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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 127 records · Page 7

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DOE OSTI2025

Extending JuTrack’s capabilities to the FRIB accelerator to enhance online modeling

JuTrack is a Julia-based accelerator modeling and tracking package that utilizes compiler-level automatic differentiation (AD) to enable fast and accurate derivative calculations. While JuTrack provides a solid foundation for beam dynamics simulations, its capabilities must be extended to support the Facility for Rare Isotopes (FRIB) linac. This includes modeling heavy-ion linac accelerator components such as the liquid-lithium charge stripper, which facilitates efficient acceleration by remove electrons from heavy isotopes, and incorporating multi-charge state acceleration tracking, which allows for charge-dependent beam dynamics. These extensions address challenges such as the beam matching and optimization of multi charge state through various accelerating structures and beam-material interaction modeling while maintaining the auto differentiation capability. This work focuses on adapting JuTrack to incorporate these elements, enhancing its online modeling abilities. We present modifications to JuTrack’s framework and demonstrate their performance in FRIB simulations.

Accelerator Physics↗

NASA NTRS1993

Simulated orbiter lift-off acceleration - A stochastic model

A simple procedure for generating synthetic orbiter liftoff acceleration is described. This stochastic method is based on a filtered white noise model which accounts for the nonstationary time evolution of amplitude and frequency content of orbiter lift-off accelerations. The example of Space Transportation System STS-41 Z-acceleration is carefully examined, and the corresponding synthetic orbiter lift-off accelerations model was developed. It is shown that the resulting simulated accelerations preserve significant properties of the original record. Statistically based acceleration response spectra curves for the simulated orbiter lift-off excitations are also evaluated and the results are compared with those for the actual flight data.

Lee-Glauser, Gina↗

DOE OSTI2024

The Integrated Virtual Blast Furnace: Enabling Physics-Based Operational Guidance

As part of a DOE-supported research effort, Purdue University Northwest researchers are collaborating with Oak Ridge National Laboratory and United States Steel Corporation to develop a tool to provide blast furnace operators and engineers with process performance insight comparable to high-fidelity computational fluid dynamics modeling, accelerated to provide “what-if” scenarios at near-real-time speed. This is accomplished by pre-simulating a baseline case and a range of potential operating scenarios to establish how the furnace responds to changing inputs, then training a neural network-based Reduced Order Model to accelerate the speed at which predictions of key parameters can be generated.

Okosun, Tyamo↗

NASA NTRS1979

Plasma radiation diagnostics of the primary energy release region in solar flares

The possibility is investigated that the plasma turbulence used in many recent models of the primary energy release and acceleration in solar flares should be detectable by radiation near the fundamental and second harmonic of the plasma frequency. Formulae are derived for fundamental emission due to the combination of ion-acoustic and Langmuir plasma turbulence and for second harmonic emission due to the combination of two Langmuir waves. These results are applied to recent primary energy release and acceleration models which shows that either such radiation should be detectable and possibly distinguishable with suitable microwave interferometers or that its absence places fairly stringent constraints on the possible level of Langmuir or Langmuir and ion-acoustic waves in these models.

Smith, D. F.↗

NASA NTRS1986

Modeling of ion acceleration through drift and diffusion at interplanetary shocks

A test particle simulation designed to model ion acceleration through drift and diffusion at interplanetary shocks is described. The technique consists of integrating along exact particle orbits in a system where the angle between the shock normal and mean upstream magnetic field, the level of magnetic fluctuations, and the energy of injected particles can assume a range of values. The technique makes it possible to study time-dependent shock acceleration under conditions not amenable to analytical techniques. To illustrate the capability of the numerical model, proton acceleration was considered under conditions appropriate for interplanetary shocks at 1 AU, including large-amplitude transverse magnetic fluctuations derived from power spectra of both ambient and shock-associated MHD waves.

