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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

H420 Imager Calibration Procedure: Field of View Scan

Gamma-ray imagers with coded apertures have a finite field of view (FOV) within which an image of a source can be generated. A “FOV scan” is a facet of imager calibration procedure that involves collecting specialized data for a range of source locations filling the FOV in order to understand and correct for differences in imager performance. Systematic distortions reveal relative displacements between internal imager components, which can be accounted for in data analysis. The success of this scan requires a stable imager position during the full measurement duration (~hours). This document summarizes the hardware and software tools required to conduct a FOV calibration scan of an H3D H420 Coded Aperture Gamma-Ray Imager.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks

Here we analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify time-dependent parameters and data-driven fractional differential operators. In particular, we consider several variations of the classical susceptible-infectious-removed (SIR) model by introducing more compartments and fractional-order and time-delay models. We report the results for the spread of COVID-19 in New York City, Rhode Island and Michigan states and Italy, by simultaneously inferring the unknown parameters and the unobserved dynamics. For integer-order and time-delay models, we fit the available data by identifying time-dependent parameters, which are represented by neural networks. In contrast, for fractional differential models, we fit the data by determining different time-dependent derivative orders for each compartment, which we represent by neural networks. We investigate the structural and practical identifiability of these unknown functions for different datasets, and quantify the uncertainty associated with neural networks and with control measures in forecasting the pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modelling the impact of host galaxy dust on type Ia supernova distance measurements

Type Ia Supernovae (SNe Ia) are a critical tool in measuring the accelerating expansion of the universe. Recent efforts to improve these standard candles have focused on incorporating the effects of dust on distance measurements with SNe Ia. In this paper, we use the state-of-the-art Dark Energy Survey 5 year sample to evaluate two different families of dust models: empirical extinction models derived from SNe Ia data and physical attenuation models from the spectra of galaxies. In this work, we use realistic simulations of SNe Ia to forward-model different models of dust and compare summary statistics in order to test different assumptions and impacts on SNe Ia data. Among the SNe Ia-derived models, we find that a logistic function of the total-to-selective extinction R V best recreates the correlations between supernova distance measurements and host galaxy properties, though an additional 0.02 mag of grey scatter is needed to fully explain the scatter in SNIa brightness in all cases. These empirically derived extinction distributions are highly incompatible with the physical attenuation models from galactic spectral measurements. From these results, we conclude that SNe Ia must either preferentially select extreme ends of galactic dust distributions, or that the characterization of dust along the SNe Ia line-of-sight is incompatible with that of galactic dust distributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
DOE OSTI2026

Yield Estimates of three Historical Atmospheric Nuclear Explosions at Lop Nor, from Reduced Order Models of Regionally Recorded Rayleigh Waveforms and One Seismic Station: A Brief Communication

We use a reduced order model (ROM) for Rayleigh waveforms sourced by large atmospheric explosions along with data collected from a single seismic station (TLG) to estimate the yields of three atmospheric nuclear tests with order-megaton (MT) yields. These historical tests were conducted by China between 1973 and 1980 (CHIC 15, CHIC 16, and CHIC 26). We use our ROM to estimate yields of 2.34 MT (CHIC 15), 0.51 MT (CHIC 16), and 1.15 (CHIC 26) in the 20.5 s period band that are consistent with publicly accepted values of 3 MT (CHIC 15), 0.50 MT (CHIC 16), and 0.730 MT (CHIC 26).

58 GEOSCIENCES↗
DOE OSTI2020

Tilted fluctuation electron microscopy

Fluctuation electron microscopy (FEM) is a scanning nanodiffraction-based method that offers a unique approach to characterizing nanometer-scale medium-range order (MRO) in disordered materials. In addition to determining the degree of MRO, careful analysis of scanning nanodiffraction data can also be used to determine strain in thin film amorphous samples. We applied FEM to characterize the strain and MRO of magnetron sputtered amorphous tantalum (a-Ta) thin films over a range of tilt angles from 0° to 45° in order to measure any deviations between the in-plane and out-of-plane strain and MRO. We validate our approach using electron diffraction simulations of FEM experiments for a-Ta. We measure anisotropic strain in the simulated a-Ta diffraction patterns and find that the experimental a-Ta is isotropically strained within the accuracy of our method. Our approach provides a workflow for acquiring tilted scanning nanodiffraction data, determining the relative strain and ordering as a function of in- A nd out-of-plane directions, and removing any artifacts induced in FEM data due to strain. We also describe some limitations of the tilted FEM method when applied to thin films with very low strains.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SPARC - Plans for a New Critical Experiment Facility with a Horizontal Split Table

