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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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NASA NTRS1971

Methodologies for optimal resource allocation to the national space program and new space utilizations. Volume 1: Technical description

The optimal allocation of resources to the national space program over an extended time period requires the solution of a large combinatorial problem in which the program elements are interdependent. The computer model uses an accelerated search technique to solve this problem. The model contains a large number of options selectable by the user to provide flexible input and a broad range of output for use in sensitivity analyses of all entering elements. Examples of these options are budget smoothing under varied appropriation levels, entry of inflation and discount effects, and probabilistic output which provides quantified degrees of certainty that program costs will remain within planned budget. Criteria and related analytic procedures were established for identifying potential new space program directions. Used in combination with the optimal resource allocation model, new space applications can be analyzed in realistic perspective, including the advantage gain from existing space program plant and on-going programs such as the space transportation system.

Source record↗

DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗

NASA NTRS2014

On the Hiatus in the Acceleration of Tropical Upwelling Since the Beginning of the 21st Century

Chemistry-climate models predict an acceleration of the upwelling branch of the Brewer-Dobson circulation as a consequence of increasing global surface temperatures, resulting from elevated levels of atmospheric greenhouse gases. The observed decrease of ozone in the tropical lower stratosphere during the last decades of the 20th century is consistent with the anticipated acceleration of upwelling. However, more recent satellite observations of ozone reveal that this decrease has unexpectedly stopped in the first decade of the 21st century, challenging the implicit assumption of a continuous acceleration of tropical upwelling. In this study we use three decades of chemistry transport-model simulations (1980-2013) to investigate this phenomenon and resolve this apparent contradiction. Our model reproduces the observed tropical lower stratosphere ozone record, showing a significant decrease in the early period followed by a statistically robust trend-change after 2002. We demonstrate that this trend-change is correlated with corresponding changes in the vertical transport and conclude that a hiatus in the acceleration of tropical upwelling occurred during the last decade.

trends↗

DOE OSTI2022

Cascade Models in Simulation of Extended Heavy Targets Irradiated by Accelerated Proton and Deuteron Beams

The paper presents a survey of the main numerical models used for simulation of interaction of accelerated particle beams with target nuclei. These models form the core of the software for simulation of various experiments and experimental facilities both for scientific and applied purposes. The beam and target parameters considered in detail in this study (protons and deuterons with energies from 0.66 to 4 AGeV and bulk U targets) cover the range of interest in development of new concepts of nuclear power production aided by accelerated particle beams.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2012

The Effects of Wave Escape on Fast Magnetosonic Wave Turbulence in Solar Flares

One of the leading models for electron acceleration in solar flares is stochastic acceleration by weakly turbulent fast magnetosonic waves ("fast waves"). In this model, large-scale flows triggered by magnetic reconnection excite large-wavelength fast waves, and fast-wave energy then cascades from large wavelengths to small wavelengths. Electron acceleration by large-wavelength fast-waves is weak, and so the model relies on the small-wavelength waves produced by the turbulent cascade. In order for the model to work, the energy cascade time for large-wavelength fast waves must be shorter than the time required for the waves to propagate out of the solar-flare acceleration region. To investigate the effects of wave escape, we solve the wave kinetic equation for fast waves in weak turbulence theory, supplemented with a homogeneous wave-loss term.We find that the amplitude of large-wavelength fast waves must exceed a minimum threshold in order for a significant fraction of the wave energy to cascade to small wavelengths before the waves leave the acceleration region.We evaluate this threshold as a function of the dominant wavelength of the fast waves that are initially excited by reconnection outflows.

Pongkitiwanichakul, Peera↗

DOE OSTI2024

Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility

Controlling the dose of radiation exposure in potential radioactive facilities is critical for ensuring the safety of staff and the public. Here, in this paper, we developed machine learning models to estimate radiation exposure efficiently at the Thomas Jefferson National Accelerator Facility (JLab), aiming to enhance safety in both accelerator facilities and public areas. Multiple sensors were deployed around the three experimental halls at JLab. Data on single-beam currents, energy levels, and radiation values at the sensor locations were collected during accelerator operation. We proposed a multi-task learning model for radiation estimation, utilizing either one-dimensional convolutional neural networks (1-D CNNs) or long short-term memory networks (LSTMs) as the backbone. The proposed model was trained to simultaneously estimate radiation levels at the sensor locations. Experimental results demonstrated that the proposed model with LSTM backbone achieved the best estimation performance, with an average R 2 score of 0.7557 for estimation within the same year and 0.7157 for estimation across different years. These results significantly surpassed those of competing models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Focusing of laser-accelerated ions with spherically curved targets

A theoretical model for laser-accelerated ion focusing from spherically curved targets using a Gaussian self-similar solution is presented. This model describes the evolution of the focal location and focal radius with final ion energy and the initial radial boundary of the ions, the latter being a function of both target geometry and the ion acceleration radius for a given ion energy. The theory is supported by particle-in-cell simulations of a variety of target shapes with varying radii of curvature and target opening angles, as well as variations in the injected electron beam radius and energy spectrum. The theory and simulations suggest that the focal location varies linearly with the radius of curvature, with the square root of the ratio of ion energy to effective electron temperature, and monotonically increases with the energy-dependent ratio of the initial ion radial boundary to the radius of curvature. Considering ponderomotive acceleration, this suggests that the focal length should scale inversely with intensity, $(I_Lλ_L^2)$ −1/4 , suggesting a 10× increase in intensity will reduce the focal distance by ̃1.8× for a given ion energy.

