Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computation time”

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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep desert aquifers as an archive for Mid- to Late Pleistocene hydroclimate: An example from the southeastern Mediterranean

Many efforts have been made to illuminate the nature of past hydroclimates in semi-arid and arid regions, where current and future shifts in water availability have enormous consequences on human subsistence. Deep desert aquifers, where groundwater is stored for prolonged periods, might serve as a direct record of major paleo-recharge events. To date, groundwater-based paleoclimate reconstructions have mainly focused on a relatively narrow timescale (up to ∼40 kyr), limited by the relatively short half-life of the widely used radiocarbon (5.73 kyr). Here we demonstrate the usage of deep regional aquifers in the arid southeastern Mediterranean as a hydroclimate archive for earlier Mid-to-Late Pleistocene epochs. State-of-the-art dating tools, primarily the 81 Kr radioisotope (t 1/2 = 229 kyr), were combined with other atmosphere-derived tracers to illuminate the impact of four distinguishable wetter episodes over the past 400 kyr, with differences in climatic conditions and paleo-recharge locations. Variations in stable water isotope composition suggest moisture transport from more proximal (Mediterranean) and distal (Atlantic) sources to different parts of the region at distinct times. Large variability in the computed noble gas-based recharge temperature (NGT), ranging ~15–30 °C, cannot be explained by climate variations solely, and points to different recharge pathways, including geothermal heating in the deep unsaturated zone and recharge from high-elevation (colder) regions. The obtained groundwater record complements and enhances the interpretation of other terrestrial archives in the arid region, including a contribution of valuable information regarding the moisture source origin as reflected in the deuterium-excess values, which is unattainable from the common practice analysis of calcitic cave deposits. We conclude that similar applications in other deep (hundred-m-order) regional groundwater systems (e.g., the Sahara desert aquifers) can significantly advance our understanding of long-term (up to 1 Myr) paleo-hydroclimate in arid regions, including places where no terrestrial remnants, such as cave, lake, and spring sediments, are available.

Atom Trap Trace Analysis↗
DOE OSTI2023

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗
DOE OSTI2020

Quantum Accurate Prediction of Plutonium–Plutonium Dihydride Phase Equilibrium Using a Lattice Gas Model

Plutonium-based materials are vital for use as nuclear fuels and as portable power sources for space vehicles. However, elucidating their sensitivity to hydriding corrosion represents an extreme challenge because of the toxicity of Pu as well as their anomalous magnetic properties. In this work, we develop a lattice gas model of plutonium–plutonium dihydride (Pu–PuH 2 ) phase equilibrium that retains the accuracy of density functional theory, while yielding many orders of magnitude improvement in computational efficiency. Using Monte Carlo and free-energy sampling algorithms, we compute a number of Pu–PuH 2 equilibrium properties that are difficult to probe experimentally, including equilibrium pressures and phase compositions and the PuH 2 heat of formation. In conclusion, our method will have particular impact on these types of materials studies, where there is a strong need for computationally efficient approaches to bridge time and length scale gaps between quantum calculations and experiments.

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

Collective dynamics and long-range order in thermal neuristor networks

Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Science & Technology - Other Topics↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2022

Nonreciprocal directional dichroism at telecom wavelengths

Magnetoelectrics with ultra-low symmetry and spin-orbit coupling are well known to display a number of remarkable properties including nonreciprocal directional dichroism. As a polar and chiral magnet, Ni 3 TeO 6 is predicted to host this effect in three fundamentally different configurations, although only two have been experimentally verified. Inspired by the opportunity to unravel the structure-property relations of such a unique light-matter interaction, we combined magneto-optical spectroscopy and first-principles calculations to reveal nonreciprocity in the toroidal geometry and compared our findings with the chiral configurations. We find that formation of Ni toroidal moments is responsible for the largest effects near 1.1 eV—a tendency that is captured by our microscopic model and computational implementation. At the same time, we demonstrate deterministic control of nonreciprocal directional dichroism in Ni 3 TeO 6 across the entire telecom wavelength range. This discovery will accelerate the development of photonics applications that take advantage of unusual symmetry characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring cycling induced crystallographic change in NMC with X-ray diffraction computed tomography

