Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Vector Processing”

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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum

Results are reported from a search for supersymmetric particles in the final state with multiple jets and large missing transverse momentum. The search uses a sample of proton-proton collisions at $ \sqrt{s} $ = 13 TeV collected with the CMS detector in 2016–2018, corresponding to an integrated luminosity of 137 fb$^{–1}$, representing essentially the full LHC Run 2 data sample. The analysis is performed in a four-dimensional search region defined in terms of the number of jets, the number of tagged bottom quark jets, the scalar sum of jet transverse momenta, and the magnitude of the vector sum of jet transverse momenta. No significant excess in the event yield is observed relative to the expected background contributions from standard model processes. Limits on the pair production of gluinos and squarks are obtained in the framework of simplified models for supersymmetric particle production and decay processes. Assuming the lightest supersymmetric particle to be a neutralino, lower limits on the gluino mass as large as 2000 to 2310 GeV are obtained at 95% confidence level, while lower limits on the squark mass as large as 1190 to 1630 GeV are obtained, depending on the production scenario.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. Here, in this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multi-element metasurface system for imaging in the near-infrared

Metasurfaces are optically thin 2D arrays of subwavelength scatterers that modify scalar and vector properties of incident electromagnetic fields. Metasurface lenses are of particular interest for imaging applications for their flat form factor, compatibility with CMOS fabrication processes, and potential for correcting aberrations with a small number of elements. Here, we advance this capability by realizing a millimeter-diameter, polarization-independent metalens triplet system with chromatic aberration correction over the wavelength range of 1.30–1.60 μm and monochromatic aberration correction enabling a field of view of 50°.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

A Machine Learning Method for the Forensics Attribution of Separated Plutonium

Plutonium (Pu) source attribution would be a powerful tool to support nuclear nonproliferation efforts. This capability to find the source of a Pu sample would act as a deterrent to smuggling efforts, and also help regulatory agencies verify declared nuclear activities. Work at Texas A&M University yielded a nuclear forensics methodology, which is capable of determining separated Pu’s reactor of origin, fuel burnup, and the time since irradiation (TSI)—three parameters of interest. The methodology used a set of ten intra-element isotopic ratios found in separated Pu, which was compared to a library of isotopic ratio values produced using neutronics simulations for reactors of interest. By calculating the probability that unknown Pu sample’s isotopic ratio set matched a set in the library, the methodology could predict the three parameters of interest of the sample. One shortcoming of this methodology was an inability to correctly attribute spoofed Pu, where Pu sourced from two different reactors or two different fuel burnup levels are mixed. A new methodology to rectify this vulnerability using machine learning (ML) technique is developed, instead of the maximum likelihood calculation previously used and the results are satisfactory. The ML approach leverages the existing simulated data for training the algorithm, but use them efficiently by only using intra-element isotope ratios that contribute to the attribution one of the three parameters at a time. Previously, all isotope ratios were used to attribute all three parameters together. The new methodology attributes the Pu parameters in three steps, one for each parameter, rather than resolving all of the three parameters simultaneously like the previous maximum likelihood approach. First, a support vector machine classifier with a set of seven isotopic ratios finds the reactor of origin and a set of regression models trained using gaussian process predicts the burnup with a different set of seven isotopes. Finally, TSI is calculated analytically using decay equations. Thus far, the new methodology is capable of attributing pure Pu samples and has been validated using experimental data. The next step will to be augment the classifier training data set with spoofed Pu data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Topological Defect Mediated Helical Phase Reorientation by Uniaxial Stress

Strain engineering enables precise, energy-efficient control of nanoscale magnetism. However, unlike well-studied strain-dislocation interactions in mechanical deformation, the spatial evolution of strain-induced spin rearrangement remains poorly understood. Using Lorentz transmission electron microscopy, we manipulate and observe helical domain reorientation under quantitatively applied uniaxial tensile stress. Our findings reveal striking similarity to plastic deformation in metals, where the critical stress for propagation vector ( Q ) reorientation depends on its angle with the stress direction. Magnetic defects mediate reorientation via “break-and-reconnect” or “dislocation gliding–annihilation” processes. Simulations confirm that strain-induced anisotropic Dzyaloshinskii-Moriya interaction may play a key role. These insights advance strain-driven magnetism and offer a promising route for energy-efficient magnetic nanophase control in next-generation information technology. Published by the American Physical Society 2025

36 MATERIALS SCIENCE↗
DOE OSTI2021

CSPlib

This computational singular perturbation library (CSPlib) software is useful for analysis of chemical kinetic systems. The code is used to analyze and describe the dynamical landscape underlying the time evolution of a chemical system. Given a database of state vectors, it allows the analysis of the source term and Jacobian matrix of the chemical system, the identification and decoupling of fast and slow processes, and the analysis of cause-and-effect relationships in the coupled system dynamics. 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. SAND2021-2391 O

Najm, HabibN.↗
DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗
DOE OSTI2019

