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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-I-78: Design of Diagnostic Multileaf Collimator Based On the Monte Carlo Simulation for Dose Reduction of Diagnostic Radiography

Purpose: A diagnostics Multileaf Collimator (MLC) was designed for diagnostic radiography dose reduction. Monte Carlo simulation was used to evaluate efficiency of shielding material for producing leaves of Multileaf collimator. Material & Methods: The general radiography unit (Rex-650R, Listem, Korea) was modeling with Monte Carlo simulation (MCNPX, LANL, USA) and we used SRS-78 program to calculate the energy spectrum of tube voltage (80, 100, 120 kVp). The shielding materials was SKD 11 alloy tool steel that is composed of 1.6% carbon(C), 0.4% silicon (Si), 0.6% manganese (Mn), 5% chromium (Cr), 1% molybdenum (Mo), and vanadium (V). The density of it was 7.89 g/m3. We simulated leafs diagnostic MLC using SKD 11 with general radiography unit. We calculated efficiency of diagnostic MLC using tally6 card of MCNPX depending on energy. Results: The diagnostic MLC consisted of 25 individual metal shielding leaves on both sides, with dimensions of 10 × 0.5 × 0.5 cm3. The leaves of MLC were controlled by motors positioned on both sides of the MLC. According to energy (tube voltage), the shielding efficiency of MLC in Monte Carlo simulation was 99% (80 kVp), 96% (100 kVp) and 93% (120 kVp). Conclusion: We certified efficiency of diagnostic MLC fabricated from SKD11 alloy tool steel. Based on the results, the diagnostic MLC was designed. We will make the diagnostic MLC for dose reduction of diagnostic radiography.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

Study of Light Neutron-Rich Nuclei Using a Multilayer Semiconductor Setup

The characteristics of two modifications of the semiconductor (s.c.d.) setup consisting of telescopes on the basis of silicon detectors are presented. These settings allow performing a precision measurement of energy in a large dynamic range (from a few to hundreds of MeV) and particle identification in a wide range of masses. The issues of measurement of the characteristics of s.c.d. telescopes and their impact on the quality of the obtained experimental data are considered. Considerable attention is paid to the use of created semiconductor devices for the search for and spectroscopy of light exotic nuclei on the accelerators of PNPI (Gatchina) and LANL (Los Alamos).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Multilayer Semiconductor Charged-Particle Spectrometers for Accelerator Experiments

The current state of studies in the field of development of multilayer semiconductor systems (semiconductor detector (SCD) telescopes), which allow the energy to be precisely measured within a large dynamic range (from a few to a few hundred MeV) and the particles to be identified in a wide mass range (from pions to multiply charged nuclear fragments), is presented. The techniques for manufacturing the SCD telescopes from silicon and high-purity germanium are described. The issues of measuring characteristics of the constructed detectors and their impact on the energy resolution of the SCD telescopes and on the quality of the experimental data are considered. Much attention is given to the use of the constructed semiconductor devices in experimental studies at accelerators of PNPI (Gatchina), LANL (Los Alamos) and CELSIUS (Uppsala).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap'' between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Anomaly detection, Attention, Recurrent Neural Net↗
DOE OSTI2016

MADSpython 1.x

MADSpython (Model analysis and decision support tools in Python) is a code in Python that streamlines the process of using data and models for analysis and decision support using the code MADS. MADS is open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11-035). MADS can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. The Python scripts in MADSpython facilitate the generation of input and output file needed by MADS as wells as the external simulators which include FEHM and PFLOTRAN. MADSpython enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. MADSpython will be released under GPL V3 license. MADSpython will be distributed as a Git repo at gitlab.com and github.com. MADSpython manual and documentation will be posted at http://madspy.lanl.gov.

O'Malley, Daniel↗
DOE OSTI2016

Multi Infrastructure Control and Optimization Toolkit, Resilient Design Module (MICOT-RDT), version 2.X

MICOT is a tool for optimizing and controlling infrastructure systems. In includes modules for optimizing the operations of an infrastructure structure (for example optimal dispatch), designing infrastructure systems, restoring infrastructures systems, resiliency, preparing for natural disasters, interdicting networks, state estimation, sensor placement, and simulation of infrastructure systems. It implements algorithms developed at LANL that have been published in the academic community. This is a release of the of resilient design module of the MICOT.

