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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 OSTI2025

Monitoring the Structural Health of the Stage-Four Gibbs Resistor In Order to Maintain a Functioning Pulse-Forming Network

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory (LANL) is a critical facility used for nuclear weapons research and development (Los Alamos National Laboratory). Its primary function is to provide high-resolution, real-time images of the behavior of materials under extreme conditions, specifically during the hydrodynamic testing of nuclear weapons surrogates. The facility uses advanced radiographic techniques, such as dual-axis X-ray imaging, to capture detailed snapshots of these materials as they react to high-pressure environments. DARHT plays a key role in maintaining the safety, security, and reliability of the U.S. nuclear arsenal, supporting the Stockpile Stewardship Program. The facility helps ensure that nuclear weapons perform as designed without the need for nuclear tests. Its dual-axis radiography provides more precise data than traditional single-axis imaging, offering a comprehensive view of the internal dynamics of a weapon's primary stage.

42 ENGINEERING↗
DOE OSTI2025

Solving the Pulsed-Power Driven Inertial Fusion Energy Standoff Problem (Abbreviated Final Report)

The standoff problem in pulsed-power driven IFE (Inertial Fusion Energy) refers to the problem of electrically connecting a fusion target with the driver in a way that preserves the driver/target interface in the presence of a large fusion energy release (100’s of megajoules to 1000 megajoules). High yield fusion drivers for stockpile stewardship applications have similar concerns, but without the complication of the high repetition rate needed for energy production. All proposed IFE or high yield systems have the challenge of protecting the reactor vessel and driver from the energy release, but pulsed power drivers have the additional challenge of establishing an electrical connection in a way that does not create excessive debris or require costly/massive structures that must be expendable. Fortunately, there are conceptual solutions in the form of very low mass transmission lines, usually in the form of wires or foils, since very little mass is needed to overcome the magnetic forces driving the transmission lines apart or provide a low resistance pathway given the short pulse duration of the driver. Several important aspects of the standoff problem were analyzed in the course of this LDRD project. Conceptual means of introducing the low mass transmission lines into the reactor chamber were proposed, including analysis of methods to retain the power flow gap between the electrodes in the presence of the chamber environment. The energy losses due to ohmic dissipation and magnetic acceleration of the low mass transmission lines were evaluated for candidate driver pulses and different transmission line masses, and the fluence of x-ray and neutron energy on the persistent power flow structures that must survive the pulse of energy were evaluated. Finally, means of rapidly pumping down the chamber were explored as a way to return the system to low pressure following the multi-atmosphere post-pulse pressure rise. The overall conclusion from the analysis is that, while the reactor environment creates stressful conditions for a low mass transmission line standoff system, there are concepts expending 10 kg or less of electrode mass each pulse with the potential to mitigate the stresses and successfully drive fusion targets. Whether such a system could lead to a practical and economical fusion reactor would require extensive research and development beyond the scope of the feasibility study.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

2020 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is essential for addressing the increasingly complex national workforce demands stemming from the growth of computational science and engineering challenges. Computational science and engineering (CSE) takes a multidisciplinary approach that utilizes scientific computing to tackle practical problems and provide technical tools across the spectrum of scientific discovery. The DOE CSGF specifically highlights high-performance computing (HPC) as a critical enabling technology in CSE, driving advancements in science and engineering that are vital to both the DOE and the broader economy. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines have been transformed through the augmentation of scientific observation via HPC. At government laboratories, academic institutions, and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, quantum information systems, fusion-reactor design, machine learning, additive manufacturing, nano materials for next-generation batteries and transistors, and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing. Computational biology, machine learning, and quantum computing are among the subjects that began to swell in the ranks of CSGF applicants before the labs were hiring as high a percentage of employees in these categories.” The explosion of scientific and technological data has heightened the demand for advanced high-performance computing (HPC) to transform these data into meaningful scientific insights. As access to vast amounts of data increases, the fields of Machine Learning and Artificial Intelligence are experiencing a resurgence, enhancing the established practices of computational modeling and simulation. In its September 2020 subcommittee report on "AI/ML, Data Intensive Science, and High-Performance Computing," the DOE Advanced Scientific Computing Advisory Committee (ASCAC) specifically called for a fellowship program to train computational and data scientists to address exascale and data-intensive computing challenges. This integration of empirical and theoretical modeling will increasingly guide federal policymakers in making decisions that impact American society and future generations. It demands a workforce of highly skilled and intellectually agile computational scientists capable of navigating the rapid advancements in scientific computing within the DOE National Laboratory research environment. The DOE CSGF program has consistently addressed this critical need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Completion document for MRT 8824 - Upgrade NIF’s gaseous radiochemistry diagnostics (RAGS) to support weapons science

