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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A comparative study of two numerical approaches for solving Kim–Kim–Suzuki phase-field models

Among the standard multi-phase multi-component phase-field (PF) methods, the Kim–Kim–Suzuki (KKS) method has the advantage of decoupling interfacial energy from bulk energy and solving concentration as the conserved variable. There are two approaches to numerically solving a KKS method: the global solution approach (GSA) solves all variables in a global system simultaneously, and the local solution approach (LSA) solves phase concentrations locally using a Newton solver. This work compares the performance of LSA and GSA for solving four KKS models of increasing complexity with the finite element method using the MOOSE framework. The solution accuracy, degrees of freedom (DOFs), memory usage, and computational efficiency are compared. We find that GSA and LSA generate similar solutions, with a maximum difference of only 0.34%. For each model, LSA has a lower number of DOFs, utilizes less memory, and less wall time. Additionally, the savings of memory and wall time in LSA increase with increasing mesh density of the same model and are more pronounced in models with higher dimensionality and more nodes. However, GSA is easier to implement in existing codes and can better solve highly nonlinear systems by utilizing sophisticated solvers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Atmospheric neutrinos in next-generation xenon and argon dark matter experiments

In this work, we study the sensitivity of future xenon- and argon-based dark matter and neutrino detection experiments to low-energy atmospheric neutrinos. Not accounting for experimental backgrounds, the primary obstacle for identifying nuclear recoils induced by atmospheric neutrinos in xenon is the tail of the electron recoil distribution due to pp solar neutrinos. We use the NEST code to model the solar and atmospheric neutrino signals in a xenon detector and find that an exposure of 700 tonne-years will produce a 5σ detection of atmospheric neutrinos. We explore the effect of different detector properties and find that a sufficiently long electron lifetime is essential to the success of such a measurement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Numerical simulation of coupled THM behaviour of full-scale EBS in backfilled experimental gallery in the Horonobe URL

Bentonite-based engineered barrier system (EBS) is a key component of many repository designs for the geological disposal of high-level radioactive waste. Given the complexity and interaction of the phenomena affecting the barrier system, coupled thermo-hydro-mechanical (THM) numerical analyses are a potentially useful tool for a better understanding of their behaviour. In this context, a Task (the Horonobe EBS experiment) was undertaken to study, using numerical analyses, the thermo-hydro-mechanical (and thermo-hydro) interactions in bentonite based engineered barriers within the international cooperative project DECOVALEX 2023. One full-scale in-situ experiment and four laboratory experiments, largely complementary, were selected for modelling. The Horonobe EBS experiment is a temperature-controlled non-isothermal experiment combined with artificial groundwater injection. The Horonobe EBS experiment consists of the heating and cooling phases. Six research teams performed the THM or TH (depended on research team approach) numerical analyses using a variety of computer codes, formulations and constitutive laws. Finally, for each experiment, the basic features of the analyses are described and the comparison between calculations and laboratory experiments and field observations are presented and discussed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

On the mechanical behavior of a Nb 3 Sn superconducting coil during a quench: Two-dimensional finite element analysis of a quench heater protected magnet

New high-field accelerator magnets based on $Nb_3Sn$ superconductors are pushing the boundaries of magnet design and quench protection towards new limits. While their large stored energies and current densities result in a very challenging scenario for magnet protection, their great electromagnetic forces create also new requirements in terms of magnet design and stress management techniques. Furthermore, the strain sensitivity of $Nb_3Sn$ cables turns the electro-mechanical limits of the conductor into a parameter of the highest importance, where conductor degradation becomes a critical aspect in magnet operation. The coupling of all the above-mentioned considerations during quench is a case of special interest that adds further complexity to the design of $Nb_3Sn$ magnets. The objective of this paper is to provide a complete two-dimensional investigation of the coil and magnet structure mechanics during a quench event. Here, the analysis is performed using a combination of finite element codes that provide the necessary input for the mechanical study. The core of the modelling strategy relies on a thermal-electric model, whose results are directly used as loads in the mechanical simulation. In doing so, the stress evolution during and after a quench is obtained. We focus for this time in the analysis of a quench heater protected magnet, where dynamic effects are of less importance in contrast to other protection systems like CLIQ.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Exploring the effect of ELM and code-coupling frequencies on plasma and material modeling of dynamic recycling in divertors

