High gain in hotspot-ignition inertial confinement fusion (ICF) implosions requires the propagation of thermonuclear burn from a central hotspot to the surrounding cold dense fuel. As ICF experiments enter the burning plasma regime, diagnostic signatures of burn propagation must be identified. In previous work [A. J. Crilly et al., Phys. Plasmas 27(1), 012701 (2020)], it has been shown that the spectral shape of the neutron backscatter edges is sensitive to the dense fuel hydrodynamic conditions. The backscatter edges are prominent features in the ICF neutron spectrum produced by the 180° scattering of primary deuterium–tritium fusion neutrons from ions. In this work, synthetic neutron spectra from radiation-hydrodynamics simulations of burning ICF implosions are used to assess the backscatter edge analysis in a propagating burn regime. Significant changes to the edge's spectral shape are observed as the degree of burn increases, and a simplified analysis is developed to infer scatter-averaged fluid velocity and temperature. The backscatter analysis offers direct measurement of the increased dense fuel temperatures that result from burn propagation.
This report presents a specification for the Portals 4 network programming interface. Portals 4 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded systems. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is targeted to the next generation of machines employing advanced network interface architectures that support enhanced offload capabilities.
Response to elongational flow is fundamental to soft matter and directly impacts new developments in a broad range of technologies form polymer processing and microfluidics to controlled flow in biosystems. Of particular significance are the effects of elongational flow on self-assembled systems where the interactions between the fundamental building blocks control their adaptation. Here we probe the effects of associating groups on the structure and dynamics of linear polymer melts in uniaxial elongation using molecular dynamics simulations. We study model polymers with randomly incorporated backbone associations with interaction strengths varying from 1kBT to 10kBT. These associating groups drive the formation of clusters in equilibrium with an average size that increases with interaction strength. Flow drives these clusters to continuously break and reform as chains stretch. These flow-driven cluster dynamics drive a qualitative transition in polymer elongation dynamics from homogeneous to nanoscale localized yield and cavitation as the association strength increases.
The inverter firmware was upgraded to version 1.09 and an initial assessment was conducted on the inverter using the equipment listed above and the response of the inverter can be seen in the following plots. This work is to base-line the response of the inverter to utility conditions and commands and further work will involve the interoperability aspect of the inverter using SunSpec dashboard to conduct the tests and configure the inverter.
Advances in machine learning algorithms and increased computational efficiencies give engineers new capabilities and tools to apply to engineering design. Machine learning models can approximate complex functions and, therefore, can be useful for various tasks in the engineering design workflow. This paper investigates using reinforcement learning (RL), a subset of machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to automate the designing of 2D discretized topologies. RL agents use past experiences to learn sequential sets of actions to best achieve some objective. In the proposed environment, an RL agent can make sequential decisions to design a topology by removing elements to best satisfy compliance minimization objectives. After each action, the agent receives feedback by evaluating how well the current topology satisfies the design objectives. After training, the agent was tasked with designing optimal topologies under various load cases. The agent's proposed designs had similar or better compliance minimization performance to those produced by traditional gradient-based topology optimization methods. These results show that a deep RL agent can learn generalized design strategies to satisfy multi-objective design tasks and, therefore, shows promise as a tool for arbitrarily complex design problems across many domains.
This user’s guide documents capabilities in Sierra/SolidMechanics which remain “in-development” and thus are not tested and hardened to the standards of capabilities listed in Sierra/SM 5.8 User’s Guide. Capabilities documented herein are available in Sierra/SM for experimental use only until their official release. These capabilities include, but are not limited to, novel discretization approaches such as the conforming reproducing kernel (CRK) method, numerical fracture and failure modeling aids such as the extended finite element method (XFEM) and J-integral, explicit time step control techniques, dynamic mesh rebalancing, as well as a variety of new material models and finite element formulations.
The Department of Energy, National Nuclear Security Administration, Sandia Field Office, and National Technology & Engineering Solutions of Sandia, LLC (NTESS), the management and operating contractor for Sandia National Laboratories, California (SNL/CA), are responding to the Regional Water Quality Control Board (Water Board) comments dated February 16, 2022 (Enclosure 1) on the report titled Soil Sampling Results for Closure of a Portion of Solid Waste Management Unit (SWMU) #16 dated January 6, 2022 (Report).
To meet stringent emissions regulations on soot emissions, it is critical to further advance the fundamental understanding of in-cylinder soot formation and oxidation processes. Among several optical techniques for soot quantification, diffuse back-illumination extinction imaging (DBI-EI) has recently gained traction mainly due to its ability to compensate for beam steering, which if not addressed, can cause unacceptably high measurement uncertainty. Until now, DBI-EI has only been used to measure the amount of soot along the line of sight, and in this work, we extend the capabilities of a DBI-EI setup to also measure in-cylinder soot temperature. This proof of concept of diffuse back-illumination temperature imaging (DBI-TI) as a soot thermometry technique is presented by implementing DBI-TI in a single cylinder, heavy-duty, optical diesel engine to provide 2-D line-of-sight integrated soot temperature maps. The potential of DBI-TI to be an accurate thermometry technique for use in optical engines is analyzed. The achievable accuracy is due in part to simultaneous measurement of the soot extinction, which circumvents the uncertainty in dispersion coefficients that depend on the optical properties of soot and the wavelength of light utilized. Analysis shows that DBI-TI provides temperature estimates that are closer to the mass-averaged soot temperature when compared to other thermometry techniques that are more sensitive to soot temperature closer to the detector. Furthermore, uncertainty analysis and Monte Carlo (MC) simulations provide estimates of the temperature measurement errors associated with this technique. The MC simulations reveal that for the light intensities and optical densities encountered in these experiments, the accuracy of the DBI-TI technique is comparable or even better than other established optical thermometry techniques. Thus, DBI-TI promises to be an easily implementable extension to the existing DBI-EI technique, thereby extending its ability to provide comprehensive line-of-sight integrated information on soot.
Stochastic modelling approaches are presented to capture random effects at multiple time and length scales. Random processes that occur at the microscale produce nondeterministic effects at the macroscale. Here we present three stochastic modeling approaches that describe random processes at microscopic length scales and map these processes to the macroscopic length scale. The first stochastic modeling approach is based upon a particle based numerical technique to solve a Stochastic Differential Equation (SDE) using an arbitrary diffusion process to capture random processes at the microstructural level. The second approach prescribes a Probability Density Function (PDF) for the drift and diffusion of the random variable derived using the forward and backward Kolmogorov equations. This method requires mean and drift evolution PDF transport equations. The third approach is the coupling of multiple random variables which are dependent on each other. The relationship of the PDFs and a coupling function, known as a copula, produces a Joint Probability Density Function (JPDF). These stochastic modeling approaches are implemented into a Multiple Component (MC) shock physics computational code and used to model statistical fracture and reactive flow applications.