Several critical experiment facilities, sometimes referred to as zero power reactor facilities, have provided crucial data to aid understanding and validate nuclear-physics models since the beginning of nuclear technology. Indeed, the first man-made reactor, Chicago Pile-1, was essentially this type of reactor. However, there was a downturn in nuclear technology development toward the turn of the millennium, and the need for these specialized research facilities waned. Now there are few of these experimental facilities operational in the world and those that remain have relatively small critical assembly machines. The need for criticality safety benchmark experiments at intermediate neutron energy levels and the modern resurgence of interest in advanced reactors designs, many of which do not have historical precedents in terms of nuclear fuel composition, moderator, and coolant combinations, all combine to create a substantial need for a critical experiment facility with a large horizontal split-table (HST) machine. A HST machine is used to arrange two separate and subcritical parts of a core assembly, bring them together in a precise manner to achieve criticality using remote controls, and separate them to achieve a subcritical configuration again. A new effort was recently performed to develop user needs for a HST, assess candidate locations at the Idaho National Laboratory (INL), and develop a plan for deployment. This project is referred to as the System Physics Advanced Reactor Critical facility (SPARC). A few months after this assessment began, and shortly after as a viable pathway was emerging, a series of important presidential executive orders were issued to revitalize nuclear energy in the United States (U.S.). The relevance of SPARC to these executive orders was immediately apparent. The far-reaching potential of SPARC to these executive orders will reside in its ability to produce data which facilitates licensing of advanced nuclear reactor designs while reducing uncertainties to help increase energy production alongside new criticality safety data to enable more efficient nuclear fuel manufacture, transport, and storage.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

S -factor and scattering-parameter extractions from ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}{ \rightarrow }^{7}\mathrm{Be}+\gamma $

Previous studies of the reaction ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}\to {}^{7}\mathrm{Be}+\gamma $ have mainly focused on providing the best central value and error bar for the S factor at solar energies. Experimental measurements of this capture reaction at higher energies, the ${}^{3}\mathrm{He}$–${}^{4}\mathrm{He}$ scattering phase shifts, as well as properties of ${}^{7}\mathrm{Be}$ and its excited state, have been used to constrain the theoretical models employed for this purpose. Here we show that much more information than was previously appreciated can be extracted from angle-integrated capture data alone. We use the next-to-leading-order (NLO) amplitude in an effective field theory (EFT) for ${}^{3}\mathrm{He}+{}^{4}\mathrm{He}\to {}^{7}\mathrm{Be}+\gamma $ to perform the extrapolation. At this order the EFT describes the capture process using an s-wave scattering length and effective range, the asymptotic properties of ${}^{7}\mathrm{Be}$ and its excited state, and short-distance contributions to the E1 capture amplitude. We extract the multi-dimensional posterior of all these parameters via a Bayesian analysis that uses capture data below 2 MeV. We find that properties of the ${}^{7}\mathrm{Be}$ ground and excited states are well constrained. The total S factor $S(0)\,={0.577}_{-0.016}^{+0.015}$ keV b, while the branching ratio for excited- to ground-state capture at zero energy, ${Br}(0)={0.406}_{-0.011}^{+0.013}$, both at 68% degree of belief. This S(0) is broadly consistent with other recent evaluations, and agrees with the previously recommended value $S(0)=0.56\pm 0.03\,\,\mathrm{keV}$ b, but has a smaller error bar. We also find significant constraints on ${}^{3}\mathrm{He}$–${}^{4}\mathrm{He}$ scattering parameters, and we obtain constraints on the angular distribution of capture gamma rays, which is important for interpreting experiments. The path forward for this reaction seems to lie with better measurements of the scattering phase shifts and S(E)'s angular dependence away from zero energy, together with better understanding of the asymptotic normalization coefficients of the ${}^{7}$Be bound states' wave functions. Lastly, data on these could further reduce the uncertainty on S(0).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network

As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).

