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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Using statistical modeling to predict and understand fusion experiments

Over 300 cryogenic layered direct-drive inertial confinement fusion implosions have been successfully executed on the OMEGA Laser System in the last decade. However, extracting sufficient understanding from these experiments to develop new designs or to identify or mitigate degradation sources remains challenging. Recently, a statistical modeling approach was developed to successfully design and predict improved implosion experiments on OMEGA. Here, we show that one-dimensional simulations can be used to predict the outcomes of systematically perturbed three-dimensional simulations and that this statistical modeling approach can be used to identify or rule out physical mechanisms for some of the degradation sources observed on the OMEGA Laser System for direct-drive cryogenic inertial confinement fusion. In this instance, we investigate the fusion yield dependencies on the ion temperature asymmetries and laser beam size observed in experiments by comparing with trends in three-dimensional synthetic simulation databases. Using the statistical model on these systematically perturbed simulations, we find that the statistically inferred dependency on the measured ion temperature asymmetries is well explained by imposed ℓ =1 modes. However, we find that the dependency on the laser beam size is only dominated by the illumination non-uniformity for some extreme cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Performance enhancement of direct-drive shock-augmented ignition inertial fusion implosions through shock timing optimization

Shock-augmented ignition (SAI) [R. H. H. Scott et al., Phys. Rev. Lett. 129, 195001 (2022)] is an alternative inertial confinement fusion concept that is designed to achieve high energy gain by combining improved resilience to instabilities with enhanced fuel compression. In SAI, lower implosion velocities can improve hydrodynamic stability and limited laser intensities reduce the excitation of detrimental laser-plasma instabilities, enabling greater areal density accumulation. Here we report an experimental investigation of SAI using a series of warm D 2 implosions. By timing the augmenting shock to arrive at the implosion center immediately prior to peak compression, the areal density and hot-spot pressure increase by 50% and 112%, respectively, relative to unoptimized designs. Ignition-scale simulations reproduce the observed timing dependence and demonstrate the potential of SAI for inertial fusion energy.

Direct drive↗
NASA NTRS1999

Inertial Electrostatic Confinement Fusion: The Laser Elevator Solar System Survey for Propellants Abstract

Dr. Wayne Pryor worked on three projects this summer. These were: 1) Inertial Electrostatic Confinement; 2) The Laser Elevator; and 3) Solar System Survey for Propellants Abstract. We Assisted Jon Nadler from Richland Community College in assembling and operating a table-top nuclear fusion reactor. We successfully demonstrated neutron production in a deuterium plasma. Pryor also obtained basic spectroscopic information on the atomic and molecular emissions in the plasma. The second project consisted of the completion of a paper on a novel propulsion concept (due to Tom Meyer of Colorado, the first author): a laser sail that bounces light back to the laser source. Recycling the photons from source to sail perhaps 100-1000 times dramatically improves the energy efficiency of this system, which may become very important for high-velocity missions in the future. Lastly, we compiled a very basic inventory of solar system propellant resources, their locations, and their accessibility. This initial inventory concentrates on sunlight availability, water availability, and the difficulty (delta-velocity requirement and radiation environment) in getting there.

Pryor, Wayne↗
DOE OSTI2023

Correcting Hohlraum Drive Asymmetry with Glow Discharge Polymerization Coated Capsule Shims

In inertial confinement fusion target design, the shape discrepancy between the cylindrical hohlraum and the spherical capsule creates a low mode asymmetry in the implosion. One way to correct such asymmetry is to shim the target capsule surface with extra mass in specific locations following a 3D P4 Legendre mode. Previously, the desired surface pattern was precision machined out of the capsule. The resulting 2DConA experiments that investigated the implosion’s shape demonstrated the shimming method’s success. However, machining leaves large defects on the capsule surface which will degrade neutron yield in a DT implosion. An alternative shimming approach is to grow the pattern on the capsule surface using a glow discharge polymerization coating process in a stencil lithography application. In this study, we discuss the fabrication, characterization and challenges of making shimmed target capsules with this new method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Statistical model of the stimulated forward Brillouin scattering driven by a randomized laser beam in plasma

The modeling of a spatially incoherent laser beam remains a central problem of the parametric instabilities in the context of inertial confinement fusion. This letter gives a simplified and comprehensive overview of the recent analytical developments regarding the modeling of these laser beams and a comparison with a dedicated experiment. Our model accounts for the first time for the statistical standard deviation of the gain and accurately captures the entanglement between wave mixing processes and the speckle correlations thus resolving the longstanding contradictions between the random phase approximation and the model of independent speckles. It is successfully compared to a recent laser beam spray experiment and the associated paraxial simulations, demonstrating that backscattering predictions require accounting for the beam spray. Furthermore, our framework thus provides a way to evaluate and guide the analysis of parametric instabilities in high laser energy experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Validation of implosion modeling through direct-drive shock timing experiments at the National Ignition Facility