Abstract Integrated modeling of plasma-surface interactions provides a comprehensive and self-consistent description of the system, moving the field closer to developing predictive and design capabilities for plasma facing components. One such workflow, including descriptions for the scrape-off-layer plasma, ion-surface interactions and the sub-surface evolution, was previously used to address steady-state scenarios and has recently been extended to incorporate time-dependence and two-way information flow. The new model can address dynamic recycling in transient scenarios, such as the application presented in this paper: the evolution of W samples pre-damaged by helium and exposed to ELMy H-mode plasmas in the DIII-D DiMES. A first set of simulations explored the effect of ELM frequency. This study was discussed in detail in this conference’s proceedings and is summarized here. The 2nd set of simulations, which is the focus of this paper, explores the effect of code-coupling frequency. These simulations include initial SOLPS solutions converged to the inter-ELM state, ion impact energy ( E in ) and angles ( A in ) calculated by hPIC2, and an improved heat transfer description in Xolotl. The model predicts increases in particle fluxes and decreases in heat fluxes by 10%–20% with the coupling time-step. Compared with the first set of simulations, the less shallow impact angle leads to smaller reflection rates and significant D implantation. The higher fraction of implanted flux (and deeper), in particular during ELMs, increases the accumulated D content in the W near-surface region. Future expansion of the workflow includes coupling to hPIC2 and GITR to ensure accurate descriptions of E in and A in , and W impurity transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Multi-variable, Multi-value Hydrodynamic Modeling Approach to Support Marine Energy and Coastal Resilience Applications

Funded by the Marine Energy Seedling Laboratory Program, the study reported on here aimed to develop a multi-variable, multi-value hydrodynamic modeling approach based on the Finite-Volume Community Ocean Model (FVCOM) to support a wide range of marine renewable energy and coastal resilience applications. We migrated the marine hydrokinetic energy module to simulate tidal energy extraction by tidal turbines under wave-current interactions. We explored the particle tracking module in FVCOM and applied it to Sequim Bay, Washington, to illustrate the surface current field and mimic the pollutant transport near the surface. A tracer-water age module was added to FVCOM to examine water exchange through the entrance. Lastly, a user-specified vertical coordinate option was added to FVCOM to improve simulation of the effect of floating turbines on surface currents and scalar transport. The above-described model development has been tested using a simplified model domain and demonstrated in real-world coastal systems. The outcome of this project will provide a useful modeling code and approach for marine renewable energy and coastal resilience research and applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Versatile multi-energy hard x-ray camera to study confined and unconfined fast electron dynamics and anisotropies (Invited)

A powerful and flexible hard x-ray (HXR) camera has been recently installed and tested on the WEST tokamak (CEA, France) in collaboration with the Princeton Plasma Physics Laboratory. The diagnostic is a pinhole camera fielded with a 2D pixel detector equipped with a 1 mm thick CdTe sensor. The novelty of this diagnostic technique is the detector’s capability of adjusting the threshold energy at the pixel level. This innovation provides great flexibility in the energy configuration, allowing simultaneous space, energy, and time resolved x-ray measurements. The novel camera has been used to measure the core radiation from non-Maxwellian (fast) electrons accelerated by Lower Hybrid (LH) waves and also the beam–target emission of tungsten in the divertor region produced by fast electron losses interacting with the target. In addition, anisotropic hard x-ray emission has been detected for the first time at the WEST core and edge plasma, with opposite toroidal intensity trends. Finally, experimental vertical and toroidal HXR profiles have been successfully reproduced with the LH code LUKE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Evaluating Awkward Arrays, uproot, and coffea as a query platform for High Energy Physics Data

