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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Proxy App Suite Release (FY2020)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Fast Computational Algorithms for Partial Differential Equations and Uncertainty Quantifications

This project concerned the construction, testing and analysis of computational algorithms for solving parameterized and stochastic partial differential equations. The study and understanding of equations of this type is of fundamental importance in numerous engineering and scientific applications. Examples include simulation of plasma dynamics in models of electric propulsion and nuclear fusion, simulation of multiphase flows, such as the flow of water, gas and oil in reservoirs, and structural analysis of the dependence of structures on materials. Parametrization is used in such settings when properties of the models such as viscosity of fluids or electric resistivity of materials are not precisely understood and instead are treated as random variables. The resulting solutions are themselves random, and having such solutions will enable engineers to use probabilistic methods to assess the likelihood of events, for example, whether a pollutant in a liquid will exceed a limit, and to use such analyses to develop ways to ensure positive outcomes. Construction of accurate (high resolution) computational solutions is expensive, requiring significant computer time and computational resources, and there is need to reduce computational cost to make simulation useful and effective. The aim of the project was to construct algorithms to efficiently compute surrogate solutions to parameterized problems to allow for efficient and accurate simulation. The technical approach used focused on two related strategies, based on rank-reduction methods and reduced-order models. These methods construct surrogate solutions of parameter-dependent models by projection or interpolation into low-dimensional approximation spaces. Cost savings are achieved if the low-dimensional spaces can be identified and constructed efficiently and if the resulting low-dimensional algebraic systems can be solved cheaply. Accomplishments include: Theoretical and empirical demonstration of the effectiveness of fast multigrid solution strategies for computing low-rank representations of parameter-dependent solutions to discrete partial differential equations, including the first proof establishing so-called textbook convergence properties for low-rank methods. Development of efficient solution algorithms for solving nonlinear parameter-dependent partial differential equations used in models of fluid dynamics. Developent of efficient algorithms for low-rank representation of solutions of time-dependent simulations of fluid dynamics using multi-dimensional tensor representations of solutions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Summarizing the interoperabilities between xSDK members

Rapid, efficient production of high-quality, sustainable extreme-scale scientific applications is best accomplished using a rich ecosystem of state-of-the art reusable libraries, tools, lightweight frameworks, and defined software methodologies, developed by a community of scientists who are striving to identify, adapt, and adopt best practices in software engineering. The vision of the xSDK is to provide infrastructure for and interoperability of a collection of related and complementary software elements — developed by diverse, independent teams throughout the high-performance computing (HPC) community — that provide the building blocks, tools, models, processes, and related artifacts for rapid and efficient development of high-quality applications. A primary component, and challenge, of the xSDK is to improve interoperability among software libraries and domain components. This document summarizes the current and planned interoperabilities of the twenty-three xSDK member packages as of March 2021. Additionally, the status of the xSDK example codes that demonstrate and test the interoperabilities within the xSDK is provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

FY20 Proxy App Suite Release: Report for ECP Proxy App Project Milestone ADCD-504-10

Version 4.0 of the ECP Proxy App Suite is practically unchanged from the previous release. The current set of proxies has proven useful for many aspects of benchmarking and co-design and we see little reason to alter the suite. Although there have been few changes to the ECP suite, the team has been hard at work in other areas. In the area of Machine Learning (ML) we have now created a separate proxy suite dedicated to this scientific applications of ML. The suite includes: miniGAN (Generative Adversarial Networks), miniRL (Reinforcement Learning), CRADL (inline inference), Cosmoflow-Benchmark (Convolutional Neural Network), and MLPerf-DeepCam (Climate Segmentation Benchmark). Section 3 contains more information about these proxies as well as the principles that are guiding the development of the suite. We have surveyed available proxies for several application domains including Computational Fluid Dynamics, Quantum Chemistry, Quantum Computing Simulation, Molecular Dynamics, Monte Carlo Transport, and Density Functional Theory to identify gaps in proxy coverage. Several new proxy apps are either already available or will be released soon to fill these gaps. Section 4 provides full details. Finally, section 5 reports on our continued collaboration with the ECP Continuous Integration (CI) effort to use proxy apps to help identify problems and roadblocks to cross-lab CI. We have also assisted the El Capitan Center of Excellence (COE) to stand up a CI system that will be used to test releases of HPE and AMD software stacks. We hope that the COE effort can serve as a model for ECP by showing how the Proxy App Team can work with AD and ST teams identify critical features, kernels, patterns, etc. and incorporate them into a CI system that will help ensure that Frontier and Aurora will provide those needed capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Final Report: Next Generation Diamond Drumhead X-Ray Optics