Classifiers for Dissolution Events in Processing Facility Using Effluents Measurements

Inferring occurrences of dissolutions at a radiochemical processing facility using measurements from an independent monitoring system has potential for verifying operator declarations and compliance with nuclear technology use agreements. We consider the identification of such dissolution events using measurements of radioactive effluents collected at the facility’s off-gas stack. By using simple models for the source term based on radioactive decay-chains and sensor measurements of transported effluents, we formulate a simplified on/off classification problem. There are many available machine learning classification methods applicable to this problem, but they are subject to potential over-fitting. We mitigate this effect by fusing four promising but disparate classifiers: the ensemble of trees, support vector machines, naive Bayes, and k-nearest neighbors. Each classifier is trained to distinguish using effluents measurements between dissolutions of irradiated 237Np/238Pu and Cm/Cf targets from other (or no) radiochemical processing activities. We present Chow and ensemble of trees methods to fuse the classifier outputs to produce a single classification output, and thereby incorporate their diversity of design. We study the performance of these classifiers and fusers using measurements of87Kr,135Xe,138Xe, and138Cs isotopes,and present their training accuracy estimates using individual isotope measurements and all four isotopes taken together.

Rao, Nageswara S.↗
DOE OSTI2021

Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps

In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.

Sane, Sudhanshu↗
DOE OSTI2026

Time-resolved spray characterization via unified optical flow and binarization technique

This work leverages an unsupervised machine learning and advanced image processing techniques to characterize the breakup of fuel sprays in a small-scale combustor under reacting conditions, providing valuable insights into near-nozzle flow phenomenology. The proposed methodology integrates an improved optical flow model on a convolutional neural network to extract flow vectors with a binarization technique to assess droplets’ size and shape across the region of interest. The velocimetry approach demonstrates superior performance compared to a state-of-the-art optical flow model when applied to high-speed X-ray phase contrast spray images, achieving more accurate and reliable flow predictions. Moreover, breakup processes are quantified by breakup length and sphericity in accordance with velocity estimations, allowing a more complete characterization of the flow. This study establishes a robust methodology for analyzing spray morphology and primary breakup in compact combustors, contributing valuable means of understanding and optimizing fuel spray behavior in advanced combustion systems.

42 ENGINEERING↗
DOE OSTI2018

Search for dark matter in events with a hadronically decaying vector boson and missing transverse momentum in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

A search for dark matter (DM) particles produced in association with a hadronically decaying vector boson is performed using pp collision data at a centre-of-mass energy of s√=13 TeV corresponding to an integrated luminosity of 36.1 fb -1 , recorded by the ATLAS detector at the Large Hadron Collider. This analysis improves on previous searches for processes with hadronic decays of W and Z bosons in association with large missing transverse momentum (mono-W/Z searches) due to the larger dataset and further optimization of the event selection and signal region definitions. In addition to the mono-W/Z search, the as yet unexplored hypothesis of a new vector boson Z' produced in association with dark matter is considered (mono-Z' search). No significant excess over the Standard Model prediction is observed. The results of the mono-W/Z search are interpreted in terms of limits on invisible Higgs boson decays into dark matter particles, constraints on the parameter space of the simplified vector-mediator model and generic upper limits on the visible cross sections for W/Z+DM production. The results of the mono-Z' search are shown in the framework of several simplified-model scenarios involving DM production in association with the Z' boson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Volumetric and shear strain localization throughout triaxial compression experiments on rocks

Deformation localization is a widely observed, but rarely quantified process in the crust. Recent observations suggest that the localization of seismicity and fracture networks can help identify the approach to catastrophic failure. Here, we quantify the localization processes of the volumetric and deviatoric strain components in twelve triaxial compression experiments imaged with X-ray tomography. We capture three-dimensional images of the rock cores during triaxial compressing toward failure, and then calculate the local strain components using digital volume correlation. The divergence and curl of the incremental displacement vector field provide the volumetric and deviatoric components of the strain field. We quantify localization using the proportion of the rock occupied by high magnitudes of the volumetric and deviatoric strains, and the Gini coefficient of these high magnitude strains, which measures the deviation from a uniform process. We find that the vast majority, but not all, of the experiments experience strain localization toward failure. The rocks typically experience their maximum degree of strain localization not immediately preceding failure, but on average at 90% of the failure stress. The volumetric strain tends to localize more than the deviatoric strain. Finally, these observations support using the localization of the volumetric strain, along with the deviatoric strain, to identify the evolution of the precursory phase preceding earthquakes.