Query languages for High Energy Physics (HEP) are an ever present topic within the field. A query language that can efficiently represent the nested data structures that encode the statistical and physical meaning of HEP data will help analysts by ensuring their code is more clear and pertinent. As the result of a multi-year effort to develop an in-memory columnar representation of high energy physics data, the NumPy, Awkward Array, and uproot Python packages present a mature and efficient interface to HEP data. Atop that base, the coffea package adds functionality to launch queries at scale, manage and apply experiment-specific transformations to data, and present a rich object-oriented columnar data representation to the analyst. Recently, a set of Analysis Description Language (ADL) benchmarks has been established to compare HEP queries in multiple languages and frameworks. In this paper we present these benchmark queries implemented within the coffea framework and discuss their readability and performance characteristics. We find that the columnar queries perform as well or better than the implementations given in previous studies.

Gray, L.↗
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On numerical errors to the fields surrounding a relativistically moving particle in PIC codes

The particle-in-cell (PIC) method is widely used to model the self-consistent interaction between discrete particles and electromagnetic fields. It has been successfully applied to problems across plasma physics including plasma based acceleration, inertial confinement fusion, magnetically confined fusion, space physics, astrophysics, high energy density plasmas. In many cases the physics involves how relativistic particles (those with high relativistic γ factors) are generated and interact with plasmas. However, when relativistic particles stream across the grid, both in “vacuum” and in plasma, many numerical issues may arise which can lead to unphysical results. We present a detailed analysis of how discretized Maxwell solvers used in PIC codes can lead to numerical errors to the fields that surround particles that move at relativistic speeds across the grid. Expressions for the axial electric field as integrals in k space are presented that reveal two types of errors. The first arises from errors to the numerator of the integrand and leads to unphysical fields that are antisymmetric about the particle. Furthermore, the second arises from errors to the denominator of the integrand and lead to Cherenkov like radiation in “vacuum”. These fields are not anti-symmetric, extend behind the particle, and cause the particle to accelerate or decelerate depending on the solver and parameters. The unphysical fields are studied in detail for two representative solvers - the Yee solver and the FFT based solver. Although the Cherenkov fields are absent, the space charge fields are still present in the fundamental Brillouin zone for the FFT based solvers. In addition, the Cherenkov fields are present in higher order zones for the FFT based solvers. Comparison between the analytical solutions and PIC simulation results are presented. A solution for eliminating these unphysical fields by modifying the k operator in the axial direction is also presented. Using a customized finite difference solver, this solution was successfully implemented into OSIRIS [1]. Results from the customized solver are also presented. Additionally, this solution will be useful for a beam of particles that all move in one direction with a small angular divergence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Neural architecture codesign for fast physics applications

We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists of a global search stage that explores a wide range of architectures while considering hardware constraints, followed by a local search stage that fine-tunes and compresses the most promising candidates. We exceed performance on various tasks and show further speedup through model compression techniques such as quantization-aware-training and neural network pruning. We synthesize the optimal models to high level synthesis code for FPGA deployment with the hls4ml library. Additionally, our hierarchical search space provides greater flexibility in optimization, which can easily extend to other tasks and domains. We demonstrate this with two case studies: Bragg peak finding in materials science and jet classification in high energy physics, achieving models with improved accuracy, smaller latencies, or reduced resource utilization relative to the baseline models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Measured Performance of Over the Range Microwave Range Hoods

