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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 OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The FRIB Decay Station: New Horizons with Rare Isotopes

In May 2022, the Facility for Rare Isotope Beams (FRIB), located on the campus of Michigan State University (MSU), began delivering exotic isotopes to an international community of scientists. New discoveries are now being reported from radioactive decay of neutron-rich nuclei near N = 20 and N = 28.FRIB is expected to produce roughly 80% of the unstable or radioactive isotopes predicted to exist up to uranium (Z = 92). The new user facility is supported by the U.S. Department of Energy, and it is operated by MSU. A high-power superconducting linear accelerator, shaped like a paper-clip, drives the production of these rare isotopes by colliding stable nuclei moving at half the speed of light with a rotating, water-cooled graphite tar-get. These collisions cause the primary stable beam to fragment into a wide variety of unstable nuclei, which can be subsequently filtered through a multistage magnetic separator, the Advanced Rare Isotope Separator, and transported to one of several experimental stations. The FRIB Decay Station initiator (FDSi) (see Figure 1) was developed to enable comprehensive radio-active decay studies of the exotic nuclei produced by FRIB and it was used in the first two experiments in 2022. Further, the FDSi is a highly reconfigurable multidetector system with two focal planes (FP1 for discrete spectroscopy and FP2 for total absorption spectroscopy) that can be optimized for the specific science goals of each experiment. It is designed, built, and operated by a community of users with the sup-port of U.S. funding agencies, including the Department of Energy and National Science Foundation.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Exabiome: Advancing Microbial Science through Exascale Computing

The Exabiome project seeks to improve the understanding of microbiomes through the development of methods for accelerating metagenomic science using exascale computing. This article gives an overview of scientific impact of the three components of the project: metagenome assembly, protein family detection, and comparative analysis of metagenomes. Exabiome developed MetaHipMer, the only metagenome assembler capable of scaling to full exascale systems. MetaHipMer has enabled ground-breaking assemblies on the Frontier supercomputer, with many scientific benefits, such as the discovery of rare species and viral genomes. To investigate protein families, Exabiome developed two exascale tools, PASTIS and HipMCL. Together, these can utilize exascale resources to understand the functional diversity of billions of dark matter proteins and novel protein families. For comparative analysis, Exabiome developed kmerprof, a tool that can be used to compare huge metagenomes for many different scientific purposes, for example, grouping human microbiomes according to body location.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Magnetospheric energetic ions from the earth's ionosphere

In the decade and a half since the initial discovery that the earth's own ionosphere could at times contribute measurably to the hot plasma in the magnetosphere, significant progress has been made in both current knowledge and understanding of this connection. It is now known that ions of ionospheric origin are found in all major regions of the magnetosphere and at its boundaries. The source region in the ionosphere and the acceleration and transport processes involved in coupling the cold ionospheric plasma to the hot magnetospheric plasma are complex and variable. The large scale morphology of the ionospheric outflow and its distribution throughout the magnetosphere is now understood and progress is being made in the understanding of the fundamental physical processes involved. In this paper attention is given to the large scale morphology and current understanding of the sources for ionospheric ions found in various regions of the magnetosphere and their transport.

Shelley, E. G.↗
DOE OSTI2023

The high energy X-ray probe (HEX-P): Galactic PeVatrons, star clusters, superbubbles, microquasar jets, and gamma-ray binaries

HEX-P is a probe-class mission concept that will combine high spatial resolution X-ray imaging (<10" FWHM) and broad spectral coverage (0.2–80 keV) with an effective area far superior to current facilities (including XMM-Newton and NuSTAR) to enable revolutionary new insights into a variety of important astrophysical problems. With the recent discoveries of over 40 ultra-high-energy gamma-ray sources (detected above 100 TeV) and neutrino emission in the Galactic Plane, we have entered a new era of multi-messenger astrophysics facing the exciting reality of Galactic PeVatrons. In the next decade, as more Galactic PeVatrons and TeV gamma-ray sources are expected to be discovered, the identification of their acceleration and emission mechanisms will be the most pressing issue in both particle and high-energy astrophysics. In this paper, along with its companion papers, we will present that HEX-P is uniquely suited to address important problems in various cosmic-ray accelerators, including Galactic PeVatrons, through investigating synchrotron X-ray emission of TeV–PeV electrons produced by both leptonic and hadronic processes. For Galactic PeVatron candidates and other TeV gamma-ray sources, HEX-P can fill in a large gap in the spectral-energy distributions (SEDs) of many objects observed in radio, soft X-rays, and gamma rays, constraining the maximum energies to which electrons can be accelerated, with implications for the nature of the Galactic PeVatrons and their contributions to the spectrum of Galactic cosmic rays beyond the knee at ~3 PeV. In particular, X-ray observation with HEX-P and TeV observation with CTAO will provide the most powerful multi-messenger diagnostics to identify Galactic PeVatrons and explore a variety of astrophysical shock mechanisms. We present simulations of each class of Galactic TeV–PeV sources, demonstrating the power of both the imaging and spectral capabilities of HEX-P to advance our knowledge of Galactic cosmic-ray accelerators. In addition, we discuss HEX-P’s unique and complementary roles to upcoming gamma-ray and neutrino observatories in the 2030s.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The high energy X-ray probe (HEX-P): Galactic PeVatrons, star clusters, superbubbles, microquasar jets, and gamma-ray binaries

