softening, especially at (0.5, 0.5, 0.5), as a possible driving mechanism. Additionally, we find that the structural transition temperature increases rapidly with increasing pressure, reaching room temperature by ~2.2 GPa, highlighting the high degree of pressure sensitivity of GaPt 5 ⁢P and fragile nature of its room-temperature structure. Even though the volume collapse and extreme pressure sensitivity suggest chemical pressure should drive a similar structural change in AlPt 5⁢ P, where both unit-cell dimensions and volume are smaller, its structure is found to be the same as that of the room-temperature GaPt 5 ⁢P. Overall, GaPt 5 ⁢P stands out as a sole member of the 1-5-1 family of compounds for which a temperature-driven structural change has been observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

HIJING can describe the anisotropy-scaled charge-dependent correlations at the BNL Relativistic Heavy Ion Collider

The experimentally measured charge-dependent correlations in heavy-ion collisions have been suggested as a signature of the chiral magnetic effect (CME). Early model studies could not reproduce the measurement. For example, the HIJING model yielded far smaller magnitude for the charge-dependent correlation than observed in data. This led to the conclusion that the CME had to be invoked to explain the observed correlations in heavy-ion collisions. In this paper we show that this conclusion of the CME interpretation is premature. We show that the reason that HIJING predicts a far smaller correlation than data is because the elliptic anisotropy $(v_ 2)$ parameter in HIJING is too small. When properly scaled, the HIJING model can reproduce in entirety the measured correlations. Further, we also employ the AMPT model, which has a large enough $v_2$, to demonstrate that the measured data can be easily accommodated by models without invoking the CME.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Proton form factor ratio μ p G E p / G M p from double spin asymmetry

We report the ratio of the electric and magnetic form factor of the proton, $\mu_p G_E^p/G_M^p$, has been measured for elastic electron-proton scattering with polarized beam and target up to four-momentum transfer squared, $Q^2=5.66$ (GeV/c)$^2$ using the double spin asymmetry for target spin orientation aligned nearly perpendicular to the beam momentum direction. This measurement of $\mu_p G_E^p/G_M^p$ agrees with the $Q^2$ dependence of previous recoil polarization data and reconfirms the discrepancy at high $Q^2$ between the Rosenbluth and the polarization-transfer method with a different measurement technique and systematic uncertainties uncorrelated to those of the recoil-polarization measurements. The form factor ratio at $Q^2$=2.06 (GeV/c)$^2$ has been measured as $\mu_p G_E^p/G_M^p = 0.720 \pm 0.176_{stat} \pm 0.039_{sys}$, which is in agreement with an earlier measurement with the polarized target technique at similar kinematics. The form factor ratio at $Q^2$=5.66 (GeV/c)$^2$ has been determined as $\mu_p G_E^p/G_M^p=0.244\pm0.353_{stat}\pm0.013_{sys}$, which represents the highest $Q^2$ reach with the double spin asymmetry with polarized target to date.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Global study of separable pairing interaction in covariant density functional theory

A systematic global investigation of pairing properties based on all available experimental data on pairing indicators has been performed for the first time in the framework of covariant density functional theory. It is based on the separable pairing interaction. The optimization of the scaling factors of this interaction to experimental data clearly reveals its isospin dependence in the neutron subsystem. However, the situation is less certain in the proton subsystem since similar accuracy of the description of pairing indicators can be achieved both with isospin-dependent and mass-dependent scaling factors. The differences in the functional dependencies of scaling factors lead to uncertainties in the prediction of proton and neutron pairing properties which are especially pronounced at high isospin and could have a significant impact on some physical observables. For a given part of the nuclear chart the scaling factors for spherical nuclei are smaller than those for deformed ones; this feature exists also in nonrelativistic density functional theories. Furthermore, its origin is traced back to particle-vibration coupling in odd-A nuclei which is missing in all existing global studies of pairing. Although the present investigation is based on the NL5(E) covariant energy density functional (CEDF), its general conclusions are expected to be valid also for other CEDFs built at the Hartree level.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Gravimeter Search for Compact Dark Matter Objects Moving in the Earth

Dark matter could be composed of compact dark objects (CDOs). These objects may interact very weakly with normal matter and could move freely inside the Earth. A CDO moving in the inner core of the Earth will have an orbital period near 55 min and produce a time-dependent signal in a gravimeter. Data from superconducting gravimeters rule out such objects moving inside the Earth unless their mass m D and or orbital radius a are very small so that m D a < 1.2 × 10 - 13 M ⊕ R ⊕ . Here, M ⊕ and R ⊕ are the mass and radius of the Earth, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Enhanced Deuteron Coalescence Probability in Jets