Problem / Opportunity Statement: Future high-impact scientific applications at synchrotron and Free-Electron Laser (FEL) X-ray sources require the development of processing and mounting methods for ultra-thin diamond crystals to meet the most challenging needs of pioneering research at next generation light source facilities. These methods should be capable of manufacturing crystal plates as thin as 10 micrometers over an area of a few mm2, and with a surface cut in different crystal orientations, such as the diamond {100}, {111}, {110} and {113} planes. Project Purpose and Approach: In Phase I, Great Lakes Crystal Technologies (GLCT) partnered with Stonybrook University (SUNY) to created thin diamond drumhead crystals. The purpose of the Phase I project is to transfer the drumhead membrane window etching technology from SUNY to GLCT. Testing of a device using the beamline at Stanford Linear Accelerator Laboratory was initially planned, however, a delay on the part of a third party service provided required us to get beamline testing done at Argonne National Laboratory. Phase I Taskwork: Testing of a high purity diamond plate thinning and etching process was performed. By SUNY and GLCT. Plates were sent to a third party service provider for thinning to ~100 microns, however, delays required that GLCT develop an in-house thinning process to complete the taskwork. GLCT successfully developed this in-house capability even though it was initially planned to be part of the Phase II Taskwork. Multiple practice and deliverable plates were provided to SUNY to be etched into the target drumhead devices. GLCT practiced and refined an in-house plate thinning process and produced five plates with thicknesses in the 60 – 150 micron range. Processing of the plates has begun and will continue in a no-cost extension period necessitated by the third party service provider delay. GLCT also procured and installed a reactive ion etching/inductively coupled plasma system. In addition, one of the thinned plates was measured at the Applied Photon Source at APL for a pre-processing assessment of the crystalline strain. After processing the plate into a drumhead and annealing, a post-processing assessment of the strain will be performed. Phase I Results: The initial data from GLCT’s plate thinning process development, SUNY’s drumhead etching processing, and characterization at APS/ANL demonstrate our teams capabilities to achieve the project goals. Commercial Applications & Benefits: The proposed technology will benefit advanced x-ray science performed at BNL and the 70+ additional advanced beamlines around the world. There are also a number of medical and homeland security applications which could benefit from the technology.

Quayle, Paul↗

DOE OSTI2023

Neutron inelastic scattering measurements on KCl and NaCl samples at the Dome

Precise and accurate measurements of neutron inelastic scattering cross sections are vital for both the LLNL national security mission and for scientific applications such as the nuclear spectroscopy of planetary bodies. In this work, initial measurements of relative cross sections were performed by irradiating KCl and NaCl samples with a DD neutron generator, a DT neutron generator, and a PuB source in building 262, the Dome. The measurements allowed the existing experimental setup to be benchmarked. Several improvements to the setup are suggested for future experiments.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Technology development for modern SRF accelerators

Superconducting RF (SRF) linear accelerators are widely used for scientific application. Challenges, status, breakthroughs and progress in SRF technology for accelerator application are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Working with Multiple MPIs: Overcoming ABI Incompatibility

Scientific applications rely on third-party libraries, which may have multiple implementations. While sharing an application programming interface (API), many of these implementations do not have a shared application binary interface (ABI) and require recompiling. Recompiling can be a long and complex process and sometimes not even an option when the application is shipped binary only. ABI incompatibility strikes at the heart of portability, productivity, and performance by (1) impeding application execution across different HPC and Cloud systems; (2) adding developer hours rebuilding an application; and (3) not taking advantage of host-optimized libraries. This tutorial teaches attendees a portable way to address ABI incompatibility in MPI using the Wi4MPI library. Wi4MPI translates the ABI dynamically from the MPI library used to build the application to a different MPI library available at run time. With Wi4MPI, HPC practitioners can break the portability barrier imposed by ABI incompatibility, potentially increase performance, and increase user productivity. The tutorial is broken down in three components: (1) Understanding ABI compatibility in MPI; (2) Translating MPI libraries dynamically; and (3) Applying dynamic translation to key use cases in HPC, including Containers. If you use more than one MPI library or supercomputer, this tutorial is for you.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Diaspora: Resilience-enabling services for science from HPC to edge