This study presents the application of X-ray diffraction computed tomography for the first time to analyze the crystal dimensions of LiNi 0.33 Mn 0.33 Co 0.33 O 2 electrodes cycled to 4.2 and 4.7 V in full cells with graphite as negative electrodes at 1 μm spatial resolution to determine the change in unit cell dimensions as a result of electrochemical cycling. The nature of the technique permits the spatial localization of the diffraction information in 3D and mapping of heterogeneities from the electrode to the particle level. An overall decrease of 0.4% and 0.6% was observed for the unit cell volume after 100 cycles for the electrodes cycled to 4.2 and 4.7 V. Additionally, focused ion beam-scanning electron microscope cross-sections indicate extensive particle cracking as a function of upper cut-off voltage, further confirming that severe cycling stresses exacerbate degradation. Finally, the technique facilitates the detection of parts of the electrode that have inhomogeneous lattice parameters that deviate from the bulk of the sample, further highlighting the effectiveness of the technique as a diagnostic tool, bridging the gap between crystal structure and electrochemical performance.

25 ENERGY STORAGE↗
DOE OSTI2020

Spectroscopic characterization of a new Re(i) tricarbonyl complex with a thiosemicarbazone derivative: towards sensing and electrocatalytic applications

This work describes the preparation of a new thiosemicarbazone derivative, (Z)- N -ethyl-2-(6-oxo-1,10-phenanthrolin-5(6 H )-ylidene)hydrazinecarbothioamide (phet) and its respective Re( I ) tricarbonyl chloro complex, fac -[ReCl(CO) 3 (phet)]. The spectroscopic, photophysical and electrochemical properties of the new complex were fully investigated through steady state and time-resolved techniques along with computational calculations. In fac -[ReCl(CO) 3 (phet)], the new ligand is coordinated to the metal center through the pyridyl rings of the phenanthroline moiety. The unbound electron pairs in the S atom of the bending thiosemicarbazone group induce new low energy lying electronic transitions. Consequently, enhanced visible light absorption up to 550 nm is observed in acetonitrile due to the overlap between MLCT Re→phet and IL phet(n→π*) transitions. The absorption bands and emission quantum yields of fac -[ReCl(CO) 3 (phet)] are sensitive to proton concentration due to an acid-basic equilibrium in the N atoms of the thiosemicarbazone. Proton dissociation constants of 10.0 ± 0.1 and 11.4 ± 0.2 were determined respectively for the ground and excited states of the new complex. Spectral changes could also be observed in the presence of Zn 2+ cations which can be further explored for sensing applications. The electrochemical behavior of the new complex was studied in detail, revealing up to four one electron reduction processes in the range from 0 to -2.4 V vs. Fc + /Fc. With support of DFT calculations, the first three processes are ascribed to the reduction of the coordinated phet ligand followed by the Re I/0 reduction and consequent Cl - release. Furthermore, the new complex was able to act as an electrocatalyst for CO 2 reduction into CO ( E onset = -1.92 V vs. Fc + /Fc), with a turnover frequency of 2.81 s -1 and turnover number of 24 ± 1 in anhydrous acetonitrile, being the first Re( I ) tricarbonyl complex with a thiosemicarbazone derivative described for this goal. The detailed characterization carried out here can drive the development of new Re( I )-thiosemicarbazone derivatives for different applications.