Precise modeling of shocks in inertial confinement fusion implosions is critical for obtaining the desired compression in experiments. Shock velocities and post-shock conditions are determined by laser-energy deposition, heat conduction, and equations of state. This paper describes experiments at the National Ignition Facility (NIF) where multiple shocks are launched into a cone-in-shell target made of polystyrene, using laser pulse shapes with two or three pickets and varying on-target intensities. Shocks are diagnosed using the Velocity Interferometric System for Any Reflector diagnostic. Simulated and inferred shock velocities agree well for the range of intensities studied in this work. These results are the first shock-velocity measurements using direct drive on the NIF and provide a good measure of early-time, directly driven laser-energy coupling. Furthermore, the validated models add to the credibility of direct-drive-ignition designs at the megajoule scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Evidence of Three-Dimensional Asymmetries Seeded by High-Density Carbon-Ablator Nonuniformity in Experiments at the National Ignition Facility

Inertial confinement fusion implosions must achieve high in-flight shell velocity, sufficient energy coupling between the hot spot and imploding shell, and high areal density ( ρ R = ∫ ρ d r ) at stagnation. Asymmetries in ρ R degrade the coupling of shell kinetic energy to the hot spot and reduce the confinement of that energy. In this study, we present the first evidence that nonuniformity in the ablator shell thickness ( ~ 0.5 % of the total thickness) in high-density carbon experiments is a significant cause for observed 3D ρ R asymmetries at the National Ignition Facility. These shell-thickness nonuniformities have significantly impacted some recent experiments leading to ρ R asymmetries on the order of ~ 25 % of the average ρ R and hot spot velocities of ~ 100 km / s . This work reveals the origin of a significant implosion performance degradation in ignition experiments and places stringent new requirements on capsule thickness metrology and symmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning

We report building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this article, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Magnetized ICF implosions: non-axial magnetic field topologies

This paper explores four different magnetic field topologies for application to spherical inertial confinement fusion implosions: axial, mirror, cusp and closed field lines. A mirror field is found to enhance the impact of magnetization over an axial field; this is because the mirror field more closely follows the hot-spot surface. A cusp field, while simple to generate, is not found to have any benefits over the tried-and-tested axial field. Closed field lines are found to be of the greatest benefit to hot-spot performance, with the simulated design undergoing a 2× increase in ion temperature before α-heating is considered. The plasma properties of the simulation with closed field lines are radically different from the unmagnetized counterpart, with electron temperatures in excess of 100 keV, suggesting that a fundamental redesign of the capsule implosion is possible if this method is pursued.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Investigating performance and variability of NIF ICF experiments with deep learning

The parameter space involved in designing an inertial confinement fusion shot at the National Ignition Facility (NIF) is massively multi-dimensional and the cost of a single shot makes a comprehensive set of sensitivity studies in the laboratory impractical. The use of machine learning to overcome these challenges has gained popularity and has had several successful applications by the scientific community. We extend on these efforts by training a neural network (NN) on information about the experimental design, engineering elements, and drive asymmetry to predict with uncertainty the neutron yield of an experiment. We find the measured and model predicted values are in good agreement, with an R 2 value of 0.91 for a randomly selected test dataset. Almost all the predicted 95% credible intervals contain the corresponding measured value for both training and test datasets. We identify correlations picked up by the NN between the shot design, yield, and variability and use them to motivate shot sensitivity studies. The first shot to exceed the Lawson-like ignition criteria (N210808) was conducted at the NIF and subsequent shots studied the design’s robustness. In a follow-up shot to N210808, our model predicts capsule quality to be the main performance degradation mechanism that prevented the shot from repeating previous performance levels. Shot N221204 was the first shot to exceed a target energy gain of 1. Our model predicts increased yield with reduced coast time for a N221204 study and greater variability for designs with lower peak powers at constant yield. The model’s fast prediction speed and uncertainty prediction are useful for identifying interesting design paths that could warrant further investigation with conventional simulations to search for robust high yield designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Simple analytic fusion hot spot models for fusion reaction history