Physics - Plasma physics↗

NASA NTRS1980

Acceleration of energetic charged particles by interplanetary and interstellar shock waves

Pioneer 11 observations of energetic proton acceleration in interplanetary shocks are in good agreement with the predictions of the Vector-V x Vector-B electric field model of shock acceleration. The Vector-V x Vector-B model predicts that relativistic particles are accelerated by a factor of 2-15 in kinetic energy during a single encounter with a quasi-perpendicular (not less than 70 deg) supernova shock wave, and that the post-encounter pitch angle distributions are very anisotropic.

Pesses, M. E.↗

DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗

DOE OSTI2023

Advanced Modeling of Plasma-based Particle Accelerators

Plasma-based acceleration (PBA) driven by an intense laser (LWFA) or particle beam (PWFA) can produce ultra-high accelerating fields in excess of a GV/cm. PBA could substantially reduce the size and cost of future linear collider facilities if deployed successfully. PBA enables compact tabletop accelerators that can provide lower energy GeV-class beams in a laboratory setting. PBA enables high quality beam generation suitable for x-ray free electron lasers (XFEL) via controllable methods of self-injection. Challenges in modeling and optimization of multi-stage PBA motivate the need for exascale computing and state-of-the-art PIC codes.

43 PARTICLE ACCELERATORS↗

NASA NTRS2007

GPS-Based Reduced Dynamic Orbit Determination Using Accelerometer Data

Currently two gravity field satellite missions, CHAMP and GRACE, are equipped with high sensitivity electrostatic accelerometers, measuring the non-conservative forces acting on the spacecraft in three orthogonal directions. During the gravity field recovery these measurements help to separate gravitational and non-gravitational contributions in the observed orbit perturbations. For precise orbit determination purposes all these missions have a dual-frequency GPS receiver on board. The reduced dynamic technique combines the dense and accurate GPS observations with physical models of the forces acting on the spacecraft, complemented by empirical accelerations, which are stochastic parameters adjusted in the orbit determination process. When the spacecraft carries an accelerometer, these measured accelerations can be used to replace the models of the non-conservative forces, such as air drag and solar radiation pressure. This approach is implemented in a batch least-squares estimator of the GPS High Precision Orbit Determination Software Tools (GHOST), developed at DLR/GSOC and DEOS. It is extensively tested with data of the CHAMP and GRACE satellites. As accelerometer observations typically can be affected by an unknown scale factor and bias in each measurement direction, they require calibration during processing. Therefore the estimated state vector is augmented with six parameters: a scale and bias factor for the three axes. In order to converge efficiently to a good solution, reasonable a priori values for the bias factor are necessary. These are calculated by combining the mean value of the accelerometer observations with the mean value of the non-conservative force models and empirical accelerations, estimated when using these models. When replacing the non-conservative force models with accelerometer observations and still estimating empirical accelerations, a good orbit precision is achieved. 100 days of GRACE B data processing results in a mean orbit fit of a few centimeters with respect to high-quality JPL reference orbits. This shows a slightly better consistency compared to the case when using force models. A purely dynamic orbit, without estimating empirical accelerations thus only adjusting six state parameters and the bias and scale factors, gives an orbit fit for the GRACE B test case below the decimeter level. The in orbit calibrated accelerometer observations can be used to validate the modelled accelerations and estimated empirical accelerations computed with the GHOST tools. In along track direction they show the best resemblance, with a mean correlation coefficient of 93% for the same period. In radial and normal direction the correlation is smaller. During days of high solar activity the benefit of using accelerometer observations is clearly visible. The observations during these days show fluctuations which the modelled and empirical accelerations can not follow.

VanHelleputte, Tom↗

DOE OSTI2023

Modeling and Simulation Development Pathways to Accelerating KP-FHR Licensing (Final Report)

This project assembles a strong U.S. industry and national laboratory team to complete scope of work. Kairos Power (KP), headquartered in Alameda, CA, is the leader of this effort and has built an internal team of highly competent engineers and managers with extensive combined experience in nuclear power, conventional power, product development, and licensing. INL, ANL, and LANL bring unique capabilities in advanced reactor R&D and licensing. The project funding source is the result of FOA No. 0001817, U. S. Industry Opportunities for Advanced Nuclear Technology Development. There has been on-going work and this Access Cooperative Research and Development Agreement (CRADA) will cover the remaining work scope of FOA 0001817. KP is implementing innovative strategies that can reduce the cost and accelerate the initial demonstration of the Kairos Power Fluoride-salt-cooled, High-temperature Reactor (KP-FHR) to meet the needs of the U.S. electricity market by 2030. Licensing of the KP-FHR could be significantly accelerated using advanced computing methods with sufficient predictive capabilities to be able to extrapolate potential response of the structure in different scenarios. However, currently used computational methods heavily rely on empirical fits and cannot be used for extrapolation. The scope focuses on improving the modeling capability in the NEAMS Grizzly structural mechanics code and the NEAMS BISON fuel performance code and the NEAMS SAM systems analysis code. This work leverages the expertise and know-how gathered in three DOE National Laboratories – INL, ANL, and LANL.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Leveraging prior mean models for faster Bayesian optimization of particle accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Improved melt model for power flow