HEX-P is a probe-class mission concept that will combine high spatial resolution X-ray imaging (<10" FWHM) and broad spectral coverage (0.2–80 keV) with an effective area far superior to current facilities (including XMM-Newton and NuSTAR) to enable revolutionary new insights into a variety of important astrophysical problems. With the recent discoveries of over 40 ultra-high-energy gamma-ray sources (detected above 100 TeV) and neutrino emission in the Galactic Plane, we have entered a new era of multi-messenger astrophysics facing the exciting reality of Galactic PeVatrons. In the next decade, as more Galactic PeVatrons and TeV gamma-ray sources are expected to be discovered, the identification of their acceleration and emission mechanisms will be the most pressing issue in both particle and high-energy astrophysics. In this paper, along with its companion papers, we will present that HEX-P is uniquely suited to address important problems in various cosmic-ray accelerators, including Galactic PeVatrons, through investigating synchrotron X-ray emission of TeV–PeV electrons produced by both leptonic and hadronic processes. For Galactic PeVatron candidates and other TeV gamma-ray sources, HEX-P can fill in a large gap in the spectral-energy distributions (SEDs) of many objects observed in radio, soft X-rays, and gamma rays, constraining the maximum energies to which electrons can be accelerated, with implications for the nature of the Galactic PeVatrons and their contributions to the spectrum of Galactic cosmic rays beyond the knee at ~3 PeV. In particular, X-ray observation with HEX-P and TeV observation with CTAO will provide the most powerful multi-messenger diagnostics to identify Galactic PeVatrons and explore a variety of astrophysical shock mechanisms. We present simulations of each class of Galactic TeV–PeV sources, demonstrating the power of both the imaging and spectral capabilities of HEX-P to advance our knowledge of Galactic cosmic-ray accelerators. In addition, we discuss HEX-P’s unique and complementary roles to upcoming gamma-ray and neutrino observatories in the 2030s.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2023

The High Energy X-Ray Probe (HEX-P): Galactic PeVatrons, Star Clusters, Superbubbles, Microquasar Jets, and Gamma-Ray Binaries

HEX-P is a probe-class mission concept that will combine high spatial resolution X-ray imaging (<10"FWHM) and broad spectral coverage (0.2--80 keV) with an effective area far superior to current facilities (including XMM-Newton and NuSTAR) to enable revolutionary new insights into a variety of important astrophysical problems. With the recent discoveries of over 40 ultra-high-energy gamma-ray sources (detected above 100 TeV) and neutrino emission in the Galactic Plane, we have entered a new era of multi-messenger astrophysics facing the exciting reality of Galactic PeVatrons. In the next decade, as more Galactic PeVatrons and TeV gamma-ray sources are expected to be discovered, the identification of their acceleration and emission mechanisms will be the most pressing issue in both particle and high-energy astrophysics. In this paper, along with its companion papers (Reynolds et al. 2023, Mori et al. 2023), we will present that HEX-P is uniquely suited to address important problems in various cosmic-ray accelerators, including Galactic PeVatrons, through investigating synchrotron X-ray emission of TeV--PeV electrons produced by both leptonic and hadronic processes. For Galactic PeVatron candidates and other TeV gamma-ray sources, HEX-P can fill in a large gap in the spectral-energy distributions (SEDs) of many objects observed in radio, soft X-rays, and gamma rays, constraining the maximum energies to which electrons can be accelerated, with implications for the nature of the Galactic PeVatrons and their contributions to the spectrum of Galactic cosmic rays beyond the knee at $\sim3$ PeV. In particular, X-ray observation with HEX-P and TeV observation with CTA will provide the most powerful multi-messenger diagnostics to identify Galactic PeVatrons and explore a variety of astrophysical shock mechanisms. We present simulations of each class of Galactic TeV--PeV sources, demonstrating the power of both the imaging and spectral capabilities of HEX-P to advance our knowledge of Galactic cosmic-ray accelerators. In addition, we discuss HEX-P’s unique and complementary roles to upcoming gamma-ray and neutrino observatories in the 2030s.