58 GEOSCIENCES↗
DOE OSTI2023

Measurement of electroweak $Z\left(\nu \overline{\nu}\right)\gamma jj$ production and limits on anomalous quartic gauge couplings in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

The electroweak production of $Z\left(\nu \overline{\nu}\right)\gamma jj$ in association with two jets is studied in a regime with a photon of high transverse momentum above 150 GeV using proton–proton collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider. The analysis uses a data sample with an integrated luminosity of 139 fb –1 collected by the ATLAS detector during the 2015–2018 LHC data-taking period. This process is an important probe of the electroweak symmetry breaking mechanism in the Standard Model and is sensitive to quartic gauge boson couplings via vector-boson scattering. The fiducial $Z\left(\nu \overline{\nu}\right)\gamma jj$ cross section for electroweak production is measured to be ${0.77}_{-0.30}^{+0.34}$ fb and is consistent with the Standard Model prediction. Evidence of electroweak $Z\left(\nu \overline{\nu}\right)\gamma jj$ production is found with an observed significance of 3.2σ for the background-only hypothesis, compared with an expected significance of 3.7σ. The combination of this result with the previously published ATLAS observation of electroweak $Z\left(\nu \overline{\nu}\right)\gamma jj$ production yields an observed (expected) signal significance of 6.3σ (6.6σ). Limits on anomalous quartic gauge boson couplings are obtained in the framework of effective field theory with dimension-8 operators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

A multi-purpose toolkit to enable advanced genome engineering in plants

Here, we report a comprehensive toolkit that enables targeted, specific modification of monocot and dicot genomes using a variety of genome engineering approaches. Our reagents, based on Transcription Activator-Like Effector Nucleases TALENs and the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 system, are systematized for fast, modular cloning and accommodate diverse regulatory sequences to drive reagent expression. Vectors are optimized to create either single or multiple gene knockouts and large chromosomal deletions. Moreover, integration of geminivirus-based vectors enables precise gene editing through homologous recombination. Regulation of transcription is also possible. A web-based tool streamlines vector selection and construction. One advantage of our platform is the use of the Csy-type (CRISPR system yersinia) ribonuclease 4 Csy4 and tRNA processing enzymes to simultaneously express multiple guide RNAs (gRNAs). For example, we demonstrate targeted deletions in up to six genes by expressing twelve gRNAs from a single transcript. Csy4 and tRNA expression systems are almost twice as effective in inducing mutations as gRNAs expressed from individual RNA polymerase III promoters. Mutagenesis can be further enhanced 2.5-fold by incorporating the Trex2 exonuclease. Finally, we demonstrate that Cas9 nickases induce gene targeting at frequencies comparable to native Cas9 when they are delivered on geminivirus replicons. The reagents have been successfully validated in tomato (Solanum lycopersicum), tobacco (Nicotiana tabacum), Medicago truncatula, wheat (Triticum aestivum), and barley (Hordeum vulgare).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗
DOE OSTI2018

Analysis of the Switching Characteristics of MRAM Cells Based on Materials with Uniaxial Anisotropy

Magnetoresistive random access memory (MRAM) has some advantages over other types of memory. However, MRAM has one substantial drawback: the current density and magnetic field that must be applied to switch the spin-valve free layer inside an MRAM cell are too high. The dependence of the current density and switching magnetic field on the magnetic parameters of the material from which the ferromagnetic layers of a spin valve are fabricated is therefore analyzed. A comparison of the critical characteristics of a spin valve with longitudinal anisotropy shows that cobalt, iron, and alloys of them; cobalt ferroborates; and alloys of cobalt with gadolinium, are promising materials for fabricating spin valves. Bifurcation diagrams of equations that describe the valve switching process are presented and analyzed. The four optimum switching modes of a valve are selected, based on an investigation of the dynamics of the magnetization vector. The magnitudes of the external magnetic field and controlling injection current that correspond to stable MRAM cell switching are compared for a variety of materials. The switching time of an MRAM cell is estimated numerically, and the conditions for its optimum speed are determined. It is established that the most promising materials for spin-valve fabrication are Fe{sub 40}Co{sub 40}B{sub 20} and Co{sub 80}Gd{sub 20}, annealed at 300 and 200°C, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The Nab experiment: A precision measurement of unpolarized neutron beta decay

Neutron beta decay is one of the most fundamental processes in nuclear physics and provides sensitive means to uncover the details of the weak interaction. Neutron beta decay can evaluate the ratio of axial-vector to vector coupling constants in the standard model, λ = gA/gV, through multiple decay correlations. The Nab experiment will carry out measurements of the electron-neutrino correlation parameter a with a precision of δa/a = 10 -3 and the Fierz interference term b to δb = 3 × 10 -3 in unpolarized free neutron beta decay. These results, along with a more precise measurement of the neutron lifetime, aim to deliver an independent determination of the ratio λ with a precision of δλ/λ = 0.03% that will allow an evaluation of Vud and sensitively test CKM unitarity, independent of nuclear models. Nab utilizes a novel, long asymmetric spectrometer that guides the decay electron and proton to two large area silicon detectors in order to precisely determine the electron energy and an estimation of the proton momentum from the proton time of flight. The Nab spectrometer is being commissioned at the Fundamental Neutron Physics Beamline at the Spallation Neutron Source at Oak Ridge National Lab. We present an overview of the Nab experiment and recent updates on the spectrometer, analysis, and systematic effects.

Fry, Jason↗