Scientific applications of interest to DOE must increasingly engage distributed resources (e.g., instruments, remote computers, data stores, edge devices) and deliver more stringent levels of service (e.g., uninterrupted processing of experiment data streams). In such systems, state is distributed and components can fail in many ways, often silently, making application resilience a major concern. Addressing the resilience needs of such applications requires methods for gaining knowledge of resources and applications and for translating that knowledge into action. We are working on addressing these needs in the context of multi-messenger astronomy, where detecting and responding to unusual transient events in multiple cosmic messengers (gravitational wave, electromagnetic, high- energy particles) from different instruments leads to a federated learning problem.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Data-Driven Supervised Dimension Reduction for Scientific Discovery (LDRD QTI Report)

This report summarizes the findings of a four months FY24 Advanced Science & Technology (AS&T) LDRD Quick Targeted Investigation (QTI) project focused on the exploration of supervised dimension reduction approaches based on autoencoders. Autoencoders have been extensively employed in literature for unsupervised learning tasks, however, their use for supervised regression tasks, which are common within scientific applications, has been limited. Motivated by linear dimension reduction strategies like Active Subspaces and Adaptive Basis, we explored the possibility of employing autoencoders to discover a non-linear manifold able to represent the original function in fewer dimensions. In this report, we discuss a neural network architecture and we perform a numerical campaign on several problems ranging from simple two-dimensional functions to a model problem for magnetohydrodynamics in five dimensions. In our preliminary results, we show that the proposed approach is found to be superior to linear dimension reduction strategies in representing the target function even with a single latent variable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Grain2Mesh: Mesh Generation for Grain-Scale Nonlinear Elasticity Modeling

The nonlinear hysteretic behavior of rocks under cyclic loading is a crucial area of study in geomechanics. The macroscopic response of a variety of materials has been found to be contingent upon the behavior of the micro-scale structure. This project aims to develop a functional and maintainable software package for generating a multi-phase numerical mesh and accompanying simulation files for finite element modeling used in computational mechanics solvers. Meshes generated from images often lack key preprocessing that reduces noise and prevents mesh element distortion that can increase computational cost. By incorporating user feedback throughout, grain2mesh ensures a high-fidelity mesh that can be used to model grain-scale interactions such as shearing, crack propagation, and interfacial material contrast. Scientific applications of this software include material fracturing, stress-strain analysis for natural and engineered materials, and nonlinear meso-scale analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Network of Modified Portable Optical Particle Spectrometers Instrument Handbook

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s portable optical particle spectrometer (POPS) network (Figure 1) provides an aerosol size distribution spectrum from 150 nm to 5 μm in 16 logarithmically spaced bins. The data emanating from this network of up to four matching POPS in weatherproof enclosures comprises one-minute averages of the aerosol size and number spectrum and the co-located temperature and relative humidity. The total particle concentration and sample flow rate are also provided. The sample stream is not dried before its measurement. Two size spectra are provided: the default size distribution corresponds to the calibration using polystyrene latex spheres (PSL), while the second size distribution corresponds to the calibration using size-selected dry ammonium sulfate aerosol. The principal scientific application of the data provided by the ARM POPS is to determine the aerosol size distribution across a region in three disparate locations – the size distribution is broadly relevant to studies of pollution and of conditions in the boundary layer. A high particle concentration can be an indicator of unhealthy levels of pollution; the dispersion of particles in the atmosphere is a predictor of long-range visibility; and particles serve as collectors of condensing gases including water vapor, and thus are a factor in the formation and properties of fogs and clouds, and in the initiation of precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Sub-GeV dark matter and neutrino searches with Skipper-CCDs: status and prospects