Decker, R. B.↗

NASA NTRS1976

Behavior of low-energy protons and alpha particles during a disturbed time period

The paper presents observations of 130- to 1200-keV protons and 40- to 420-keV/nucleon alpha particles made on the earth-orbiting spacecraft Imp 8 and Imp 7 during an active solar period in September 1974, concentrating, in particular, on an energetic storm particle (ESP) event observed in association with an interplanetary shock wave on September 21. It is found that the observed variations in the proton-to-alpha particle ratios and spectral indices can be explained either by 'pileup' or by acceleration models of ESP events. Several instances of local acceleration of particles in the near-earth environment are also discussed.

Ipavich, F. M.↗

NASA NTRS1984

Kelvin wave packets and flow acceleration - A comparison of modeling and observations

Atmospheric Kelvin waves, as revealed by temperatures obtained from the recent Limb Infrared Monitor of the Stratosphere (LIMS) experiment, commonly occur in packets. A simple two-dimensional gravity-wave model is used to study the upward propagation of these packets through different zonal mean wind profiles derived from the LIMS data. The observed prevalence of high frequency waves in the lower mesosphere and low frequency waves in the lower stratosphere can be exlained by dispersion of energy associated with the range of frequencies comprising a packet. Dominant wave frequencies at upper and lower levels are more distinctly separated if the packet propagates through a layer of westerly winds. Due to dispersion and shear effects, a packet of short temporal length at low levels will have a considerably extended impact on a layer of westerly winds at higher levels. Observed and modeled westerly accelerations resulting from packet absorption occur in the same layer, and are similar in magnitude and duration. These results support the theory that Kelvin waves are responsible for the westerly phase of the semiannual oscillation.

Coy, L.↗

DOE OSTI2020

Analysis of the performance of a hybrid CPU/GPU 1D2D coupled model for real flood cases

Coupled 1D2D models emerged as an efficient solution for a two-dimensional (2D) representation of the floodplain combined with a fast one-dimensional (1D) schematization of the main channel. At the same time, high-performance computing (HPC) has appeared as an efficient tool for model acceleration. In this work, a previously validated 1D2D Central Processing Unit (CPU) model is combined with an HPC technique for fast and accurate flood simulation. Due to the speed of 1D schemes, a hybrid CPU/GPU model that runs the 1D main channel on CPU and accelerates the 2D floodplain with a Graphics Processing Unit (GPU) is presented. Since the data transfer between sub-domains and devices (CPU/GPU) may be the main potential drawback of this architecture, the test cases are selected to carry out a careful time analysis. Here, the results reveal the speed-up dependency on the 2D mesh, the event to be solved and the 1D discretization of the main channel. Additionally, special attention must be paid to the time step size computation shared between sub-models. In spite of the use of a hybrid CPU/GPU implementation, high speed-ups are accomplished in some cases.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

An Experimental Study of Unconfined Hydrogen/Oxygen and Hydrogen/Air Explosions

Development tests are being conducted to characterize unconfined Hydrogen/air and Hydrogen/Oxygen blast characteristics. Most of the existing experiments for these types of explosions address contained explosions, like shock tubes. Therefore, the Hydrogen Unconfined Combustion Test Apparatus (HUCTA) has been developed as a gaseous combustion test device for determining the relationship between overpressure, impulse, and flame speed at various mixture ratios for unconfined reactions of hydrogen/oxygen and hydrogen/air. The system consists of a central platform plumbed to inject and mix component gasses into an attached translucent bag or balloon while monitoring hydrogen concentration. All tests are ignited with a spark with plans to introduce higher energy ignition sources in the future. Surrounding the platform are 9 blast pressure "Pencil" probes. Two high-speed cameras are used to observe flame speed within the combustion zone. The entire system is raised approx. 6 feet off the ground to remove any ground reflection from the measurements. As of this writing greater than 175 tests have been performed and include Design of Experiments test sets. Many of these early tests have used bags or balloons between approx. 340L and approx. 1850L to quantify the effect of gaseous mixture ratio on the properties of interest. All data acquisition is synchronized between the high-speed cameras, the probes, and the ignition system to observe flame and shock propagation. Successful attempts have been made to couple the pressure profile with the progress of the flame front within the combustion zone by placing a probe within the bag. Overpressure and impulse data obtained from these tests are used to anchor engineering analysis tools, CFD models and in the development of blast and fragment acceleration models.

