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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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DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

NASA NTRS1992

Analysis of crustal structure of Venus utilizing residual Line-of-Sight (LOS) gravity acceleration and surface topography data. A trial of global modeling of Venus gravity field using harmonic spline method

To construct Venus' gravity disturbance field (or gravity anomaly) with the spacecraft-observer line of site (LOS) acceleration perturbation data, both a global and a local approach can be used. The global approach, e.g., spherical harmonic coefficients, and the local approach, e.g., the integral operator method, based on geodetic techniques are generally not the same, so that they must be used separately for mapping long wavelength features and short wavelength features. Harmonic spline, as an interpolation and extrapolation technique, is intrinsically flexible to both global and local mapping of a potential field. Theoretically, it preserves the information of the potential field up to the bound by sampling theorem regardless of whether it is global or local mapping, and is never bothered with truncation errors. The improvement of harmonic spline methodology for global mapping is reported. New basis functions, a singular value decomposition (SVD) based modification to Parker & Shure's numerical procedure, and preliminary results are presented.

Fang, Ming↗

NASA NTRS2009

Insights into the Galactic Cosmic-ray Source from the TIGER Experiment

We report results from 50 days of data accumulated in two Antarctic flights of the Trans-Iron Galactic Element Recorder (TIGER). With a detector system composed of scintillators, Cherenkov detectors, and scintillating optical fibers, TIGER has a geometrical acceptance of 1.7 sq m sr and a charge resolution of 0.23 cu at Iron. TIGER has obtained abundance measurements of some of the rare galactic cosmic rays heavier than iron, including Zn, Ga, Ge, Se, and Sr, as well as the more abundant lighter elements (down to Si). The heavy elements have long been recognized as important probes of the nature of the galactic cosmic-ray source and accelerator. After accounting for fragmentation of cosmic-ray nuclei as they propagate through the Galaxy and the atmosphere above the detector system, the TIGER source abundances are consistent with a source that is a mixture of about 20% ejecta from massive stars and 80% interstellar medium with solar system composition. This result supports a model of cosmic-ray origin in OB associations previously inferred from ACE-CRIS data of more abundant lighter elements. These TIGER data also support a cosmic-ray acceleration model in which elements present in interstellar grains are accelerated preferentially compared with those found in interstellar gas.

Link, Jason T.↗

DOE OSTI2020

Electron acceleration in laboratory-produced turbulent collisionless shocks

Astrophysical collisionless shocks are among the most powerful particle accelerators in the Universe. Generated by violent interactions of supersonic plasma flows with the interstellar medium, supernova remnant shocks are observed to amplify magnetic fields and accelerate electrons and protons to highly relativistic speeds. In the well-established model of diffusive shock acceleration, relativistic particles are accelerated by repeated shock crossings. However, this requires a separate mechanism that pre-accelerates particles to enable shock crossing. This is known as the ‘injection problem’, which is particularly relevant for electrons, and remains one of the most important puzzles in shock acceleration. In most astrophysical shocks, the details of the shock structure cannot be directly resolved, making it challenging to identify the injection mechanism. In this letter we report results from laser-driven plasma flow experiments, and related simulations, that probe the formation of turbulent collisionless shocks in conditions relevant to young supernova remnants. We show that electrons can be effectively accelerated in a first-order Fermi process by small-scale turbulence produced within the shock transition to relativistic non-thermal energies, helping overcome the injection problem. Our observations provide new insight into electron injection at shocks and open the way for controlled laboratory studies of the physics underlying cosmic accelerators.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗

DOE OSTI2025

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.

aerosol chemistry dynamics↗

NASA NTRS1991

Regolith-structure modeling

Early lunar missions have provided a basic understanding of the physical and strength properties of lunar regolith, which have been shown to differ from those of dry terrestrial granular soils. Lunar regolith is predominantly a fine sand of which nearly 40 percent can be characterized as silt with a particle size smaller than 100 micrometers. The top 10 to 20 cm of the regolith can be characterized as being in a loose to medium-loose state. The density of the regolith, however, rapidly increases below a depth of 20 cm. The highly irregular and angular shapes of the regolith particles tend to interlock and create relatively strong mechanical bonds that give the particulate mass substantial cohesive properties and smaller amounts of tensile strength properties. In addition, the friction angle of lunar regolith at medium to high densities is quite high and often exceeds 55 degrees. These known properties of lunar regolith have been matched in a terrestrially-manufactured analog known as Minnesota Lunar Simulant. A variety of experiments were conducted using this simulant to both verify existing information and generate new information on the physical and constitutive properties of lunar regolith. These experiments include maximum and minimum density determinations, specific mass of solids, grain-size distribution, conventional triaxial compression and extension, isotropic compression, one-dimensional compression, direct shear, and direct tension. Direct shear experiments were conducted under atmospheric and vacuum conditions. Results of the physical and strength experiments compare closely to results obtained from lunar missions. Results of simulant strength experiments performed in vacuum indicated no observable difference from results obtained in air. A test bed currently under study is one involving a regolith shield covering a first-generation human habitat module. It is understood that regolith in depths ranging from 3 to 5 meters is required for radiation shielding for habitation and workspace. The habitat module is treated as a rigid cylindrical tube with a smooth exterior. By making the cylinder rigid, a complex interaction problem is reduced to a situation where we can consider the support regolith and the shielding regolith as behaving independently of the structural properties of the cylindrical structure. Medium-dense lunar simulant was placed around a scaled model of the habitat module to provide a radiation shield. This embankment-type shield was constructed in relatively thin but fine layers by compacting, by mechanical vibratory means, layer upon layer of simulant placed adjacent to the horizontally-aligned cylinder. The slope angles were constructed at 55 degrees. The model described above was studied in a geotechnical centrifuge, which allows for the scaling of model dimensions to prototype dimensions by increasing the acceleration of gravity on the model. The deformation response can be scaled up to prototype dimensions to provide an assessment of the deformation patterns of the lunar structure. The actual process of local and/or global growth of instabilities or skip planes can also be observed.