The California residential building code requires kitchen exhaust ventilation to protect indoor air quality. The requirement can be met with a kitchen exhaust fan or a range hood that conforms to airflow and sound specifications based on certified, standard test results. Appliances that integrate an exhaust fan with an over the range microwave (OTRs) are popular for their space saving utility; but for years there were none with the certified performance data required for building code compliance. This project, initiated during that period, aimed to evaluate OTR models found in new California homes and compare their performance to range hoods that minimally met code requirements. The study aimed to expand on limited information available about OTR performance with a particular focus on measuring the fraction of cooktop-generated air pollutants that are captured and removed by the exhaust devices, a parameter called “capture efficiency”. Airflow and capture efficiency (CE) were measured in a simulated kitchen in Berkeley Lab’s FLEXLAB facility. Airflows were measured using several variations of a balanced-pressure flow method (Walker et al. 2001) including a protocol that had been used in the California Healthy Efficient New Gas Homes (HENGH) field study. Measurements were made for six OTRs observed in the field study, including three with certified test results (published after the start of this project) for airflow and sound. Measurements were also made on two standard range hoods with comparable airflows and costs to the OTRs, when accounting for the microwave functionality. CE was measured using the CO 2 emitted from burners while heating pots of water (POW) as a tracer and calculating the ratio of added CO 2 in the exhaust flow over the total CO 2 generated from burning fuel. Results show that OTRs generally met the California code requirements for airflow, which are the same as those of the residential ventilation standard of ASHRAE. It was determined that the field protocol used in HENGH study homes was biased low by ~14% on average. The CE performance of OTRs tested in this study were consistent with those tested under controlled conditions in prior studies, showing CE increasing with airflow and being higher for emissions occurring at the back cooktop burner(s) compared with front burner emissions. The measured CE covered a range of 40% to 85% for the front burners and 60% to 100% for the back burners. The relationship of CE to airflow for OTRs was within the range of those found for standard range hoods in this study and prior studies, with the key caveat that OTRs appear to have more consistent CE performance for emissions on the front burner.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The gMOSS: the galaxy survey and galaxy populations of the large homogeneous field

ABSTRACT We present the gMOSS (Galaxies of Medium-band One-meter Schmidt telescope Survey) catalogue of ∼19 000 galaxies in 20 filters (4 broad-band SDSS and 16 medium-band filters). We observed 2.386 deg2 on the central part of the HS47.5-22 field with the 1-m Schmidt telescope of the Byurakan Astrophysical Observatory. The gMOSS is a complete flux-limited sample of galaxies with a threshold magnitude of r SDSS ≤ 22.5 AB. From photometric measurements with 16 medium-band filters and u SDSS, we get spectral energy distributions for each object in the field, which are used for further analysis. Galaxy classification and photometric redshift estimation based on spectral template matching with zebra software. The obtained redshift accuracy is σNMAD < 0.0043. Using the SED-fitting cigale code, we obtained the main properties of the stellar population of galaxies, such as rest-frame (u − r)res colour, stellar mass, extinction, and mass-weighted age with a precision of 0.16 ± 0.07 mag, 0.14 ± 0.04 dex, 0.27 ± 0.1 mag, and 0.08 ± 0.04 dex, respectively. Using a dust-corrected colour–mass diagram, we divided the full sample into populations of red and blue galaxies and considered the dependencies between stellar mass and age. Throughout cosmic time, red sequence galaxies remain older and more massive than blue cloud galaxies. The star formation history of a complete subsample of galaxies selected in the redshift range 0.05 ≤ z ≤ 0.015 with <$\mathrm{log} M \mathrm{\gt }_\mathrm{[M_\odot ]}$>8.3 shows an increase in the SFRD up to z ∼ 3, under the results obtained in earlier studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

DESI-253.2534+26.8843: A New Einstein Cross Spectroscopically Confirmed with Very Large Telescope/MUSE and Modeled with GIGA-Lens