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

Unveiling the mechanism of the photocatalytic reduction of CO 2 to formate promoted by porphyrinic Zr-based metal–organic frameworks

A complete picture of the reaction mechanism responsible for the photocatalytic reduction of CO 2 into formate promoted by the Zr-based porphyrinic MOF-545 (PCN-222) in acetonitrile/triethanolamine (CH 3 CN/TEOA) solutions is provided for the first time by combining experimental and computational approaches. Firstly, we combined the metalation of the porphyrin linkers and the synthesis under microwave conditions of nano-sized materials (150–200 nm) to reach one of the highest CO 2 RR activities under visible light irradiation reported so far for a MOF-based catalyst in terms of formate production (identified in all cases as the sole product), i.e., 6000 μmol g –1 after 4 h for the NanoMOF-545(Fe). A thorough mechanistic study of CO 2 RR is provided afterwards, which elucidated the respective roles of TEOA, porphyrin linkers and Zr 6 -nodes. Photophysical measurements of both metalated and unmetalated MOFs excluded the prevailing belief of ligand-to-Zr 6 cluster charge-transfer state formation, while bulk photolysis proved that the porphyrinic linkers of the MOF promote the photooxidation of TEOA under catalytic conditions. DFT calculations allowed characterization with atomistic detail of a novel reaction mechanism whereby TEOA˙ radicals generated photochemically at the porphyrinic linkers play the crucial role of transferring a formal hydride to a CO 2 molecule activated on a Zr IV center, which acts as a Lewis acid. Notably, this mechanism backed by additional experimental assays explains the experimental activity trends observed upon metalation, on the basis of the ability of the materials to accomplish the photo-oxidation of TEOA. Here, while elucidating the reaction pathways at work in Zr 6 -based MOFs with chromophore linkers, the findings reported herein pinpoint new mechanistic features capable of rationalizing other light-driven reactions making use of TEOA (or structurally related donors) and ultimately, inspire the bottom-up design of new catalysts and catalytic routes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗
DOE OSTI2020

ServiceX A Distributed, Caching, Columnar Data Delivery Service

We will describe a component of the Intelligent Data Delivery Service being developed in collaboration with IRIS-HEP and the LHC experiments. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analysis. This work is motivated by the data engineering challenges posed by HL-LHC data volumes and the increasing popularity of python and Spark-based analysis workflows.ServiceX gives analyzers the ability to query events by dataset metadata. It uses containerized transformations to extract just the data required for the analysis. This operation is colocated with the data to avoid transferring unnecessary branches over the WAN. Simple filtering operations are supported to further reduce the amount of data transferred.Transformed events are cached in a columnar datastore to accelerate delivery of subsequent similar requests. ServiceX will learn commonly related columns and automatically include them in the transformation to increase the potential for cache hits by other users.Selected events are streamed to the analysis system using an efficient wire protocol that can be readily consumed by a variety of computational frameworks. This reduces time-to-insight for physics analysis by delegating to ServiceX the complexity of event selection, slimming, reformatting, and streaming.

Galewsky, B.↗
DOE OSTI2020

Extreme Compression for Large Scale Data Store

For the last 5 years Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom line outcome would be to provide a state-of-the-art compression algorithm that surpasses those currently available in the ROOT framework, with the purpose of enabling substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4.In this contribution, we will present our concepts of “functionally lossless compression”, have a glance at examples and achievements in other communities, present the results and outcome of our current, ongoing R&D, as well as present a high-level view of our plan to move forward with a ROOT implementation that would deliver a basic solution readily integrated into HENP applications. As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Offsite Data Processing for the GlueX Experiment

The Thomas Jefferson National Accelerator Facility (JLab) 12GeV accelerator upgrade completed in 2015 is now producing data at volumes unprecedented for the lab. The resources required to process this data now exceed the capacity of the onsite farm necessitating the use of offsite computing resources for the first time in the history of JLab. GlueX is now utilizing NERSC and PSC for raw data production. Details of the workflow are presented.

