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Recrystallization, melting, and erosion of dispersoid-strengthened tungsten materials during exposure to DIII-D plasmas

Kolasinski, Robert; Coburn, Jonathan D.; Truong, Dinh D.; Watkins, Jonathan; Abrams, Tyler; Fang, Z.Z.; Nygren, Richard; Leonard, Anthony; Ren, Jun; Wang, Huiqian; Whaley, Josh; Bykov, Igor; Glass, Fenton; Herfindal, Jeffrey; Hood, Ryan T.; Lasnier, Charles; Marini, Claudio; Mclean, Adam; Moser, Auna; Nishimoto, Ryan K.; Sugar, Joshua D.; Wilcox, Robert; York, Warren

Abstract not provided.

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TYPE Conference Presentation YEAR 2022
DOIOSTI

Peridynamics and surrogate modeling of pressure-driven well stimulation

International Journal of Rock Mechanics and Mining Sciences

Seidl, D.T.; Valiveti, Dakshina M.

In this work we use the peridynamics theory of solid mechanics to simulate fracture in an annular rock domain subject to an in-situ stress and create surrogate models that predict the area of the resulting cracks. Peridynamics is a non-local formulation of continuum mechanics that naturally accommodates material discontinuities. Furthermore, unlike other fracture modeling techniques there is no need to provide information about the crack path. We utilize the peridynamics code Peridigm and take a two-stage approach to fracture modeling. First an implicit solve is performed to compute the in-situ stress state. We then execute an explicit solve where a pressure loading designed to emulate fluid-driven hydraulic fracture is applied at the borehole and transmitted to the pre-stressed rock. We present results from polynomial and single and multi-level Gaussian process surrogate models constructed from a sampling study of the peridynamics model. The surrogates predict crack area given a measure of the in-situ stress anisotropy and rise time and amplitude of the pressure loading. These surrogates take a minuscule fraction of peridynamics model's running time to evaluate and are a step towards enabling advanced optimization and uncertainty quantification workflows that require many model evaluations.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Exceedance Response Action (ERA) Level 2 (Technical Report)

Engineering, Yorke

The Sandia National Laboratories, California (SNL/CA) site comprises approximately 410 acres and is located in the eastern portion of Livermore, Alameda County, California. The property is owned by the United States Department of Energy/National Nuclear Security Administration (DOE/NNSA) and is being managed and operated by National Technology & Engineering Solutions of Sandia, LLC (NTESS). Operations at the SNL/CA facility consist of DOE statutory responsibilities for nuclear weapon research and design, development of energy technologies, and basic scientific research. Specific industrial activities occur in discrete buildings and include electroplating or anodizing, machine shop, permitted hazardous waste treatment, storage and disposal facility (TSDF), and a scrap yard.

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TYPE Other Report YEAR 2022
DOIOSTI

Charon User Manual: v. 2.2 (revision1)

Musson, Lawrence C.; Hennigan, Gary L.; Gao, Xujiao; Humphreys, Richard; Negoita, Mihai; Huang, Andy

This manual gives usage information for the Charon semiconductor device simulator. Charon was developed to meet the modeling needs of Sandia National Laboratories and to improve on the capabilities of the commercial TCAD simulators; in particular, the additional capabilities are running very large simulations on parallel computers and modeling displacement damage and other radiation effects in significant detail. The parallel capabilities are based around the MPI interface which allows the code to be ported to a large number of parallel systems, including linux clusters and proprietary “big iron” systems found at the national laboratories and in large industrial settings.