Richardson, Erin↗

NASA NTRS2013

Modeling of Particle Acceleration at Multiple Shocks Via Diffusive Shock Acceleration: Preliminary Results

We present preliminary results from a model that diffusively accelerates particles at multiple shocks. Our basic approach is related to box models (Protheroe and Stanev, 1998; Moraal and Axford, 1983; Ball and Kirk, 1992; Drury et al., 1999) in which a distribution of particles is diffusively accelerated inside the box while simultaneously experiencing decompression through adiabatic expansion and losses from the convection and diffusion of particles outside the box (Melrose and Pope, 1993; Zank et al., 2000). We adiabatically decompress the accelerated particle distribution between each shock by either the method explored in Melrose and Pope (1993) and Pope and Melrose (1994) or by the approach set forth in Zank et al. (2000) where we solve the transport equation by a method analogous to operator splitting. The second method incorporates the additional loss terms of convection and diffusion and allows for the use of a variable time between shocks. We use a maximum injection energy (Emax) appropriate for quasi-parallel and quasi-perpendicular shocks (Zank et al., 2000, 2006; Dosch and Shalchi, 2010) and provide a preliminary application of the diffusive acceleration of particles by multiple shocks with frequencies appropriate for solar maximum (i.e., a non-Markovian process).

Parker, Linda Neergaard↗

NASA NTRS2014

A Monte Carlo Approach to Modeling the Breakup of the Space Launch System EM-1 Core Stage with an Integrated Blast and Fragment Catalogue

The Liquid Propellant Fragment Overpressure Acceleration Model (L-FOAM) is a tool developed by Bangham Engineering Incorporated (BEi) that produces a representative debris cloud from an exploding liquid-propellant launch vehicle. Here it is applied to the Core Stage (CS) of the National Aeronautics and Space Administration (NASA) Space Launch System (SLS launch vehicle). A combination of Probability Density Functions (PDF) based on empirical data from rocket accidents and applicable tests, as well as SLS specific geometry are combined in a MATLAB script to create unique fragment catalogues each time L-FOAM is run-tailored for a Monte Carlo approach for risk analysis. By accelerating the debris catalogue with the BEi blast model for liquid hydrogen / liquid oxygen explosions, the result is a fully integrated code that models the destruction of the CS at a given point in its trajectory and generates hundreds of individual fragment catalogues with initial imparted velocities. The BEi blast model provides the blast size (radius) and strength (overpressure) as probabilities based on empirical data and anchored with analytical work. The coupling of the L-FOAM catalogue with the BEi blast model is validated with a simulation of the Project PYRO S-IV destruct test. When running a Monte Carlo simulation, L-FOAM can accelerate all catalogues with the same blast (mean blast, 2 σ blast, etc.), or vary the blast size and strength based on their respective probabilities. L-FOAM then propagates these fragments until impact with the earth. Results from L-FOAM include a description of each fragment (dimensions, weight, ballistic coefficient, type and initial location on the rocket), imparted velocity from the blast, and impact data depending on user desired application. LFOAM application is for both near-field (fragment impact to escaping crew capsule) and far-field (fragment ground impact footprint) safety considerations. The user is thus able to use statistics from a Monte Carlo set of L-FOAM catalogues to quantify risk for a multitude of potential CS destruct scenarios. Examples include the effect of warning time on the survivability of an escaping crew capsule or the maximum fragment velocities generated by the ignition of leaking propellants in internal cavities.

