SEARCH · Engineering Papers

Results for “physical data and processes”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,261 records · Page 70

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Hunting for gamma-ray emission from fast radio bursts

Context. Fast radio bursts (FRBs) are a recently discovered class of GHz-band, ms-duration, Jansky-level-flux astrophysical transients. Although hundreds of models have been proposed so far for FRB progenitors (the most popular ones involve magnetars), their physical origin and emission mechanism are still a mystery, making them one of the most compelling problems in astrophysics. Aims. FRBs are caused by astrophysical processes that are not yet understood. Exploring their high-energy counterpart is crucial for constraining their origin and emission mechanism. Methods. Thanks to more than 13 years of gamma-ray data collected by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope, and to more than 1000 FRB events (from 561 non-repeating and 22 repeating sources), one of the largest samples created thus far, we performed the largest and deepest search for high-energy emission from FRB sources to date (between 100 MeV and 1 TeV). In addition to the analysis involving individual FRB events on different timescales (from a few seconds up to several years), we performed, for the first time, a stacking analysis on the full sample of FRB events as well as a search for triplet photons in coincidence with the radio event. Results. We do not detect significant emission, reporting the most stringent constraints, on short timescales, for the FRB-like emission from SGR 1935+2154 with E γ <10 41 erg, corresponding to a factor η < 10 7 with respect to the emitted radio energy. Similarly, for the stacked signal of steady emission from all repeaters, the obtained upper limit (UL) on the FRBs luminosity (L γ < 1.6 × 10 43 erg s -1 is more than two orders of magnitude lower than those derived from the individual sources. Finally, no individual or triplet photons have been significantly associated with FRB events. We derived the LAT ms-sensitivity to be ~0.3 ph cm -2 s -1 and constrained the gamma-ray energy E γ,δT = 1 ms ≲ 10 47 (D L /150 Mpc) 2 erg, ruling out a gamma-ray-to-radio energy ratio greater than 10 9 on ms timescales. Conclusions. The results reported here represent the most stringent UL reported so far on the high-energy emission from FRBs on short and long time scales, as well as on cumulative emission and individual photon searches. While the origin of FRBs is still unclear, our work provides important constraints for FRB modelling, which might shed light on their emission mechanism.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion

Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require $\mathcal{O}(10^3)$ parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Assessing equation of state-independent relations for neutron stars with nonparametric models

Relations between neutron star properties that do not depend on the nuclear equation of state offer insights on neutron star physics and have practical applications in data analysis. Such relations are obtained by fitting to a range of phenomenological or nuclear physics equation of state models, each of which may have varying degrees of accuracy. In this study we revisit commonly used relations and reassess them with a very flexible set of phenomenological nonparametric equation of state models that are based on Gaussian processes. Our models correspond to two sets: equations of state which mimic hadronic models, and equations of state with rapidly changing behavior that resemble phase transitions. Here we quantify the accuracy of relations under both sets and discuss their applicability with respect to expected upcoming statistical uncertainties of astrophysical observations. We further propose a goodness-of-fit metric which provides an estimate for the systematic error introduced by using the relation to model a certain equation-of-state set. Overall, the nonparametric distribution is more poorly fit with existing relations, with the I–Love–Q relations retaining the highest degree of universality. Fits degrade for relations involving the tidal deformability, such as the binary-Love and compactness-Love relations, and when introducing phase transition phenomenology. For most relations, systematic errors are comparable to current statistical uncertainties under the nonparametric equation of state distributions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Plasma proteomic biomarkers of physical frailty in heart failure: a propensity score matched discovery-based pilot study

