Cherenkov Gamma Ray Detectors on High-Energy-Density Systems

High energy density (HED) systems are some of the most extreme environments ever created by mankind. Systems with pressures greater than 1 MBar can only be created by a handful of devices on earth, often utilizing high intensity lasers or pulsed power machines. HED systems offer a view into an extreme form of matter only seen in stellar cores, supernovas and other powerful astrophysical systems. Creating HED systems on Earth offer the possibility, if the physics and technology can be matured, to one day create a fusion power plant. If a system is hot and dense enough, the fusion reaction can drive more fusion reactions through the alpha particle depositing its energy. There has been a generation goal of getting this fusion chain reaction to create high yield and gain, however, this regime, called ignition, has remained elusive. Diagnosing HED systems to understand the degradation preventing ignition presents a challenge. HED pockets are often short lived (nanoseconds or picoseconds) and concurrent with a release of a physical blast shock, nuclear particles, a strong electromagnetic pulse and a noisy radiation environment. Cherenkov detectors to measure energy thresholded, time resolved gamma rays is one such technique that can be applied to these systems to gain insight and understanding to the properties of HED systems. In this dissertation, the fundamentals and background of this diagnostic technique are applied in detail to three HED systems. Gas Cherenkov detectors are applied onto the National Ignition Facility\u27s inertial confinement fusion ignition campaigns. Inertial confinement fusion uses high powered lasers to compress a capsule full of deuterium-tritium fuel surrounding by a carbon ablator shell, which is used to push the fuel into high densities and temperatures. A technique to isolate a 4.4 MeV carbon gamma ray is introduced and applied to understand the areal density and compression of the outside portion of the inertial confinement fusion pusher. The new data reveals that the outside portion of the pusher followed the expected hydrodynamic predicted trends, while the inner fuel portion of the pusher looks to be degraded and less compressible. This data suggests for specific degradation mechanisms acting on the capsule that preferentially degrade the fuel, such as ablator-ice mix. The time resolution of the gamma detector also gives information about the velocity of the carbon ablator during the fusion burn. Second, gamma ray measure mix studied field on the OMEGA laser system are shared, showing a complicated mix landscape for spherical implosions. Aerogel Cherenkov detectors are fielded on the Mercury pulsed power facility at the Naval Research Lab which creates an accelerated electron beam to create a strong bremsstrahlung x-ray source. The Aerogel Cherenkov detectors are able to characterize and measure time resolved signals of the x-ray pulse, vital information for the x-ray pulse to be feed into a radiography or photofission source. This dissertation presents a body of work that applies a diagnostic technique to an extreme experimental conditions, receives novel measurements and interprets their results

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

Towards a solution of the closure problem for convective atmospheric boundary-layer turbulence

We consider the closure problem for turbulence in the dry convective atmospheric boundary layer (CBL). Transport in the CBL is carried by small scale eddies near the surface and large plumes in the well mixed middle part up to the inversion that separates the CBL from the stably stratified air above. An analytically tractable model based on a multivariate Delta-PDF approach is developed. It is an extension of the model of Gryanik and Hartmann [1] (GH02) that additionally includes a term for background turbulence. Thus an exact solution is derived and all higher order moments (HOMs) are explained by second order moments, correlation coefficients and the skewness. The solution provides a proof of the extended universality hypothesis of GH02 which is the refinement of the Millionshchikov hypothesis (quasi- normality of FOM). This refined hypothesis states that CBL turbulence can be considered as result of a linear interpolation between the Gaussian and the very skewed turbulence regimes. Although the extended universality hypothesis was confirmed by results of field measurements, LES and DNS simulations (see e.g. [2-4]), several questions remained unexplained. These are now answered by the new model including the reasons of the universality of the functional form of the HOMs, the significant scatter of the values of the coefficients and the source of the magic of the linear interpolation. Finally, the closures 61 predicted by the model are tested against measurements and LES data. Some of the other issues of CBL turbulence, e.g. familiar kurtosis-skewness relationships and relation of area coverage parameters of plumes (so called filling factors) with HOM will be discussed also

Laboratory Directed Research and Development FY2010 Annual Report

A premier applied-science laboratory, Lawrence Livermore National Laboratory (LLNL) has at its core a primary national security mission - to ensure the safety, security, and reliability of the nation's nuclear weapons stockpile without nuclear testing, and to prevent and counter the spread and use of weapons of mass destruction: nuclear, chemical, and biological. The Laboratory uses the scientific and engineering expertise and facilities developed for its primary mission to pursue advanced technologies to meet other important national security needs - homeland defense, military operations, and missile defense, for example - that evolve in response to emerging threats. For broader national needs, LLNL executes programs in energy security, climate change and long-term energy needs, environmental assessment and management, bioscience and technology to improve human health, and for breakthroughs in fundamental science and technology. With this multidisciplinary expertise, the Laboratory serves as a science and technology resource to the U.S. government and as a partner with industry and academia. This annual report discusses the following topics: (1) Advanced Sensors and Instrumentation; (2) Biological Sciences; (3) Chemistry; (4) Earth and Space Sciences; (5) Energy Supply and Use; (6) Engineering and Manufacturing Processes; (7) Materials Science and Technology; Mathematics and Computing Science; (8) Nuclear Science and Engineering; and (9) Physics