particle accelerators↗
DOE OSTI2024

Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery

Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

2020 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is essential for addressing the increasingly complex national workforce demands stemming from the growth of computational science and engineering challenges. Computational science and engineering (CSE) takes a multidisciplinary approach that utilizes scientific computing to tackle practical problems and provide technical tools across the spectrum of scientific discovery. The DOE CSGF specifically highlights high-performance computing (HPC) as a critical enabling technology in CSE, driving advancements in science and engineering that are vital to both the DOE and the broader economy. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines have been transformed through the augmentation of scientific observation via HPC. At government laboratories, academic institutions, and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, quantum information systems, fusion-reactor design, machine learning, additive manufacturing, nano materials for next-generation batteries and transistors, and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing. Computational biology, machine learning, and quantum computing are among the subjects that began to swell in the ranks of CSGF applicants before the labs were hiring as high a percentage of employees in these categories.” The explosion of scientific and technological data has heightened the demand for advanced high-performance computing (HPC) to transform these data into meaningful scientific insights. As access to vast amounts of data increases, the fields of Machine Learning and Artificial Intelligence are experiencing a resurgence, enhancing the established practices of computational modeling and simulation. In its September 2020 subcommittee report on "AI/ML, Data Intensive Science, and High-Performance Computing," the DOE Advanced Scientific Computing Advisory Committee (ASCAC) specifically called for a fellowship program to train computational and data scientists to address exascale and data-intensive computing challenges. This integration of empirical and theoretical modeling will increasingly guide federal policymakers in making decisions that impact American society and future generations. It demands a workforce of highly skilled and intellectually agile computational scientists capable of navigating the rapid advancements in scientific computing within the DOE National Laboratory research environment. The DOE CSGF program has consistently addressed this critical need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Future Circular Collider Feasibility Study Report

In response to the 2020 Update of the European Strategy for Particle Physics , the Future Circular Collider (FCC) Feasibility Study was launched as an international collaboration hosted by CERN. This report describes the FCC integrated programme , which consists of two stages: an electron-positron collider (FCC-ee) in the first phase, serving as a high-luminosity Higgs, top, and electroweak factory; followed by a proton-proton collider (FCC-hh) at the energy frontier in the second phase. The FCC-ee is designed to operate at four key centre-of-mass energies: the Z pole, the WW pair production threshold, the ZH production peak, and the top/anti-top production threshold—each delivering the highest possible luminosities to four experiments. Over 15 years of operation, FCC-ee will produce more than 6 trillion Z bosons, 200 million WW pairs, nearly 3 million Higgs bosons, and 2 million top anti-top pairs. Precise energy calibration at the Z pole and WW threshold will be achieved through frequent resonant depolarisation of pilot bunches. The sequence of operation modes between the Z, WW, and ZH substages remains flexible. The FCC-hh will operate at a centre-of-mass energy of approximately 85 TeV—nearly an order of magnitude higher than the LHC—and is designed to deliver 5 to 10 times the integrated luminosity of the upcoming High-Luminosity LHC. Its mass reach for direct discovery extends to several tens of TeV. In addition to proton-proton collisions, the FCC-hh is capable of supporting ion-ion, ion-proton, and lepton-hadron collision modes. This second volume of the Feasibility Study Report presents the complete design of the FCC-ee collider, its operation and staging strategy, the full-energy booster and injector complex, required accelerator technologies, safety concepts, and technical infrastructure. It also includes the design of the FCC-hh hadron collider, development of high-field magnets, hadron injector options, and key technical systems for FCC-hh.