This milestone highlights the successful upgrade and deployment of the Radiochemical Analysis of Gaseous Samples (RAGS) system, which delivers high-quality measurements (<20% uncertainty) of activated gaseous species from NIF implosions. These measurements are critical for supporting Stockpile Stewardship Program (SSP) relevant platforms, including LANL’s Double Shell and LLNL’s Pushered Single Shell campaigns. The RAGS diagnostic technique enables analysis of short-range mixing in implosions using high-Z shells, which are otherwise inaccessible to conventional x-ray diagnostic methods. By facilitating the investigation of high-Z material mixing into fusion burn, this system provides essential data for quantifying and interpreting results in high-energy density (HED) experiments. This report details the physics motivation, diagnostic fundamentals, planned and enacted upgrade work, and the quantification of uncertainty for the upgraded system.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2015

The advantages of beryllium and diamond capsules for the National Ignition Facility

As part of stockpile stewardship, the Inertial Confinement Fusion program has focused on exploring fusion burn at the National Ignition Facility. The process attempt to use lasers, rather than nuclear fission, to generate x-rays, implode, and fuse deuterium (D) tritium (T) in a small capsule. Laser light is directed into a gold or uranium cylinder (a "hohlraum") producing x-rays that penetrate, heat, and explode the exterior of a spherical capsule.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Recent progress on the National Ignition Facility advanced radiographic capability

The National Ignition Facility (NIF) is a megajoule (million-joule)-class laser and experimental facility built for Stockpile Stewardship and High Energy Density (HED) science research [1]. Up to several times a day, 192 laser pulses from NIF's 192 laser beamlines converge on a millimeter-scale target located at the center of the facility's 10-meter diameter target chamber. The carefully synchronized pulses, typically a few nanoseconds (billionths of a second) in duration and co-times to better than 20 picoseconds (trillionths of a second), a deliver a combined energy of up to 1.8 megajoules and a peak power of 500 terawatts (trillion watts). Furthermore, this drives temperatures inside the target to tens of millions of degrees and pressures to many billion times greater than Earth's atmosphere.

42 ENGINEERING↗
DOE OSTI2015

20 Years of Success: Science, Technology, and the Nuclear Weapons Stockpile

On Oct. 22, 2015, NNSA celebrated the proven success of the Stockpile Stewardship Program at a half-day public event featuring remarks by Secretary of Energy Ernest Moniz, Secretary of State John Kerry, and Under Secretary for Nuclear Security and NNSA Administrator Lt. Gen. (retired) Frank G. Klotz. The event also featured remarks by Deputy Secretary of Energy Elizabeth Sherwood-Randall and NNSA Principal Deputy Administrator Madelyn Creedon.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

A Long History of Supercomputing

As part of its national security science mission, Los Alamos National Laboratory and HPC have a long, entwined history dating back to the earliest days of computing. From bringing the first problem to the nation’s first computer to building the first machine to break the petaflop barrier, Los Alamos holds many “firsts” in HPC breakthroughs. Today, supercomputers are integral to stockpile stewardship and the Laboratory continues to work with vendors in developing the future of HPC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Breaking the Supermassive Black Hole Speed Limit

A new computer simulation helps explain the existence of puzzling supermassive black holes observed in the early universe. The simulation is based on a computer code used to understand the coupling of radiation and certain materials. “Supermassive black holes have a speed limit that governs how fast and how large they can grow,” said Joseph Smidt of the Theoretical Design Division at Los Alamos National Laboratory. “The relatively recent discovery of supermassive black holes in the early development of the universe raised a fundamental question, how did they get so big so fast?” Using computer codes developed at Los Alamos for modeling the interaction of matter and radiation related to the Lab’s stockpile stewardship mission, Smidt and colleagues created a simulation of collapsing stars that resulted in supermassive black holes forming in less time than expected, cosmologically speaking, in the first billion years of the universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Joint the Center for Applied Scientific Computing

The Center for Applied Scientific Computing serves as Livermore Lab’s window to the broader computer science, computational physics, applied mathematics, and data science research communities. In collaboration with academic, industrial, and other government laboratory partners, we conduct world-class scientific research and development on problems critical to national security. CASC applies the power of high-performance computing and the efficiency of modern computational methods to the realms of stockpile stewardship, cyber and energy security, and knowledge discovery for intelligence applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Los Alamos works on a biologically realistic computer network