Lawrence, David↗
DOE OSTI2021

Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction memory elements can be used for computation by manipulating a domain wall, a transition region between magnetic domains, but the experimental study of such devices has been limited by high current densities and low tunnel magnetoresistance. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy, spin–orbit torque switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 171% and average resistance-area product RA = 29 Ω μm2, close to the RA of the unpatterned film. Device initialization variation in switching voltage is shown to be curtailed to 7%–10% by controlling the domain wall initial position, which we show corresponds to 90%–96% accuracy in a domain wall-magnetic tunnel junction full adder simulation. Repeatability of writing and resetting the device is shown. A circuit shows an inverter operation between two devices, showing that a voltage window is large enough, compared to the variation noise, to repeatably operate a domain wall-magnetic tunnel junction circuit. These results make strides in using magnetic tunnel junctions and domain walls for in-memory and neuromorphic computing applications.

Alamdar, Mahshid (ORCID:0000000221732935)↗
DOE OSTI2024

Design of experiments to spectroscopically characterize radiation flow in stochastic media

Precise characterization of experimental radiation flow is required to validate the high energy density physics models, numerical methods, and codes that are used to simulate radiation-hydrodynamics phenomena such as thermal radiation transport in stochastic media. The Cassio code is used to simulate thermal radiation flow through inhomogeneous, stochastic-media-foam configurations containing optically thick clumps dispersed within an optically thin background aerogel. Cassio can model small inhomogeneous problems directly, but most problems require approximations to meet computer limitations on run-times and memory usage. Various examples of these approximations are methods that produce, in one calculation, an ensemble-averaged solution and associated standard deviation; reduced spatial dimensionality with approximate geometries; and full material homogenization with no geometric detail. Cassio simulations are used to design experiments at the OMEGA-60 Laser Facility that can measure the radiation flow using the spatially resolved COAX absorption spectroscopy diagnostic. The experimental platforms flow radiation through foam targets ranging from a background-only aerogel, to a single configuration of a specified stochastic medium, to a fully homogenized foam of the background and clump materials. Under constant total clump mass, larger clumps (here, larger than 10 μm diameter) will mix more slowly with the background such that the bulk radiation flow is faster than it would be in a fully homogenized material. The COAX platform can be used to infer temperature and density profiles in both the background material and clumps, simultaneously, and therefore to differentiate radiation flow in a range of stochastic and homogeneous media.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A rotationally invariant approach based on Gutzwiller wave function for correlated electron systems

Here, we introduce a rotationally invariant approach combined with the Gutzwiller conjugate gradient minimization method to study correlated electron systems. In the approach, the Gutzwiller projector is parametrized based on the number of electrons occupying the onsite orbitals instead of the onsite configurations. The approach efficiently groups the onsite orbitals according to their symmetry and greatly reduces the computational complexity, which yields a speedup of $20 \sim 50 \times $ in the minimal basis energy calculation of dimers. The computationally efficient approach promotes more accurate calculations beyond the minimal basis that is inapplicable in the original approach. A large-basis energy calculation of F 2 demonstrates favorable agreements with standard quantum-chemical calculations Bytautas et al (2007 J. Chem. Phys. 127 164317).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Neutrino halo effect on collective neutrino oscillation in iron core-collapse supernova model of a 9.6 M ⊙ star

We extend the multi-angle computational framework and investigate the time evolution of the neutrino halo on collective neutrino oscillation in the core collapse of an iron core progenitor. We find that in the case of the 9.6 M progenitor adopted in this work, there are windows of time when the effects of neutrino halo and collective neutrino oscillation are not simultaneously large. Inside the shock, the impact of the inward-scattered halo neutrino cannot in general be neglected compared to the outward-propagating neutrino flux. However, during early epochs, collective neutrino oscillation is effectively shut down by multi-angle matter suppression. During the intermediate epoch, collective neutrino oscillation is not suppressed, but its onset radius is beyond the still relatively small explosion shock front where the halo is prominent. We also find in the case of the 9.6 M progenitor the halo neutrinos induce a delay in the onset of collective neutrino oscillations. This causes novel flavor conversions which sharpen collective neutrino oscillation spectral features. We predict that the inclusion of neutrino halo effects makes neutrino signals that are more clearly distinct from thermal emission that when halo neutrinos are omitted.

79 ASTRONOMY AND ASTROPHYSICS↗