Benedikt, M. [European Organization for Nuclear Re↗
NASA NTRS1984

Activity of calcium activated protease in skeletal muscles and its changes in atrophy and stretch

The reduction of protein content in skeletal muscle undergoing disuse-induced atrophy is correlated with accelerated rates of protein degradation and reduced rates of protein synthesis (Goldspink, 1977). It is not known in what manner myofibers are partially disassembled during disuse atrophy to fibers of smaller diameter; nor is it known which proteases are responsible for this morphological change in contractile protein mass. Dayton and colleagues (1975) have suggested that the Ca(2+)-activated protease (CaP) may initiate myofibril degradation. The discovery of a form of CaP that is activatable by nano-molar concentrations of Ca(2+) indicates that CaP activity may be regulated by physiological concentrations of Ca(2+) (Mellgren, 1980). The enhancement of proteolysis by the Ca(2+) ionophore A23187, reported by Etlinger (1979), is consistent with a significant role for CaP in protein degradation. It was of interest, therefore, to measure the levels of CaP activity and the CaP inhibitor in extracts obtained from skeletal muscles of rat and chicken limbs undergoing disuse atrophy or stretch hypertrophy, respectively.

Ellis, S.↗
NASA NTRS1989

A 110-ms pulsar, with negative period derivative, in the globular cluster M15

The discovery of a 110-ms pulsar, PSR2127+11, in the globular cluster M15, is reported. The results of nine months of timing measurements place the new pulsar about 2 arcsec from the center of the cluster, and indicate that it is not a member of a close binary system. The measured negative value of the period derivative is probably the result of the pulsar being bodily accelerated in our direction by the gravitational field of the collapsed core of M15. This apparently overwhelms a positive contribution to the period derivative due to magnetic braking. Although the pulsar has an unexpectedly long period, it is argued that it belongs to the class of 'recycled' pulsars, which have been spun up by accretion in a binary system. The subsequent loss of the pulsar's companion is probably due to disruption of the system by close encounters with other stars.

Wolszczan, A.↗
NASA NTRS2015

Characterization of the Inner Knot of the Crab: the Site of the Gamma-ray Flares?

One of the most intriguing recent discoveries has been the detection of powerful gamma-ray flares from the Crab Nebula. Such events, with a recurrence time of about once per year, can be so dramatic to make the system the brightest source in the gamma‐ray sky as occurred, e.g. in April 2011. These flares challenge our understanding of how pulsar wind nebulae work and defy current astrophysical models for particle acceleration. We present here our study of the inner knot located within a fraction of an arcsecond from the pulsar with the aim of characterizing the feature and asking if this might be the site of the origin of the gamma-ray flares. We took data using Keck, HST, and Chandra obtained as part of our multi‐wavelength campaign to identify the source of the enigmatic flares. We set an upper limit as to the gamma-ray flux from the knot. We also find that the dimensions, surface brightness, flux, etc. of the optical and infrared knot are all correlated with distance from the pulsar. This distance, in turn, varies with time. In addition to this most thorough characterization of the inner knot's properties, we examine the hypothesis that the knot may be the site of the flares by examining the knot separation versus the Fermi/LAT gamma-ray flux. Finally, as part of this research, we make use of a new approach employing singular value decomposition (SVD) for analyzing time series of images and compare the approach to more traditional methods. Our conclusions are only refined but not impacted by using the new approach.

Gamma-ray Flares↗
DOE OSTI2020

Simulation of MHD instabilities with fluid runaway electron model in M3D- C 1

Runaway electrons are generated in a tokamak during the start up, during normal operation and during a plasma disruption. During a disruption, runaway electrons can be accelerated to high energies, potentially damaging the first wall. To predict the consequences of runaway generation during a disruption, it is necessary to consider resonant interactions of runaways with the bulk plasma. Here we consider the interactions of runaways on low mode number tearing modes. We have developed a fluid runaway electron model for the 3D MHD code M3D-C1[Jardin,et al. J Comput. Sci Discovery 6 014002 (2012)]. To benchmark, we have reproduced the MHD linear tearing mode results (with runaway electrons) in a circular cylinder presented in previous analytic studies[[Helander, P., et al, Phys. Plasmas 14 144102 (2007)] and extended here with a numerical eigenvalue calculation. Furthermore, we find that the low mode number tearing mode has a rotation caused by the MHD - runaways interaction, and the toroidal current scale length is much smaller with runaways than that for without and decreases as the runaway speed increases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