Gravitational lensing provides unique insights into astrophysics and cosmology, including the determination of galaxy mass profiles and constraining cosmological parameters. We present spectroscopic confirmation and lens modeling of the strong lensing system DESI-253.2534+26.8843, discovered in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys data. This system consists of a massive elliptical galaxy surrounded by four blue images forming an Einstein Cross pattern. We obtained spectroscopic observations of this system using the Multi Unit Spectroscopic Explorer on ESO's Very Large Telescope and confirmed its lensing nature. The main lens, which is the elliptical galaxy, has a redshift of z L1 = 0.636 ± 0.001, while the spectra of the background source images are typical of a starburst galaxy and have a redshift of z s = 2.597 ± 0.001. Additionally, we identified a faint galaxy foreground of one of the lensed images, with a redshift of z L2 = 0.386. We employed the GIGA-Lens modeling code to characterize this system and determined the Einstein radius of the main lens to be ${\theta }_{{\rm{E}}}=2\buildrel{\prime\prime}\over{.} {520}_{-0.031}^{+0.032}$, which corresponds to a velocity dispersion of σ = 379 ± 2 km s –1 . Our study contributes to a growing catalog of this rare kind of strong lensing system and demonstrates the effectiveness of spectroscopic integral field unit observations and advanced modeling techniques in understanding the properties of these systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment - supporting data and code

This data package contains data, descriptions, and code-based analyses that were used to support conclusions drawn in “Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment”, by Freitas et al. (2025) (https://doi.org/10.1038/s41561-024-01614-y). The study evaluated greenhouse gas production along a deep sediment core (20 m) taken in 2018 from below Goldstream Lake, a field site approximately 15 km north of Fairbanks, Alaska.The file “ESSDive_NFreitas_2024_flmd.csv” includes an overview of all other csv files in this data package, namely: sediment descriptions (depth and type of sediment), sediment characterizations (bulk density, gravimetric water content, total carbon, etc.), calculated respiration and temperature sensitivity values associated with year-long incubations of the sediment core, and the R code used to process the dataset. Additional details regarding the content of these files and how the calculations were performed are described in the Methods section of this archive. The “data_dictionary_ESSDive_NFreitas_2024_dd.csv” is a data dictionary for all files included in the data package. Each row in the data dictionary represents a column name in a given file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).Data package updates- August 2024: Title was updated, R code now includes a process for calculating potential production across the whole sediment column, the "sediment_column_production_potentials_ESSDive_NFreitas_2024.csv" is the associated data output file, and the file level metadata and data dictionaries were updated to reflect the contents of the additional csv file.- October 2024: Title was updated, and a statement was added to Step 2 of the Methods that describes that samples were collected (and exported) in a responsible manner and in accordance with relevant permits and local laws.- January 2025: Updated the associated manuscript details in the Abstract (manuscript title, publication date, DOI link) and in the Related References section (full citation).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advancing our understanding of instabilities in high-energy-density systems through the study of benchmark datasets with advanced modeling tools

Numerous types of pulsed power driven inertial confinement fusion (ICF) and high energy density (HED) systems rely on implosion stability to achieve desired temperatures, pressures, and densities. Sandia National Laboratories Pulsed Power Sciences Center’s main ICF platform, Magnetized Liner Inertial Fusion (MagLIF), suffers from implosion instabilities which limit attainable fuel conditions and can compromise fuel confinement. This Truman Fellowship research primarily focused on computationally exploring (a) methods for improving our understanding of hydrodynamic and magnetohydrodynamic instabilities that form during cylindrical liner implosions, (b) methods for mitigating implosion instabilities, particularly those that degrade performance of MagLIF targets, and (c) novel MagLIF target designs intended to improve target performance primarily via enhanced implosion stability. Several multi-dimensional computational tools were used, including the magnetohydrodynamics code ALEGRA, the radiation-magnetohydrodynamics code HYDRA, and the magnetohydrodynamics code KRAKEN. This research succeeded in executing and analyzing simulations of automagnetizing liner implosions, shockless MagLIF implosions, dynamic screw pinch driven cylindrical liner implosions, and cylindrically convergent HED instability studies. The methods and tools explored and developed in this Truman Fellowship research have been published in several peer-reviewed journal articles and will serve as useful contributions to the fields of pulsed power science and engineering, particularly pertaining to pulsed power ICF and HED science.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