Ko, Hon-Yim↗

NASA NTRSDate not supplied

The Containment Assurance Risk Framework of the Mars Sample Return Program

The Mars Sample Return campaign aims at bringing rock and atmospheric samples from Mars to Earth through a series of robotic missions. These missions would collect the samples being cached and deposited on Martian soil by the Perseverance rover, place them in a container, and launch them into Martian orbit for subsequent capture by an orbiter that would bring them back. Given there exists a non-zero probability that the samples contain biological material, precautions are being taken to design systems that would break the chain of contact between Mars and Earth. These include techniques such as sterilization of Martian particles, redundant containment vessels, and a robust reentry capsule capable of accurate landings without a parachute. Requirements exist that the probability of containment not assured of Martian-contaminated material into Earth’s biosphere be less than one in a million. To demonstrate compliance with this strict requirement, a statistical framework was developed to assess the likelihood of containment loss during each sample return phase and make a statement about the total combined mission probability of containment not assured. The work presented here describes this framework, which considers failure modes or fault conditions that can initiate failure sequences ultimately leading to containment not assured. Reliability estimates are generated from databases, design heritage, component specifications, or expert opinion in the form of probability density functions or point estimates and provided as inputs to the mathematical models that simulate the different failure sequences. The probabilistic outputs are then combined following the logic of several fault trees to compute the ultimate probability of containment not assured. Given the multidisciplinary nature of the problem and the different types of mathematical models used, the statistical tools needed for analysis are required to be computationally efficient. While standard Monte Carlo approaches are used for fast models, a multi-fidelity approach to rare event probabilities is proposed for expensive models. In this paradigm, inexpensive low-fidelity models are developed for computational acceleration purposes while the expensive high-fidelity model is kept in the loop to retain accuracy in the results. This work presents an example of end-to-end application of this framework highlighting the computational benefits of a multi-fidelity approach.

Giuseppe Cataldo↗

NASA NTRS2003

Phenomenological Model of Current Sheet Canting in Pulsed Electromagnetic Accelerators

The phenomenon of current sheet canting in pulsed electromagnetic accelerators is the departure of the plasma sheet (that carries the current) from a plane that is perpendicular to the electrodes to one that is skewed, or tipped. Review of pulsed electromagnetic accelerator literature reveals that current sheet canting is a ubiquitous phenomenon - occurring in all of the standard accelerator geometries. Developing an understanding of current sheet canting is important because it can detract from the propellant sweeping capabilities of current sheets and, hence, negatively impact the overall efficiency of pulsed electromagnetic accelerators. In the present study, it is postulated that depletion of plasma near the anode, which results from axial density gradient induced diamagnetic drift, occurs during the early stages of the discharge, creating a density gradient normal to the anode, with a characteristic length on the order of the ion skin depth. Rapid penetration of the magnetic field through this region ensues, due to the Hall effect, leading to a canted current front ahead of the initial current conduction channel. In this model, once the current sheet reaches appreciable speeds, entrainment of stationary propellant replenishes plasma in the anode region, inhibiting further Hall-convective transport of the magnetic field; however, the previously established tilted current sheet remains at a fairly constant canting angle for the remainder of the discharge cycle, exerting a transverse J x B force which drives plasma toward the cathode and accumulates it there. This proposed sequence of events has been incorporated into a phenomenological model. The model predicts that canting can be reduced by using low atomic mass propellants with high propellant loading number density; the model results are shown to give qualitative agreement with experimentally measured canting angle mass dependence trends.