The transverse-momentum ($p_T$) spectra and coalescence parameters $B_2$ of (anti)deuterons are measured in p-p collisions at $\sqrt{s}=13$ $\text{TeV}$ for the first time in and out of jets. In this measurement, the direction of the leading particle with the highest $p_T$ in the event ($p^{lead}_T > 5 GeV/c$) is used as an approximation for the jet axis. The event is consequently divided into three azimuthal regions, and the jet signal is obtained as the difference between the toward region, that contains jet fragmentation products in addition to the underlying event (UE), and the transverse region, which is dominated by the UE. The coalescence parameter in the jet is found to be approximately a factor of 10 larger than that in the underlying event. This experimental observation is consistent with the coalescence picture and can be attributed to the smaller average phase-space distance between nucleons in the jet cone as compared with the underlying event. The results presented in this Letter are compared to predictions from a simple nucleon coalescence model, where the phase-space distributions of nucleons are generated using pythia8 with the Monash 2013 tuning, and to predictions from a deuteron production model based on ordinary nuclear reactions with parametrized energy-dependent cross sections tuned on data. The latter model is implemented in pythia8.3. Both models reproduce the observed large difference between in-jet and out-of-jet coalescence parameters, although the almost flat trend of the $B^{Jet}_2$ is not reproduced by the models, which instead give a decreasing trend.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training

Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the largest configuration does not necessarily yield the best performance. Horizontal scaling introduces additional communication overhead, while vertical scaling is constrained by computation cost and device memory limits. Thus, simply increasing the batch-size leads to diminishing returns: training time and cost decrease initially but eventually plateaus, creating a knee-point in the time/cost vs. batch-size pareto curve. The optimal batch-size therefore depends on the underlying model, data and available compute resources. Large batches also suffer from worse model quality due to the well-known “generalization gap”. In this paper, we present Tula, an online service that automatically optimizes time, cost, and convergence quality for large-batch training of convolutional models. It combines parallel-systems modeling with statistical performance prediction to identify the optimal batchsize. Tula predicts training time and cost within 7.5−14% error across multiple models, and achieves up to 20× overall speedup and improves test accuracy by ≈9% on average over standard large-batch training on various vision tasks, thus successfully mitigating the generalization gap and accelerating training at the same time.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2022

Accelerating Flash-X Simulations with Asynchronous I/O

Most high-fidelity physics simulation codes, such as Flash-X, need to save intermediate results (checkpoint files) to restart or gain insights into the evolution of the simulation. These simulation codes save such intermediate files synchronously, where computation is stalled while the data is written to storage. Depending on the problem size and computational requirements, this file write time can be a substantial portion of the total simulation time. In order to hide the I/O latency of checkpointing, asynchronous I/O methods have been introduced. These methods use background threads for performing I/O while the main threads continue with the simulation. The usage of background threads can compete for resources on the node as well as with communication. In this paper, we evaluate the overheads and the overall benefit of asynchronous I/O in HDF5 to simulations. Results from real-world high-fidelity simulations on the Summit supercomputer show that I/O operation is overlapped with application communication or computation or both, effectively hiding some or all of the I/O latency. Our evaluation shows that while using asynchronous I/O adds overhead to the application, the I/O time reduction is more significant, resulting in overall up to 1.5X performance speedup.

Jain, Rajeev↗
DOE OSTI2023

Benchmarking the PCMCI Causal Discovery Algorithm for Spatiotemporal Systems

Causal discovery algorithms construct hypothesized causal graphs that depict causal dependencies among variables in observational data. While powerful, the accuracy of these algorithms is highly sensitive to the underlying dynamics of the system in ways that have not been fully characterized in the literature. In this report, we benchmark the PCMCI causal discovery algorithm in its application to gridded spatiotemporal systems. Effectively computing grid-level causal graphs on large grids will enable analysis of the causal impacts of transient and mobile spatial phenomena in large systems, such as the Earth’s climate. We evaluate the performance of PCMCI with a set of structural causal models, using simulated spatial vector autoregressive processes in one- and two-dimensions. We develop computational and analytical tools for characterizing these processes and their associated causal graphs. Our findings suggest that direct application of PCMCI is not suitable for the analysis of dynamical spatiotemporal gridded systems, such as climatological data, without significant preprocessing and downscaling of the data. PCMCI requires unrealistic sample sizes to achieve acceptable performance on even modestly sized problems and suffers from a notable curse of dimensionality. This work suggests that, even under generous structural assumptions, significant additional algorithmic improvements are needed before causal discovery algorithms can be reliably applied to grid-level outputs of earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Integration of Online Cross-Section Generation Capability with Depletion and Transient Solvers in Griffin