The measured fusion reaction history is a combination of the temporal evolution of the fusion hot spot temperature, mass, and volume. Depending on the mechanism of evolution in inertial confinement fusion implosions—shocks, compression, convergence, mass ablation, ignition—the evolution of the reaction history varies. Here, we derive and catalog a set of simplified inertial confinement fusion hot spot models with analytic solutions to infer the evolution of the fusion reaction history for each mechanism. The models give valuable insight into the meaning and cause of fusion reaction history and nuclear burnwidth measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Carbon ablator areal density at fusion burn: Observations and trends at the National Ignition Facility

For inertial confinement fusion experiments, the pusher is composed of a high-density deuterium tritium cyrogenic fuel layer and an ablator, often made of carbon. In an ideal, no-mix implosion, increasing the areal density of the pusher transfers more pressure to the hot spot and increases the hot spot confinement time. There has been a lack of knowledge about the final compressed state of the ablator for implosions at the National Ignition Facility. 14 MeV fusion neutrons inelastically scattering on the remaining carbon ablator excites a nuclear metastable state that emits a prompt 4.4 MeV gamma ray. The gamma reaction history diagnostic data, when reduced by a new data analysis technique, can isolate and measure the carbon gamma rays, which are proportional to the areal density of the ablator during fusion burn. The trends over many National Ignition Facility campaigns show that the ablator areal density is weakly sensitive to the maximum shell velocity, the cold fuel radius, the ablator mass remaining, or the laser picket intensity. Controlled parameter scans reveal that, for specific campaigns, ablator compression has a strong dependence on laser coast time, high Z dopants, and the laser drive foot duration. Using a model of the compressed ablator density profile reveals that the greatest variation of the ablator areal density comes from its thickness, with highly compressed, thin layers having high areal density values. The compression and thickness of the ablator are other metrics that designers should understand to differentiate the types of capsule degradation and maximize the inertial confinement fusion performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Observation of d(t,n) α Neutrons Following d(d,p)t Reactions in a Deuterium Gas Cell: An Attempt to Repeat Ruhlig’s 1938 Observation of Secondary Reactions

In order to benchmark methods used to calculate reaction-in-flight fusion reactions in inertial confinement fusion and address issues related to the first claimed observation of d(t,n)α reactions in 1938, secondary d(t,n)α reactions have been observed following d(d,p)t reactions in deuterium gas. A pulsed 200-nA, 2.2-MeV deuterium beam from the Triangle Universities Nuclear Laboratory FN tandem accelerator was injected into a cylindrical multiatmosphere deuterium gas target. The incident beam traversed along the target cylinder’s 3-cm symmetry axis after its passage through a Havar entrance foil. Two different Havar foil thicknesses were used to obtain 1.5- and 0.6-MeV deuteron beams entering the deuterium cell. The cylinder’s radius was 2 cm to allow for d(d,p)t tritons emitted perpendicular to the beam to range out in the deuterium gas. The neutron emission from the cell was observed via its time of flight to a liquid scintillator placed at various angles to the beam direction, at a distance of 243 cm. Pulse-shape-discrimination techniques were used to separate neutron and gamma-ray signals seen in the liquid scintillator. The observed probability of ~2 × 10 –4 for inducing secondary d(t,n)α fusion in the gas cell per d(d,p)t reaction is consistent with theoretical expectations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Stability of perpendicular magnetohydrodynamic shocks in materials with ideal and nonideal equations of state

Magnetized target fusion approach to inertial confinement fusion involves the formation of strong shocks that travel along a magnetized plasma. Shocks, which play a dominant role in thermalizing the upstream kinetic energy generated in the implosion stage, are seldom free from perturbations, and they wrinkle in response to upstream or downstream disturbances. In Z-pinch experiments, significant plasma instability mitigation was observed with pre-embedded axial magnetic fields. To isolate effects, in this work we theoretically study the impact of perpendicular magnetic fields on the planar shock dynamics for different equations of state. For fast magnetosonic shocks in ideal gases, it was found that the magnetic field amplifies the intensity of the perturbations when $\gamma$ > 2 or it weakens them when $\gamma$ < 2. Weak shocks have been found to be stable regardless of the magnetic plasma intensity and gas compressibility; however, for sufficiently strong shocks the magnetic fields can promote a neutral stability/SAE at the shock if the adiabatic index is higher than 1 + $\sqrt{2}$. Finally, results have been validated with numerical simulations performed with the FLASH code.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identifying Entangled Physics Relationships Through Sparse Matrix Decomposition to Inform Plasma Fusion Design

We report a sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulation codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g., neutron yield, neutron velocity, areal density) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield, such as picket power and laser energy, variables that represent a dramatic change of the ICF design, such as number of pulse steps, are also very important. The obtained sparse components are then used to train a random forest (RF) regression surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF trained using all the design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulation results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