Markusic, Thomas↗

DOE OSTI2023

TeraChem protocol buffers ( TCPB ): Accelerating QM and QM/MM simulations with a client–server model

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. Here, we describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google’s protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ~250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI - )—a model chromophore in green fluorescent proteins—on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Evaluation of Composite Airframe Dynamic Impact Modeling Using Hawker 4000 Fuselage Drop Test Data

Two drop tests of partial Hawker 4000 fuselage sections were conducted at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) to characterize the response of representative composite aerospace structure to dynamic impact loads. Test conditions were selected to induce damage into the composite structure in order to study material failure within a composite fuselage and evaluate the capability of finite element (FE) model analysis to predict that failure. The tests were simulated using FE models which were generated to isolate the effect of developmental data availability on predictive capability. FE Models of the tested fuselage sections were generated using two limited data sets. The first model configuration was reverse engineered from the test article with no information related to design or fabrication details which would be known only by the manufacturer. The second model was generated from data provided by the manufacturer but without additional material characterization test data. Models were developed using these methodologies for both fuselage sections tested. Correlation of each model to the tests conducted was evaluated in terms of damage, deformation, and cabin acceleration measurements. Correlation between the developed models and the tested fuselage sections showed that the reverse engineered model predicted the composite damage and cabin acceleration measured during test though it was limited due to lack of detail in the composite layup changes through the structure. The model developed using manufacturer specifications did not predict damage, due to limited material and component model characterization data, but it did predict acceleration on par with the reverse engineered model. Model capability and limitation sources identified were verified through correlation of a final model which was developed by combining the individual data sets. The combined model demonstrated that the addition of calibrated composite material models to accurate composite layup definitions and detailed geometry led to improved correlation of damage and acceleration response within the composite fuselage structures.

Crashworthiness↗

NASA NTRSDate not supplied

Evaluation of Composite Airframe Dynamic Impact Modeling Using Hawker 4000 Fuselage Drop Test Data

Crashworthiness↗

NASA NTRS1975

The 10 sheath-accelerated electrons and ions

A model is presented that suggests that plasma sheaths form between the ionospheric plasma moving with Io and the ambient plasma corotating with Jupiter. Potentials across these sheaths could be as high as 580 kV which is the motional emf across Io's ionosphere. Electrons and ions can be accelerated across these sheaths. The sheaths may exist at the top of the Io ionosphere with characteristic thicknesses of 1/4 kilometers. The model is consistent with the Pioneer observations of 0.15 MeV electrons at the inner edge of Io's L-shell and the enhanced number density of low-energy protons at the outer edge. Ion sputtering of the Io surface is discussed and may explain the presence of atomic hydrogen and sodium in the vicinity of Io. Also these accelerated particles may be important to the formation of the Io ionosphere. High electron flux which may lead to decametric radio emissions, Jovian atmospheric heating and optical and X-ray emissions is also discussed.

Shawhan, S. D.↗

NASA NTRS1985

Cosmic ray acceleration by stellar wind. Simulation for heliosphere

The solar wind deceleration by the interstellar medium may result in the existence of the solar wind terminal shock. In this case a certain fraction of thermal particles after being heated at the shock would obtain enough energy to be injected to the regular acceleration process. An analytical solution for the spectrum in the frame of a simplified model that includes particle acceleration at the shock front and adiabatic cooling inside the stellar wind cavity has been derived. It is shown that the acceleration of the solar wind particles at the solar wind terminal shock is capable of providing the total flux, spectrum and radial gradients of the low-energy protons close to one observed in the interplanetary space.

Petukhov, S. I.↗

DOE OSTI2024

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗

DOE OSTI2021

Investigating high-energy proton-induced reactions on spherical nuclei: Implications for the preequilibrium exciton model

A number of accelerator-based isotope production facilities utilize $100-200$ MeV proton beams due to the high production rates enabled by high-intensity beam capabilities and the greater diversity of isotope production brought on by the long range of high-energy protons. However, nuclear reaction modeling at these energies can be challenging because of the interplay between different reaction modes and a lack of existing guiding cross section data. A Tri-lab collaboration has been formed between the Lawrence Berkeley, Los Alamos, and Brookhaven National Laboratories to address these complexities by characterizing charged-particle nuclear reactions relevant to the production of established and novel radioisotopes. In the inaugural collaboration experiments, stacked-targets of niobium foils were irradiated at the Brookhaven Linac Isotope Producer ($E_p=200$ MeV) and the Los Alamos Isotope Production Facility ($E_p=100$ MeV) to measure $^{93}$Nb(p,x) cross sections between $50-200$ MeV. The results were compared with literature data as well as the default calculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. The default code predictions largely failed to reproduce the measurements. Therefore, we developed a standardized procedure, which determines the reaction model parameters that best reproduce the most prominent reaction channels in a physically justifiable manner. Overall, the primary focus of the procedure was to determine the best parameterization for the pre-equilibrium two-component exciton model. This modeling study revealed a trend towards a relative decrease for internal transition rates at intermediate proton energies ($E_p=20-60$ MeV) in the current exciton model as compared to the default values. The results of this work are instrumental for the planning, execution, and analysis essential to isotope production.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1973

Io-accelerated electrons - Predictions for Pioneer 10 and Pioneer 11

Based on a model in which electrons are accelerated to energies of 100 kiloelectron volts through sheaths associated with Io, predictions are made about energetic electrons to be observed by Pioneer 10 and Pioneer 11 in the Jovian magnetosphere. This energetic electron source may be distinguishable from the solar wind diffusion source by the radial flux profile and by the characteristic electron energies.

Shawhan, S. D.↗