Bent, Russell↗
DOE OSTI2017

Charliecloud

Charliecloud is a set of scripts to let users run a virtual cluster of virtual machines (VMs) on a desktop or supercomputer. Key functions include: 1. Creating (typically by installing an operating system from vendor media) and updating VM images; 2. Running a single VM; 3. Running multiple VMs in a virtual cluster. The virtual machines can talk to one another over the network and (in some cases) the outside world. This is accomplished by calling external programs such as QEMU and the Virtual Distributed Ethernet (VDE) suite. The goal is to let users have a virtual cluster containing nodes where they have privileged access, while isolating that privilege within the virtual cluster so it cannot affect the physical compute resources. Host configuration enforces security; this is not included in Charliecloud, though security guidelines are included in its documentation and Charliecloud is designed to facilitate such configuration. Charliecloud manages passing information from host computers into and out of the virtual machines, such as parameters of the virtual cluster, input data specified by the user, output data from virtual compute jobs, VM console display, and network connections (e.g., SSH or X11). Parameters for the virtual cluster (number of VMs, RAM and disk per VM, etc.) are specified by the user or gathered from the environment (e.g., SLURM environment variables). Example job scripts are included. These include computation examples (such as a "hello world" MPI job) as well as performance tests. They also include a security test script to verify that the virtual cluster is appropriately sandboxed. Tests include: 1. Pinging hosts inside and outside the virtual cluster to explore connectivity; 2. Port scans (again inside and outside) to see what services are available; 3. Sniffing tests to see what traffic is visible to running VMs; 4. IP address spoofing to test network functionality in this case; 5. File access tests to make sure host access permissions are enforced. This test script is not a comprehensive scanner and does not test for specific vulnerabilities. Importantly, no information about physical hosts or network topology is included in this script (or any of Charliecloud); while part of a sensible test, such information is specified by the user when the test is run. That is, one cannot learn anything about the LANL network or computing infrastructure by examining Charliecloud code.

Priedhorsky, Reid↗
DOE OSTI2018

Fast Equilibration of Ocean Tracers Software

The Fast Equilibration of Ocean Tracers Software (FEOTS) is a set of Fortran modules and programs for post-processing output from LANL's Parallel Ocean Program (POP). However, it is written so that it can be extended for use with other General Circulation Models. Tools are provided for aiding in the diagnosis of sparse matrices that capture advection and diffusion operators that are consistent with General Circulation Model discretizations. FEOTS additionally offers tools to make use of the diagnosed operators to run offline tracer models in forward or equilibration modes.

Zhang, Jiaxu↗
DOE OSTI2017

PetaVision version 2.0

PetaVision is an open source, object oriented neural simulation toolbox optimized for high-performance multi-core, multi-node computer architectures. PetaVision is intended for computational neuroscientists who seek to apply neuromorphic models to hard signal processing problems; both to improve on the performance of existing algorithms and/or to gain insight into the computational mechanisms underlying biological neural processing. Please note that this assertion and ESTSC deposit are only for the portions of PetaVision which have been funded and written by LANL developers.

Lundquist, Sheng↗
DOE OSTI2018

MAMA Plugin API

Plugin API for the LANL Mama (Morphological Analysis of Materials) application.