Thick fully-depleted charge-coupled devices (CCDs) with high-resistivity silicon are used in a wide range of scientific applications, from particle detection to astronomical imaging. Their low noise and high charge collection efficiency allow us to reach unprecedented sensitivity to physical processes with low-energy transfers. The newly-developed Skipper-CCD enhances this sensitivity by reducing the read-out noise reaching a sub-electron resolution. In this work, we summarize the fundamentals of the skipper-CCD operation and the prospects for both sub-GeV dark-matter searches and the detection of coherent elastic neutrino-nucleus scattering. A brief discussion of the challenges associated with the construction of the foreseen detectors with multi-kilogram target mass is also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Learning thermodynamic master equations for open quantum systems

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

Mathematics and Computing↗

DOE OSTI2023

Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model’s predictions and estimate the growth of uncertainties in the region far from measurements.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Harnessing Ultra-Intense Long-Wave Infrared Lasers: New Frontiers in Fundamental and Applied Research

This review explores two main topics: the state-of-the-art and emerging capabilities of high-peak-power, ultrafast (picosecond and femtosecond) long-wave infrared (LWIR) laser technology based on CO2 gas laser amplifiers, and the current and advanced scientific applications of this laser class. The discussion is grounded in expertise gained at the Accelerator Test Facility (ATF) of Brookhaven National Laboratory (BNL), a leading center for ultrafast, high-power CO2 laser development and a National User Facility with a strong track record in high-intensity physics experiments. We begin by reviewing the status of 9–10 μm CO2 laser technology and its applications, before exploring potential breakthroughs, including the realization of 100 terawatt femtosecond pulses. These advancements will drive ongoing research in electron and ion acceleration in plasma, along with applications in secondary radiation sources and atmospheric energy transport. Throughout the review, we highlight how wavelength scaling of physical effects enhances the capabilities of ultra-intense lasers in the LWIR spectrum, expanding the frontiers of both fundamental and applied science.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation

Over the next 5 yr, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4 m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage IV dark energy galaxy survey. At z < 0.6, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the universe during the dark-energy-dominated epoch with redshifts of >10 million galaxies spanning 14,000 deg 2 . In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target an r < 19.5 mag limited sample (BGS Bright), a fainter 19.5 < r < 20.175 color-selected sample (BGS Faint), and a smaller low-z quasar sample. BGS will observe these targets using exposure times scaled to achieve homogeneous completeness and cover the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with simulations to show that BGS can complete its strategy and make optimal use of "bright" time. BGS targets have stellar contamination <1%, and their densities do not depend strongly on imaging properties. BGS Bright will achieve >80% fiber assignment efficiency. Finally, BGS Bright and BGS Faint will achieve >95% redshift success over any observing condition. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise baryon acoustic oscillation and redshift-space distortion measurements at z < 0.4. It presents opportunities for new methods that require highly complete and dense samples (e.g., N-point statistics, multitracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

BACCHUS Analysis of Weak Lines in APOGEE Spectra (BAWLAS)

Elements with weak and blended spectral features in stellar spectra are challenging to measure and require specialized analysis methods to precisely measure their chemical abundances. In this work, we have created a catalog of approximately 120,000 giants with high signal-to-noise Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 17 (DR17) spectra, for which we explore weak and blended species to measure Na, P, S, V, Cu, Ce, and Nd abundances and 12 C/ 13 C isotopic ratios. We employ an updated version of the Brussels Automatic Code for Characterizing High-accuracy Spectra (BACCHUS) code to derive these abundances using the stellar parameters measured by APOGEE's DR17 Stellar Parameters and Chemical Abundances Pipeline, quality flagging to identify suspect spectral lines, and a prescription for upper limits. Combined, these allow us to provide our BACCHUS Analysis of Weak Lines in APOGEE Spectra catalog of precise chemical abundances for these weak and blended species, which agrees well with the literature and improves upon APOGEE abundances for these elements, some of which are unable to be measured with APOGEE's current, grid-based approach without computationally expensive expansions. This new catalog can be used alongside APOGEE and provides measurements for many scientific applications ranging from nuclear physics to Galactic chemical evolution and Milky Way population studies. To illustrate this we show some examples of uses for this catalog, such as showing that we observe stars with enhanced s-process abundances or that we can use the 12 C/ 13 C ratios to explore extra mixing along the red giant branch.

79 ASTRONOMY AND ASTROPHYSICS↗