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TYPE SAND Report YEAR 2022
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Evaluating the Impact of gRNA SNPs in CasRx Activity for Reducing Viral RNA in HCoV-OC43

Cells

Mayes, Cathryn; Santarpia, Joshua

Viruses within a given family often share common essential genes that are highly conserved due to their critical role for the virus’s replication and survival. In this work, we developed a proof-of-concept for a pan-coronavirus CRISPR effector system by designing CRISPR targets that are cross-reactive among essential genes of different human coronaviruses (HCoV). We designed CRISPR targets for both the RNA-dependent RNA polymerase (RdRp) gene as well as the nucleocapsid (N) gene in coronaviruses. Using sequencing alignment, we determined the most highly conserved regions of these genes to design guide RNA (gRNA) sequences. In regions that were not completely homologous among HCoV species, we introduced mismatches into the gRNA sequence and tested the efficacy of CasRx, a Cas13d type CRISPR effector, using reverse transcription quantitative polymerase chain reaction (RT-qPCR) in HCoV-OC43. We evaluated the effect that mismatches in gRNA sequences has on the cleavage activity of CasRx and found that this CRISPR effector can tolerate up to three mismatches while still maintaining its nuclease activity in HCoV-OC43 viral RNA. This work highlights the need to evaluate off-target effects of CasRx with gRNAs containing up to three mismatches in order to design safe and effective CRISPR experiments.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

“Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains”

Materials and Design

Garland, Anthony; Brown, Nathan K.; Fadel, Georges M.; Li, Gang

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.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Analysis of the Spontaneous Emission Limited Linewidth of an Integrated III–V/SiN Laser

Laser and Photonics Reviews

Chow, Weng W.

This article describes a calculation of the spontaneous emission limited linewidth of a semiconductor laser consisting of hybrid or heterogeneously integrated, silicon and III–V intracavity components. Central to the approach are a) description of the multi-element laser cavity in terms of composite laser/free-space eigenmodes, b) use of multimode laser theory to treat mode competition and multiwave mixing, and c) incorporation of quantum-optical contributions to account for spontaneous emission effects. Application of the model is illustrated for the case of linewidth narrowing in an InAs quantum-dot laser coupled to a high- (Formula presented.) SiN cavity.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Medium-Scale Methanol Pool Fire Model Validation

Journal of Heat Transfer

Hubbard, Joshua A.; Kirsch, Jared; Hewson, John C.; Hansen, Michael A.; Domino, Stefan P.

Medium scale (30 cm diameter) methanol pool fires were simulated using the latest fire modeling suite implemented in Sierra/Fuego, a low Mach number multiphysics reacting flow code. The sensitivity of model outputs to various model parameters was studied with the objective of providing model validation. This work also assesses model performance relative to other recently published large eddy simulations (LES) of the same validation case. Two pool surface boundary conditions were simulated. The first was a prescribed fuel mass flux and the second used an algorithm to predict mass flux based on a mass and energy balance at the fuel surface. Gray gas radiation model parameters (absorption coefficients and gas radiation sources) were varied to assess radiant heat losses to the surroundings and pool surface. The radiation model was calibrated by comparing the simulated radiant fraction of the plume to experimental data. The effects of mesh resolution were also quantified starting with a grid resolution representative of engineering type fire calculations and then uniformly refining that mesh in the plume region. Simulation data were compared to experimental data collected at the University of Waterloo and the National Institute of Standards and Technology (NIST). Validation data included plume temperature, radial and axial velocities, velocity temperature turbulent correlations, velocity velocity turbulent correlations, radiant and convective heat fluxes to the pool surface, and plume radiant fraction. Additional analyses were performed in the pool boundary layer to assess simulated flame anchoring and the effect on convective heat fluxes. This work assesses the capability of the latest Fuego physics and chemistry model suite and provides additional insight into pool fire modeling for nonluminous, nonsooting flames.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Sierra/SD - User's Manual - 5.8

Foulk, James W.; Bunting, Gregory; Chen, Mark J.Y.; Crane, Nathan K.; Day, David M.; Dohrmann, Clark R.; Joshi, Sidharth S.; Lindsay, Payton; Plews, Julia A.; Stevens, Brian; Vo, Johnathan

Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high-fidelity, validated models used in modal, vibration, static and shock analysis of weapons systems. This document provides a user's guide to the input for Sierra/SD. Details of input specifications for the different solution types, output options, element types and parameters are included. The appendices contain detailed examples, and instructions for running the software on parallel platforms.

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TYPE SAND Report YEAR 2022
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Results 6801–6900 of 99,299
Results 6801–6900 of 99,299