Accelerators that drive z-pinch experiments transport current densities in excess of 1 MA/cm 2 in order to melt or ionize the target and implode it on axis. These high current densities stress the transmission lines upstream from the target, where rapid electrode heating causes plasma formation, melt, and possibly vaporization. These plasmas negatively impact accelerator efficiency by diverting some portion of the current away from the target, referred to as “current loss”. Simulations that are able to reproduce this behavior may be applied to improving the efficiency of existing accelerators and to designing systems operating at ever higher current densities. The relativistic particle-in-cell code CHICAGO ® is the primary code for modeling power flow on Sandia National Laboratories’ Z accelerator. We report here on new algorithms that incorporate vaporization and melt into the standard power-flow simulation framework. Taking a hybrid approach, the CHICAGO® kinetic/multi-fluid treatment has been expanded to include vaporization while the quasi-neutral equation-of-motion has been updated for melt at high current-densities. For vaporization, a new one-dimensional substrate model provides a more accurate calculation of electrode thermal, mass, and magnetic field diffusion as well as a means of emitting absorbed contaminants and vaporized metal ions. A quasi-fluid model has been implemented expressly to mimic the motion of imploding liners for accurate inductance histories. For melt, a multi-ion Hall-MHD option has been implemented and benchmarked against Alegra MHD. This new model is described with sufficient detail to reproduce these algorithms in any hybrid kinetic code. Physics results from the new code are also presented. A CHICAGO ® Hall-MHD simulation of a radial transmission line demonstrates that Hall physics, not included in Alegra, has no significant impact on the diffusion of electrode material. When surface contaminant desorption is mocked in as a hydrogen surface plasma, both the surface and bulk-material plasmas largely compress under the influence of the j × B force. Similar results are seen in Alegra, which also shows magnetic and material diffusion scaling with peak current. Test vaporization simulations using MagLIF and a power-flow experimental geometry show Fe + ions diffuse only a few hundred µm from the electrodes, so present models of Z power flow remain valid.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Strong interplay between superluminosity and radiation friction during direct laser acceleration

Using a test-particle model, we examine direct laser acceleration of electrons within a magnetic filament that has been shown to form inside a laser-irradiated plasma. We focus on ultra-high intensity interactions where the force of radiation friction caused by electron emission of electromagnetic radiation must be taken into account. It is shown that even relatively weak superluminosity of laser wave fronts—the feature that has been previously neglected—qualitatively changes the electron dynamics, leading to a so-called attractor effect. As a result of this effect, electrons with various initial energies reach roughly the same maximum energy and emit roughly the same power in the form of x-rays and gamma-rays. Our analysis implies that the primary cause of the superluminosity is the laser-heated plasma. The discovered strong interplay between superluminosity and radiation friction is of direct relevance to laser-plasma interactions at high-intensity multi-PW laser facilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1989

Magnetohydrodynamic waves and particle acceleration in a coronal loop

A model is presented for the acceleration of electrons in a flaring solar coronal loop. It is suggested that the primary energy release event in the flare establishes a spectrum of MHD waves within the loop which accelerate electrons by wave-particle resonant interactions. The nature of the MHD oscillations are described and numerical and analytic solution of the dispersion relation are examined. It is found that the behavior of a particle depends on whether or not it is in resonance with the wave spectrum. Resonant particles follow chaotic trajectories and can diffuse in velocity space between wave effective potentials, thereby gaining energy.

De La Beaujardiere, Jean-Francois↗

DOE OSTI2021

Enhanced Beam Diagnostics with Existing BPPMs via GPU-powered Multi-Particle Simulation

This research aims to utilize the multi-particle code, High-Performance Simulator (HPSim), to realistically model the Side-Coupled-Cavity Linac (CCL) lattice of the LANSCE accelerator. This new model would allow us to predict the beam’s bunch length (the longitudinal spread), which is unavailable for individual accelerating modules or only accessible at the end of the linac. However, a correct bunch length is critical for the high-energy beam transport after the CCL. Its impact would be most significant in the Proton Storage Ring (PSR), where we should be able to reduce losses for the circulating beam. The PSR is scheduled to have a 25% current increase for the neutron spallation target upgrade at the Lujan Center. A highly bunched beam would be necessary to reduce the particle losses and lower the radiation levels produced from the ring. A realistic HPSim model with >1M macro-particles can help tackle the beam losses at the sub-percent level. This new work would also create a realistic surrogate model for future machine learning projects.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

47 OTHER INSTRUMENTATION↗