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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational approaches to model X-ray photon correlation spectroscopy from molecular dynamics

X-ray photon correlation spectroscopy (XPCS) allows for the resolution of dynamic processes within a material across a wide range of length and time scales. X-ray speckle visibility spectroscopy (XSVS) is a related method that uses a single diffraction pattern to probe ultrafast dynamics. Interpretation of the XPCS and XSVS data in terms of underlying physical processes is necessary to establish the connection between the macroscopic responses and the microstructural dynamics. To aid the interpretation of the XPCS and XSVS data, we present a computational framework to model these experiments by computing the X-ray scattering intensity directly from the atomic positions obtained from molecular dynamics simulations. We compare the efficiency and accuracy of two alternative computational methods: the direct method computing the intensity at each diffraction vector separately, and a method based on fast Fourier transform that computes the intensities at all diffraction vectors at once. Furthermore, the computed X-ray speckle patterns capture the density fluctuations over a range of length and time scales and are shown to reproduce the known properties and relations of experimental XPCS and XSVS for liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enabling Scalable and Extensible Memory-mapped Datastores in Userspace

Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present UMap , a scalable and extensible userspace service for memory-mapping datastores. Furthermore, through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, UMap enables application performance to scale even at high concurrency. We evaluate UMap in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that UMap as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 × . We performed two case studies to demonstrate UMap 's extensibility. First, a new datastore residing in remote memory is incorporated into UMap as an application-specific plugin. Second, we present a persistent memory allocator Metall built atop UMap for unified storage/memory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Lookup Table Based Low Resource Usage Waveform Data Compression Block in FPGA

aA lookup table based lossless data compression scheme suitable for FPGA implementation, with very low silicon resource usage, is developed. Different algorithms can be implemented by simply changing the contents of the lookup tables in the functional block while keeping the logic circuits unchanged. It exhibits performance comparable to the zip compression software in Linux distributions. The compression block can fit into almost any existing FPGA used in the online data acquisition systems of high energy physics experiments. The decompression process can be performed in either software or in FPGA firmware, allowing users to process compressed data flexibly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

r-process abundances in neutron-rich merger ejecta given different theoretical nuclear physics inputs

This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references. When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The role of supernova convection for the lower mass gap in the isolated binary formation of gravitational wave sources

ABSTRACT Understanding the astrophysical phenomena involving compact objects requires an insight about the engine behind the core-collapse supernovae (SNe) and the fate of the stellar collapse of massive stars. In particular, this insight is crucial in developing an understanding of the origin and formation channels of the growing populations of the detected black hole–black hole, black hole–neutron star, and neutron star–neutron star mergers. The time-scale of convection growth may have a large effect on the strength of SN explosion and therefore also on the mass distribution of stellar remnants. We adopt new formulas for the relation between the pre-SN star properties and their remnants and check how they impact the population of double compact object (DCO) mergers formed via the isolated binary evolution. The new formulas give one the ability to test a wide spectrum of assumptions on the convection growth time. In particular, the different variants allow for a smooth transition between having a deep mass gap and a remnant mass distribution filled by massive neutron stars and low-mass black holes. We present the distribution of masses, mass ratios, and the local merger rate densities of DCO for the different variants of new formulas and test them together with different approaches to other highly uncertain processes. We find that the mass distribution of DCO mergers is sensitive to the adopted assumption on the SN convection growth time-scale up to $m_1+m_2 \lesssim 35 \, \mathrm{M}_{\odot }$. Between the two extreme tested variants the probability of compact object formation within the mass gap may differ by up to approximately two orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Integrated package of electron cyclotron emission imaging data processing and forward modeling in OMFIT

An Electron Cyclotron Emission Imaging (ECEI) data analysis module has been developed for the OMFIT platform to accommodate the needs of users at the DIII-D tokamak for physics applications. The user can easily access the ECEI spatial observation windows in the plasma that are calculated based on the automatically retrieved hardware setup and available DIII-D equilibria, perform spectral analysis, and make 2D electron temperature fluctuation images. The module provides a powerful data post processing package for extracting important physics parameters from the 2D measurements, including the radial structure and poloidal mode number of Alfven Eigenmodes, as well as the frequency-vs-wavenumber dispersion relationship of broadband MHD. Lastly, the module propagates characterized synthetic fluctuations for the user, so one can perform forward modeling tasks with simple analytical fluctuations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
DOE OSTI2024

Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Methods and devices for preventing computationally explosive calculations in a computer for model parameters distributed on a hierarchy of geometric simplices

A computer-implemented method of preventing computationally explosive calculations. The method includes obtaining, by a processor of the computer, measured data of one of a physical process or a physical object; performing hierarchical numerical modeling of a physical process inclusive of an Earth model containing at least one of (a) infrastructure in the ground and (b) a formation feature in the ground, wherein predicted data is generated; comparing the measured data to the predicted data to calculate an estimated error; analyzing the estimated error via an inversion process to update the at least one of the Earth model and infrastructure model so as to reduce the estimated error and to determine a final composite Earth model of at least one of the infrastructure and the feature; and using the final composite Earth model to characterize at least one of the process and the physical object.

Source record↗
DOE OSTI2021

Definition of a new (Doniach-Sunjic-Shirley) peak shape for fitting asymmetric signals applied to reduced graphene oxide/graphene oxide XPS spectra

The existence of asymmetry in X-ray photoelectron spectroscopy (XPS) photoemission lines is widely accepted, but line shapes designed to accommodate asymmetry are generally lacking in theoretical justification. Here in this work, we present a new line shape for describing asymmetry in XPS signals that is based on two facts. First, the most widely known line shape for fitting asymmetric XPS signals that has a theoretical basis, referred to as the Doniach-Sunjic (DS) line shape, suffers from a mathematical inconvenience, which is that for asymmetric shapes the area beneath the curve (above the x-axis) is infinite. Second, it is common practice in XPS to remove the inelastically scattered background response of a peak in question with the Shirley algorithm. The new line shape described herein attempts to retain the theoretical virtues of the DS line shape, while allowing the use of a Shirley background, with the consequence that the resulting line shape has a finite area. To illustrate the use of this Doniach-Sunjic-Shirley (DSS) line shape, a set of spectra obtained from varying amounts of graphene oxide (GO) and reduced GO on a patterned, heterogeneous surface are fit and discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Approximate recoverability and relative entropy II: 2-positive channels of general von Neumann algebras

Abstract We generalize our results in paper I in this series to quantum channels between general von Neumann algebras, proving the approximate recoverability of states which undergo a small change in relative entropy through the channel. To this end, we derive a strengthened form of the quantum data processing inequality for the change in relative entropy of two states under a channel between two von Neumann algebras. Compared to the usual inequality, there is an explicit lower bound involving the fidelity between the original state and a recovery channel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Density weighted angular redshift fluctuations: a new cosmological observable

We propose the use of angular fluctuations in the galaxy redshift field as a new way to extract cosmological information in the Universe. This new probe $\delta z (\hat{\mathbf {n}})$ consists of the statistics of sky maps built by projecting redshifts under a Gaussian window of width σz centred upon a redshift zobs, and weighted by the galaxy density field. We compute the angular power spectrum of the $\delta z (\hat{\mathbf {n}})$ field in both numerical simulations and in linear perturbation theory. From these, we find that the $\delta z (\hat{\mathbf {n}})$ field (i) is sensitive to the underlying density and peculiar velocity fields; (ii) is highly correlated, at the $\gtrsim 60\, {{\ \rm per\ cent}}$ level, to the line-of-sight projected peculiar velocity field; (iii) for narrow windows (σz < 0.03), it is almost completely uncorrelated to the projected galaxy angular density field under the same redshift window; and (iv) it is largely unaffected by multiplicative and additive systematic errors on the observed number of galaxies that are redshift-independent over ~σz. We conclude that $\delta z (\hat{\mathbf {n}})$ is a simple and robust tomographic measure of the cosmic density and velocity fields, complementary to angular clustering, that will contribute to more complete exploitations of current and upcoming galaxy redshift surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Unsupervised quantum circuit learning in high energy physics

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. Here, in this work, we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use nonadversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over two and three variables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Abstract for CRADA among National Energy Technology Laboratory, Research Triangle Institute, Lawrence Livermore National Laboratory, and Los Alamos National Laboratory (AGMT-0921)