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE)-based reactor multiphysics analysis application jointly developed by Argonne and Idaho National Laboratories under the DOENE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. In FY25, an online crosssection generation capability based on the Self-Shielding Application Programming Interface (SSAPI) was demonstrated for TRISO-fueled reactor problems under steady-state conditions. This fiscal year, that capability was extended to support depletion and transient multiphysics calculations, enabling high-fidelity analyses that generate self-shielded cross sections on the fly from the actual evolving composition and temperature states rather than from pre-tabulated libraries. For depletion, a two-way coupling was established in which SSAPI computes compact-averaged self-shielded cross sections that the depletion solver then uses to advance the Bateman equations, with the updated compositions returned to SSAPI at each step; the depletion module was refactored to support both library-based and SSAPI-based cross sections, and additional logic was added to track daughter isotopes and to exclude minor isotopes for efficiency. For transient analysis, the SSAPI multigroup library was extended with the kinetics data required for time-dependent calculations, the Improved Quasi-Static (IQS) scheme was coupled with SSAPI, and several supporting capabilities were implemented, including a self-shielding treatment that lets control rods and drums move within a self-shielded model, which had previously been impossible and had ruled out rod- and drum-movement transients with on-the-fly cross sections altogether, a new mixing scheme for delayed-neutron precursor decay constants, a checkpoint-based restart workflow, and performance improvements such as pointwise cross-section interpolation and the bypassing of unnecessary Dancoff factor calculations. The implemented capabilities were verified against Serpent Monte Carlo solutions. For depletion, a prismatic pin-cell problem based on a Next Generation Nuclear Plant (NGNP) Very High Temperature Reactor benchmark showed excellent agreement, with eigenvalue differences within 200 pcm over the entire burnup range (up to 140 MWD/kgU) and fission-product and actinide inventories agreeing to within 0.8% and 2.5%, respectively; a heat-pipe microreactor assembly problem with a much higher fuel loading confirmed the same behavior and quantified the bias introduced when the multigroup equivalence effect is neglected. For transient analysis, a pin-cell problem with a step reactivity insertion and temperature feedback reproduced the analytically expected asymptotic power and showed close agreement between the direct and IQS solutions, and a two-dimensional microreactor core problem with control-drum rotation exercised the new moving-drum self-shielding treatment and demonstrated successful coupling of the online crosssection generation with both the direct and IQS transient methods. The capability was further exercised on a full-core pebble-bed problem, in which Griffin was coupled with the System Analysis Module (SAM) to simulate load-following operation of the gPBR with the Doppler feedback resolved at the TRISO fuel kernel temperature. These developments in Griffin provide a convenient, high-fidelity approach to cross-section generation for advanced thermal reactors with geometrically complex and highly heterogeneous configurations, including TRISO-fueled prismatic and pebble-bed systems, and support steady-state, depletion, and transient multiphysics calculations. They also enable self-shielded cross sections to be evaluated directly at the actual coupled state of the system, thereby establishing a foundation for high-fidelity, fully coupled multiphysics analysis of advanced reactors

Park, H.↗
DOE OSTI2022

Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications.

Schranz, Thomas↗
DOE OSTI2022

Marine renewable energy for Arctic observations

Arctic observations are becoming increasingly valuable as researchers investigate climate change and its associated concerns, such as decreasing sea ice and increasing ship traffic. Networks of sensors with frequent sampling capabilities are needed to run forecast models, improve navigation, and inform climate research. Sampling frequency and deployment duration are currently constrained by battery power limitations. In-situ power generation using marine renewable energy sources such as waves and currents can be used to circumvent this constraint. Wave and current resources vary spatially and temporally in the Arctic, with some locations and seasons being better suited for marine renewable energy power generation. Locations and seasons with small resources may still be able to use marine renewable energy because of the low power requirements of the instruments. In this study, we describe the wave and current resources in the Arctic, outline the electricity generation developments that are needed to utilize the resources, and suggest use cases. Wave and current energy converters developed to power observations in the Arctic could also be used to power observations at lower latitudes. Marine renewable energy has the potential to decrease dependence on batteries and improve data collection capabilities in the Arctic; however, this would require the development of new low power technologies that can operate in extreme Arctic environments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Lectin Receptor-like Kinase Signaling during Engineered Ectomycorrhiza Colonization

Mutualistic association can improve a plant’s health and productivity. G-type lectin receptor-like kinase (PtLecRLK1) is a susceptibility factor in Populus trichocarpa that permits root colonization by a beneficial fungus, Laccaria bicolor. Engineering PtLecRLK1 also permits L. bicolor root colonization in non-host plants similar to Populus trichocarpa. The intracellular signaling reprogramed by PtLecRLK1 upon recognition of L. bicolor to allow for the development and maintenance of symbiosis is yet to be determined. In this study, phosphoproteomics was utilized to identify phosphorylation-based relevant signaling pathways associated with PtLecRLK1 recognition of L. bicolor in transgenic switchgrass roots. Our finding shows that PtLecRLK1 in transgenic plants modifies the chitin-triggered plant defense and MAPK signaling along with a significant adjustment in phytohormone signaling, ROS balance, endocytosis, cytoskeleton movement, and proteasomal degradation in order to facilitate the establishment and maintenance of L. bicolor colonization. Moreover, protein–protein interaction data implicate a cGMP-dependent protein kinase as a potential substrate of PtLecRLK1.

ectomycorrhiza colonization↗