Richardson, Erin↗

DOE OSTI2022

Anderson Acceleration for Distributed Training of Deep Learning Models

Anderson acceleration (AA) is an extrapolation technique that has recently gained interest in the deep learning (DL) community to speed-up the sequential training of DL models. However, when performed at large scale, the DL training is exposed to a higher risk of getting trapped into steep local minima of the training loss function, and standard AA does not provide sufficient acceleration to escape from these steep local minima. This results in poor generalizability and makes AA ineffective. To restore AA’s advantage to speed-up the training of DL models on large scale computing platforms, we combine AA with an adaptive moving average procedure that boosts the training to escape from steep local minima. By monitoring the relative standard deviation between consecutive iterations, we also introduce a criterion to automatically assess whether the moving average is needed. We applied the method to the following DL instantiations for image classification: (i) ResNet50 trained on the open-source CIFAR100 dataset and (ii) ResNet50 trained on the open-source ImageNet1k dataset. Numerical results obtained using up to 1,536 NVIDIA V100 GPUs on the OLCF supercomputer Summit showed the stabilizing effect of the moving average on AA for all the problems above.

Lupo Pasini, Massimiliano↗

NASA NTRS1995

Results of the Quasi-Steady Acceleration Environment from the STS-62 Missions

One of the clear benefits of conducting scientific research in space is to take advantage of the reduced acceleration environment. Many accelerometer packages have proven to accurately measure the acceleration environment at frequency levels above one Hz. However, for particular classes of experiments the quality of science returns is a direct function of the extremely low frequency (less than 0.01 Hz), quasi-steady acceleration environment. One class particularly interested in this acceleration regime is the group of crystal growth experimenters. These scientists are primarily interested in knowing the resultant quasi-steady acceleration vector at their respective crystal growth locations. The objective of many of these scientists is to minimize the amount of convective flow acting in a direction perpendicular to the growth axis of the crystal. Convective flow within the crystal can be induced by the direction and magnitude of the quasi-steady acceleration vector. Convective flows acting perpendicular to the growth axis of the crystal can cause nonuniformity within the crystal, thus reducing the quality of the results. The Orbital Acceleration Research Experiment (OARE), an accelerometer package hardmounted to the bottom of the payload bay of the orbiter Columbia (OV-102), has the capability of monitoring and recording the quasi-steady acceleration environment. This paper will describe the components that make up the on-orbit quasi-steady acceleration environment, detail how results from the OARE device were achieved, and compare modelled acceleration results with actual on-orbit OARE results from the STS-62 and STS-65 flights. A summary of the results will be provided along with possible recommendations of how to combine modelled and realtime quasi-steady accelerometer data for future Shuttle flights.

Matisak, Brian↗

DOE OSTI2022

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗

NASA NTRS2014

Pulsed Inductive Plasma Acceleration: Performance Optimization Criteria

Optimization criteria for pulsed inductive plasma acceleration are developed using an acceleration model consisting of a set of coupled circuit equations describing the time-varying current in the thruster and a one-dimensional momentum equation. The model is nondimensionalized, resulting in the identification of several scaling parameters that are varied to optimize the performance of the thruster. The analysis reveals the benefits of underdamped current waveforms and leads to a performance optimization criterion that requires the matching of the natural period of the discharge and the acceleration timescale imposed by the inertia of the working gas. In addition, the performance increases when a greater fraction of the propellant is initially located nearer to the inductive acceleration coil. While the dimensionless model uses a constant temperature formulation in calculating performance, the scaling parameters that yield the optimum performance are shown to be relatively invariant if a self-consistent description of energy in the plasma is instead used.

Polzin, Kurt A.↗

DOE OSTI2024

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗

DOE OSTI2022

Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells

We developed a biomechanics-informed online learning framework to learn the dynamics with ground truth generated with multiscale modeling simulation. It was built on Summit-like supercomputers, which were also used to benchmark and validate our framework on one physiologically significant modeling of deformable biological cells. We generalized the century-old equation of Jeffery orbits to a new equation of motion with additional parameters to account for the flow conditions and the cell deformability. Using simulation data at particle-based resolutions for flowing cells and the learned parameters from our framework, we validated the new equation by the motions, mostly rotations, of a human platelet in shear blood flow at various shear stresses and platelet deformability. Our online framework, which surrogates redundant computations in the conventional multiscale modeling by solutions of our learned equation, accelerates the conventional modeling by three orders of magnitude without visible loss of accuracy.

multiscale modeling↗

DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