Brain neuroscientists and computer scientists call this field neuromimetic computing—building computers inspired by how the cerebral cortex of the brain works. Its cortical processes rely on billions of small biological “switches,” called neurons. To learn, they store and process information. Using an approach called neural networks, researchers are developing computers that simulate neurons and their interconnections. Then computers can learn about their surroundings, interpret data, and make predictions based on it. In practice, however, researchers attempting to simulate neural processing at anything close to the scale and complexity of the brain’s cortical circuits have been stymied by limitations on computer memory and computational power. All that has changed with the new Trinity supercomputer at Los Alamos, which became fully operational in mid-2017. Trinity has unique capabilities designed for the stockpile stewardship mission, which includes highly complex nuclear simulations that have replaced the testing of nuclear weapons. All this capability means Trinity allows a fundamentally different approach to large-scale cortical simulations, enabling an unprecedented leap in the ability to model neural processing. To test that capability on a limited-scale problem, computer scientists and neuroscientists at Los Alamos created a “sparse prediction machine” that runs on a neural network on Trinity. A sparse prediction machine is designed to work like the brain: researchers expose it to data—in this case, various videos of a car driving down a road—without labeling the data in any way. Then the program sorts through that data frame by frame, focuses on the important information, and develops a prediction about the car’s motion. With Trinity’s power, the Los Alamos team simulates the way a brain handles information in its neurons but uses the fewest neurons at any given moment to explain the information at hand. That’s the “sparse” part, and it makes the brain very efficient—and, hopefully, a computer more efficient, too.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

MCNP Estimate of ZLS Lens Sensitivity in an X-Ray Field

The telecentric zoom lens system (ZLS) has proven to be invaluable in flash x-ray field operations and recent successful experiments pertaining to stockpile stewardship. The ZLS contains 11 custom-manufactured lenses, a turning mirror (pellicle), and an x-ray-to-visible-light converting scintillator. Images are recorded on a fully characterized CCD. All hardware is supported by computerized, programmable, electro-mechanical mounts and alignment apparatus. Seven different glass material types varying in chemical stoichiometry comprise the 11 ZLS lenses. All lenses within the ZLS are out of the path of direct x-ray radiation during normal operation. However, any unshielded scattered x-ray radiation can result in energy deposition into the lenses, which may generate some scintillating light that can couple into the CCD. This extra light may contribute to a decrease in signal-to-noise ratio (SNR) and lower the overall fidelity of the radiograph images. An estimate of the scintillation generation and sensitivities for each of the seven types of glass used as lenses in the ZLS is presented. This report also includes estimates of the total observed background decoupling that each of the lens material types contribute.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

MCNP Estimate of ZLS Lens Sensitivity in an X-Ray Field

The telecentric zoom lens system (ZLS) has proven to be invaluable in flash x-ray field operations and recent successful experiments pertaining to stockpile stewardship. The ZLS contains 11 custom-manufactured lenses, a turning mirror (pellicle), and an x-ray-to-visible-light converting scintillator. Images are recorded on a fully characterized CCD. All hardware is supported by computerized, programmable, electro-mechanical mounts and alignment apparatus. Seven different glass material types varying in chemical stoichiometry comprise the 11 ZLS lenses. All lenses within the ZLS are out of the path of direct x-ray radiation during normal operation. However, any unshielded scattered x-ray radiation can result in energy deposition into the lenses, which may generate some scintillating light that can couple into the CCD. This extra light may contribute to a decrease in signal-to-noise ratio (SNR) and lower the overall fidelity of the radiograph images.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SHOCKED SURFACE STEREO IMAGING WITH FIBER-OPTIC IMAGING PROBES

“A picture tells a thousand words”—this is a concept that captivates everyone, from the casual observer to the experimental physics scientist. To bring this concept to light in support of Stockpile Stewardship subcritical experiments executed at the Nevada National Security Site (NNSS), we have developed a new diagnostic tool to investigate the behavior of materials under high-explosive dynamic shock conditions. For the Leda subcritical experiment executed in August, 2014, a single line-of-sight imaging system was designed and fielded as a stretch diagnostic goal for National Security Technologies, LLC (NSTec). For the Lyra series, beginning with the Orpheus experiment in September 2015, a newly created dynamic stereo surface imaging (DSSI) capability was deployed. The DSSI diagnostic records a high-speed image sequence that allows scientists to peer inside an imploding hydrodynamic test article with stereoscopic imaging.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Verification of Upcoming MCNP Features For Estimating Nuclear Data Sensitivities in Fixed Source Simulations [Abstract]

Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Estimating List-Mode Data Sensitivities to Nuclear Data with MCNP6

Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