Through the Carbon Capture Simulation for Industry Impact (CCSI 2 ) project, the National Energy Technology Laboratory (NETL), Lawrence Livermore National Laboratory (LLNL), and Los Alamos National Laboratory (LANL) (herein collectively “the CCSI 2 team”) will collaborate through a CRADA with the Research Triangle Institute (RTI). This collaboration will develop a modeling framework of RTI’s Non-Aqueous Solvent (NAS) process using physical and thermodynamic data and models previously developed by RTI. RTI will provide existing models and data on its NAS system to inform the model construction effort by the CCSI 2 team.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Chromium Nucleosynthesis and Silicon–Carbon Shell Mergers in Massive Stars

In this work, we analyze the production of the element Cr in galactic chemical evolution (GCE) models using the NuGrid nucleosynthesis yields set. We show that the unusually large [Cr/Fe] abundance at [Fe/H] ≈ 0 reported by previous studies using those yields and predicted by our Milky Way model originates from the merging of convective Si-burning and C-burning shells in a 20 ${M}_{\odot }$ model at metallicity Z = 0.01, about an hour before the star explodes. This merger mixes the incomplete burning material in the Si shell, including 51 V and 52 Cr, out to the edge of the carbon/oxygen (CO) core. The adopted supernova model ejects the outer 2 ${M}_{\odot }$ of the CO core, which includes a significant fraction of the Cr-rich material. When including this 20 M⊙ model at Z = 0.01 in the yields interpolation scheme of our GCE model for stars between 15 and 25 ${M}_{\odot }$, we overestimate [Cr/Fe] by an order of magnitude at [Fe/H] ≈ 0 relative to observations in the Galactic disk. This raises a number of questions regarding the occurrence of Si–C shell mergers in nature, the accuracy of different simulation approaches, and the impact of such mergers on the presupernova structure and explosion dynamics. According to the conditions in this 1D stellar model, the substantial penetration of C-shell material into the Si shell could launch a convective–reactive global oscillation if a merger does take place. In any case, GCE provides stringent constraints on the outcome of this stellar evolution phase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

High-Throughput Data Processing at FRIB Using ESnet

Real-time or nearly real-time (nearline) data processing methods are critical tools as detector technologies and data acquisition (DAQ) systems allow for higher data rates and volumes. The introduction of the energy sciences network (ESnet), a U.S. Department of Energy (DOE) supported high-speed network for scientific research, creates opportunities to leverage the computing power of DOE facilities like the National Energy Research Scientific Computing Center (NERSC). As a first step toward realizing a DOE Office of Science Integrated Research Infrastructure (IRI) pattern, an automated workflow was developed to remotely process data obtained from a nuclear physics experiment at the Facility for Rare Isotope Beams (FRIB) at NERSC with data transferred between FRIB and NERSC over ESnet. The workflow demonstrated the ability to process one week’s worth of experimental data in approximately 90 min and was used successfully for nearline analysis during a recently completed FRIB experiment. Here, a summary of the workflow development and results of recent demonstrations will be presented.

Data processing↗
DOE OSTI2021

Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian deep learning

The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true data-generating process is based on physical processes, these impose symmetries and constraints, and the generative model can be created by learning an effective description of the underlying physics, which enables scaling of the generative model to very high dimensions. In this work, we propose Lagrangian deep learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations. The model uses layers of Lagrangian displacements of particles describing the observables to learn the effective physical laws. The displacements are modeled as the gradient of an effective potential, which explicitly satisfies the translational and rotational invariance. The total number of learned parameters is only of order 10, and they can be viewed as effective theory parameters. Further, we combine N-body solver fast particle mesh (FastPM) with LDL and apply it to a wide range of cosmological outputs, from the dark matter to the stellar maps, gas density, and temperature. The computational cost of LDL is nearly four orders of magnitude lower than that of the full hydrodynamical simulations, yet it outperforms them at the same resolution. We achieve this with only of order 10 layers from the initial conditions to the final output, in contrast to typical cosmological simulations with thousands of time steps. This opens up the possibility of analyzing cosmological observations entirely within this framework, without the need for large dark-matter simulations.

79 ASTRONOMY AND ASTROPHYSICS↗