Background: Physical frailty is highly prevalent in heart failure (HF), but we lack an understanding of the underlying pathophysiology. Proteomics evaluation of plasma samples may elucidate potential mechanisms and biomarkers of physical frailty in HF. We aimed to identify plasma proteomic biomarkers that are differentially expressed between physically frail and non physically frail adults with HF. Methods: This was a secondary analysis of a subset of data and plasma samples from a study of frailty among patients with New York Heart Association (NYHA) Functional Classification I-IV HF. Physical frailty was measured using the Frailty Phenotype Criteria. Propensity score matching was used to match pairs of physically frail (n = 20) vs. non-physically frail (n = 20) patients on clinical characteristics. Plasma samples were processed using a sensitive liquid chromatography mass spectrometry platform, utilizing a multiplexed tandem mass tag-labeled quantitative proteomics approach. Differentially expressed proteins were quantified individually using paired t tests with associated log fold change of 0.3 and Fisher’s combined p values. Results: The sample (n = 40) was 62.8±16.9 years old, 58% female, and 55% NYHA Class III/IV. Proteomics analysis revealed 7 proteins differentially expressed using full differential criteria: matrix metalloproteinase-14 was downregulated in frailty, and copine-1, low affinity immunoglobulin gamma Fc region receptor III-A and III-B, probable non-functional immunoglobulin kappa variable 2D-24, glutathione S-transferase Mu 1, and argininosuccinate lyase were upregulated in frailty. Conclusions: Proteomic biomarkers related to the immune system, stress response, and detoxification were differentially expressed between physically frail and non-physically frail adults with HF.

Biomarkers↗

DOE OSTI2020

Fission In R-process Elements - FY2020

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for work on nuclear data (ground-state properties, fission, β-decay), BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities receive funds from the DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

FY22 Progress Report: SRNL Analysis of ICCWR LCM and WAMS Data for Corrosion and Cracking

Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.

3013 Corrosion↗

DOE OSTI2023

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Carbon Capture through Membranes - Leveraging Multiphysics Modeling, Dimensional Analysis and Machine Learning to Scale up and Optimize Devices and Processes for Decarbonization

We study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow (DFfeed). Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗

DOE OSTI2024

Modeling and Scaling up of Membrane Modules Leveraging Dimensional Analysis

In the current analysis, we study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow. Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗

DOE OSTI2025

Ultra-sensitive radon assay using an electrostatic chamber in a recirculating system

Rare event searches such as neutrinoless double beta decay and Weakly Interacting Massive Particle detection require ultra-low background detectors. Radon contamination is a significant challenge for these experiments, which employ highly sensitive radon assay techniques to identify and select low-emission materials. This work presents the development of ultra-sensitive electrostatic chamber (ESC) instruments designed to measure radon emanation in a recirculating gas loop, for future lower background experiments. Unlike traditional methods that separate emanation and detection steps, this system allows continuous radon transport and detection. This is made possible with a custom-built recirculation pump. A Python-based analysis framework, PyDAn, was developed to process and fit time-dependent radon decay data. Radon emanation rates are given for various materials measured with this instrument. A radon source of known activity provides an absolute calibration, enabling statistically-limited minimal detectable activities of 20 uBq. These devices are powerful tools for screening materials in the development of low-background particle physics experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

NEXUS Qubit Analysis: Jump Rates and Efciencies

Correlated qubit errors are a hinderance to quantum computing, but when using quantum chips as a particle detector, they can be a sign of energy deposits. Using a four transmon qubit chip in the NEXUS fridge at Fermilab, we have taken data to better understand the impact ionizing radiation has on the correlated errors. To analyze this data, we need a jump detection code that is robustly tested with understood efficiencies. In this work, we describe the process of getting these efficiencies using a rolling $\chi^2$ jump detection code.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), nonlinearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8- bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Understanding the Angular Momentum Evolution of T Tauri and Herbig Ae/Be Stars