Oyen, Diane↗
DOE OSTI2018

FEHM V3.0 - V3.X

The numerical background of the FEHM computer code can be traced to the early 1970s when it was used to simulate geothermal and hot dry rock reservoirs. The primary use over a number of years was to assist in the understanding of flow fields and mass transport in the saturated and unsaturated zones below the potential Yucca Mountain repository. Today FEHM is used to simulate groundwater and contaminant flow and transport in deep and shallow, fractured and un-fractured porous media throughout the US DOE complex. FEHM has proved to be a valuable asset on a variety of projects of national interest including Environmental Remediation of the Nevada Test Site, the LANL Groundwater Protection Program, geologic CO2 sequestration, Enhanced Geothermal Energy (EGS) programs, Oil and Gas production, Nuclear Waste Isolation, and Arctic Permafrost. Subsurface physics has ranged from single fluid/single phase fluid flow when simulating basin scale groundwater aquifers to complex multifluid/ multi-phase fluid flow that includes phase change with boiling and condensing in applications such as unsaturated zone surrounding nuclear waste storage facility or leakage of CO2/brine through faults or wellbores. The numerical method used in FEHM is the control volume method (CV) for fluid flow and heat transfer equations which allows FEHM to exactly enforce energy/mass conservation; while an option is available to use the finite element (FE) method for displacement equations to obtain more accurate stress calculations. In addition to these standard methods, an option to use FE for flow is available, as well as a simple Finite Difference scheme.

Pawar, Rajesh↗
DOE OSTI2018

RMA-MT Benchmarks

The RMA-MT benchmarks are a suite of performance tests for multi-threaded Message Passing Interface (MPI) Remote Memory Access (RMA) implementations. The suite includes tests for bandwidth and latency with any number of threads. This suite was originally developed and release by Sandia National Laboratory. LANL modifications to the suite include a complete re-write of much of the source code, bi-directional bandwidth, and message rate tests.

Hjelm, Nathan↗
DOE OSTI2018

System Administration Tools

High Performance Computing clusters and supercomputers are expensive and complex systems to manage. The software tools to manage these systems are also aging to the point where they are no longer actively developed and their methodology is antiquated. There has been a surge of new methodologies and software tools developed by the cloud providers to maximize availability of services and scalability of server deployment. The authors would like to utilize these new software tools to distribute our proof of concept, named Kraken, that will be responsible for booting and managing LANL's HPC systems in the future

Peltz, Paul↗
DOE OSTI2017

ShiftNMFk 1.2

The ShiftNMFk1.2 code, or as we call it, GreenNMFk, represents a hybrid algorithm combining unsupervised adaptive machine learning and Green's function inverse method. GreenNMFk allows an efficient and high performance de-mixing and feature extraction of a multitude of nonnegative signals that change their shape propagating through the medium. The signals are mixed and recorded by a network of uncorrelated sensors. The code couples Non-negative Matrix Factorization (NMF) and inverse-analysis Green's functions method. GreenNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of the governing partial differential equation to identify the unknown sources and their charecteristics. GreenNMF can be applied directly to any problem controlled by a known partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations. Full GreenNMFk method is a subject LANL U.S. Patent application S133364.000 August, 2017. The ShiftNMFk 1.2 version here is a toy version of this method that can work with a limited number of unknown sources (4 or less).

Alexandrov, BoianS.↗
DOE OSTI2020

Kokkos Clang

This will be a fork of the LLVM project which will contain enhancements to help Kokkos users. It will also wrap in contributions from LANL and Argonne, that we might also give our users access to that work. We will eventually try to upstream (give back to LLVM) any changes we make, if possible. SAND2020-12447 O Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Lewis, Cannada↗
DOE OSTI2020

HSNMon: High-Speed Monitoring Software for Omni-Path

HSNMon is a robust high speed network monitoring daemon that provides insight into network availability, hardware failure, and network performance for switches and network cards. HSNMon collects counters, forwarding tables, and error information at high resolution using publicly available Omni-Path Management APIs. The collected data can be forwarded into multiple logging infrastructures including syslog and message queuing systems. HSNMon is only the infrastructure for collecting the network data. It does not include any collected data or proprietary LANL information

Holman, Brett↗
DOE OSTI2023

OrthoPhylo

This software builds on PHAME developed at LANL to generate phylogenetic trees of bacterial whole genome sequences. Where PHAME uses whole genome alignments to generate informative sites to base tress on, PHAME-OuS annotates bacterial genes, identifies orthologous sequences, aligns related proteins, uses those alignments to inform transcript alignments, then builds trees with several methods. The first is a conventional gene concatenation and ML tree estemation method. The second attempts to reconcile gene tree with a unified species tree using quartets (ASTRAL). Both methods allow filtering of gene lists on number of species represented, length, and gappiness in order to tune noise-to-signal for tree estimation

Middlebrook, Earl↗