Wang, Jinzhen↗
DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗
DOE OSTI2024

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Genome-scale phylogeny and comparative genomics of the fungal order Sordariales

The order Sordariales is taxonomically diverse, and harbours many species with different lifestyles and large economic importance. Despite its importance, a robust genome-scale phylogeny, and associated comparative genomic analysis of the order is lacking. In this study, we examined whole-genome data from 99 Sordariales, including 52 newly sequenced genomes, and seven outgroup taxa. We inferred a comprehensive phylogeny that resolved several contentious relationships amongst families in the order, and cleared-up intrafamily relationships within the Podosporaceae. Extensive comparative genomics showed that genomes from the three largest families in the dataset (Chaetomiaceae, Podosporaceae and Sordariaceae) differ greatly in GC content, genome size, gene number, repeat percentage, evolutionary rate, and genome content affected by repeat-induced point mutations (RIP). All genomic traits showed phylogenetic signal, and ancestral state reconstruction revealed that the variation of the properties stems primarily from within-family evolution. Together, the results provide a thorough framework for understanding genome evolution in this important group of fungi.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

In this paper, we consider the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outer products of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in machine learning. The dth-order empirical moment tensor of a set of p observations of n variables is a symmetric d-way tensor. Our goal is to nd a low-rank tensor approximation comprising r $\ll$ p symmetric outer products. The challenge is that forming the empirical moment tensor costs O(pn d ) operations and O(n d ) storage, which may be prohibitively expensive; additionally, the algorithm to compute the low-rank approximation costs O(n d ) per iteration. Our contribution is avoiding formation of the moment tensor, computing the low-rank tensor approximation of the moment tensor implicitly using O(pnr) operations per iteration and no extra memory. This advance opens the door to more applications of higher-order moments since they can now be efficiently computed. We present numerical evidence of the computational savings and show an example of estimating the means for higher-order moments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Platform for 100 s Mbar equation of state measurements on the National Ignition Facility

Equation of state (EOS) measurements in the 100 s Mbar range are needed to underwrite models employed in the simulation of high energy density plasmas. To this end, a platform has been developed for fielding on the National Ignition Facility, capable of producing high-quality impedance match EOS data, wherein a planar, high-pressure, steady shock is driven into a sample package, and sample and reference standard shock velocities are measured. This platform, dubbed planar high pressure, or PHP, was fielded with an initial proof-of-concept shot in January 2023. The first PHP shot, aiming to study gold, demonstrated a pressure close to 400 Mbar, two orders of magnitude higher than previously reported gold EOS data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

CTA and SWGO can discover Higgsino dark matter annihilation

Thermal Higgsino dark matter (DM), with a mass near 1.1 TeV, is one of the most well-motivated and untested DM candidates. Leveraging recent hydrodynamic cosmological simulations that give DM density profiles in Milky Way analog galaxies we show that the linelike gamma-ray signal predicted from Higgsino annihilation in the Galactic Center could be detected at high significance with the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO) for all but the most pessimistic DM profiles. We perform the most sensitive search to-date for the linelike signal using 15 years of data from the Fermi Large Area Telescope, coming within an order one factor of the necessary sensitivity to detect the Higgsino for some Milky Way analog DM density profiles. We show that H.E.S.S. has subleading sensitivity relative to Fermi for the Higgsino at present. In contrast, we analyze H.E.S.S. inner Galaxy data for the thermal wino model with a mass near 2.8 TeV; we find no evidence for a DM signal and exclude the wino by over a factor of two in cross section for all DM profiles considered. In the process, we identify and attempt to correct what appears to be an inconsistency in previous H.E.S.S. inner Galaxy analyses for DM annihilation related to the analysis effective area, which may weaken the DM cross-section sensitivity claimed in those works by around an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