We investigate a sample of six Herbig Ae/Be stars belonging to the Orion OB1 association, as well as 73 low-mass objects, members of the σ Orionis cluster, in order to explore the angular momentum evolution at early stages of evolution, and its possible connection with main-sequence Ap/Bp magnetic stars. Using FIES and HECTOCHELLE spectra, we obtain projected rotational velocities through two independent methods. Individual masses, radii, and ages are computed using evolutionary models, distance, and cluster extinction. Under the assumption that similar physical processes operate in both T Tauri and Herbig Ae/Be stars, we construct snapshots of the protostar’s rotation against mass during the first 10 Myr with the aid of a rotational model that includes a variable disk lifetime, changes in the stellar moment of inertia, a dipolar magnetic field with variable strength, and angular momentum loss through stellar winds powered by accretion. We use these snapshots, as well as the rotational data, to infer a plausible scenario for the angular momentum evolution. We find that magnetic field strengths of a few kilo-Gauss at 3 Myr are required to match the rotational velocities of both groups of stars. Models with masses between 2 M {sub ⊙} and 3 M {sub ⊙} display larger angular momentum values by a factor of ~3, in comparison to stars of similar spectral types on the main sequence. Even though some quantitative estimates on this dramatic decrease with age for Ap/Bp magnetic main-sequence stars are presented, the results obtained for the angular momentum evolution do not explain their low rotation rates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

PUMIPic: A mesh-based approach to unstructured mesh Particle-In-Cell on GPUs

Unstructured mesh particle-in-cell, PIC, simulations executing on the current and next generation of massively parallel systems require new methods for both the mesh and particles to achieve performance and scalability on GPUs. The traditional approach to implementing PIC simulations defines data structures and algorithms in terms of particles with a full copy of the unstructured mesh on every process. To effectively scale the unstructured mesh and particles, mesh-based PIC uses the unstructured mesh as the predominant data structure with the particles stored in terms of the mesh entities. Here, this paper details the PUMIPic library, a framework for developing efficient and performance-portable mesh-based PIC simulations on GPU systems. A pseudo physics simulation based on a five-dimensional gyro-kinetic code for modeling plasma physics is used to examine the performance of PUMIPic. Scaling studies of the unstructured mesh partition and number of particles are performed up to 4096 nodes of the Summit system at Oak Ridge National Laboratory. The studies show that mesh-based PIC can utilize a partitioned mesh and maintain scaling up to system limitations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Constraining composition and temperature variations in the mantle transition zone

The mantle transition zone connects two major layers of Earth’s interior that may be compositionally distinct: the upper mantle and the lower mantle. Wadsleyite is a major mineral in the upper mantle transition zone. Here, we measure the single-crystal elastic properties of hydrous Fe-bearing wadsleyite at high pressure-temperature conditions by Brillouin spectroscopy. Our results are then used to model the global distribution of wadsleyite proportion, temperature, and water content in the upper mantle transition zone by integrating mineral physics data with global seismic observations. Our models show that the upper mantle transition zone near subducted slabs is relatively cold, enriched in wadsleyite, and slightly more hydrated compared to regions where plumes are expected. This study provides direct evidence for the thermochemical heterogeneities in the upper mantle transition zone which is important for understanding the material exchange processes between the upper and lower mantle.

58 GEOSCIENCES↗

DOE OSTI2023

Structure, electrical and thermal properties of single-crystal BaCuGdTe 3

Single crystals of the quaternary chalcogenide BaCuGdTe 3 were obtained by direct reaction of elements allowing for a complete investigation of the intrinsic electrical and thermal properties of this previously uninvestigated material. The structure was investigated by high-resolution single-crystal synchrotron X-ray diffraction, revealing an orthorhombic crystal structure with the space group Cmcm. Although recently identified as a semiconductor suitable for thermoelectric applications from theoretical analyses, our electrical resistivity and Seebeck coefficient measurements show metallic conduction, the latter revealing strong phonon-drag. Temperature dependent hole mobility reveals dominant acoustic phonon scattering. Heat capacity data reveal a Debye temperature of 183 K and a very high density of states at the Fermi level, the latter confirming the metallic nature of this composition. Thermal conductivity is relatively high with Umklapp processes dominating thermal transport above the Debye temperature. The findings in this work lay the foundation for a more detailed understanding of the physical properties of this and similar multinary chalcogenide materials, and is part of the continuing effort in investigating quaternary chalcogenide materials and their suitability for use in technological applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