The CABle ANAlysis (CABANA) portion of the EMPHASIS(TM) suite is designed specifically for the simulation of cable SGEMP. The code can be used to evaluate the response of a specific cable design to threat or to compare and minimize the relative response of difference d esigns. This document provides user - specific information to facilitate the application of the code to cables of interest. Acknowledgement The authors would like to thank all of those individuals who have helped to bring CABANA to the point it is today, including Gary Scrivner and Wesley Fan for many useful theory and design discussions.
EMPHASIS TM /NEVADA is the SIERRA/NEVADA toolkit implementation of portions of the EMP HASIS TM code suite. The purpose of the toolkit i m- plementation is to facilitate coupling to other physics drivers such as radi a- tion transport as well as to better manage code design, implementation, co m- plexity, and important verification and validation processes. This document describes the theory and implementation of the unstructured finite - element method solver , associated algorithms, and selected verification and valid a- tion . Acknowledgement The author would like to recognize all of the ALEGRA team members for their gracious and willing support through this initial Nevada toolkit - implementation process. Although much of the knowledge needed was gleaned from document a- tion and code context, they were always willing to consult personally on some of the less obvious issues and enhancements necessary.
This analysis utilizes a 5 - MW VAWT topside design envelope created by Sandia National Laboratories to compare floating platform options for each turbine in the design space. The platform designs are based on two existing designs, the OC3 Hywind spar - buoy and Principal Power's WindFloat semi - submersible. These designs are scaled using Froude - scaling relationships to determine an appropriately sized spar - buoy and semi - submersible design for each topside. Both the physical size of the required platform as well as mooring configurations are considered. Results are compared with a comparable 5 - MW HAWT in order to identify potential differences in the platform and mooring sizing between the VAWT and HAWT. The study shows that there is potential for cost savings due to reduced platform size requirements for the VAWT.
A n indust ry standard wave modeling tool was utilized to investigate model sensitivity to input parameters and wave energy converter ( WEC ) array deploym ent scenarios. Wave propagation was investigated d ownstream of the WECs to evaluate overall near - and far - field effects of WEC arrays. The sensitivity study illustrate d that b oth wave height and near - bottom orbital velocity we re subject to the largest pote ntial variations, each decreas ed in sensitivity as transmission coefficient increase d , as number and spacing of WEC devices decrease d , and as the deployment location move d offshore. Wave direction wa s affected consistently for all parameters and wave perio d was not affected (or negligibly affected) by varying model parameters or WEC configuration .
The future of extreme-scale computing is expected to magnify the influence of soft faults as a source of inaccuracy or failure in solutions obtained from distributed parallel computations. The development of resilient computational tools represents an essential recourse for understanding the best methods for absorbing the impacts of soft faults without sacrificing solution accuracy. The Rexsss (Resilient Extreme Scale Scientific Simulations) project pursues the development of fault resilient algorithms for solving partial differential equations (PDEs) on distributed systems. Performance analyses of current algorithm implementations assist in the identification of runtime inefficiencies.
Sensitivity analysis is a crucial element of rigorous engineering analysis, but performing such an analysis on a complex model is difficult and time consuming. The mission of the DART Workbench team at Sandia National Laboratories is to lower the barriers to adoption of advanced analysis tools through software integration. The integrated environment guides the engineer in the use of these integrated tools and greatly reduces the cycle time for engineering analysis.
Smith, Christopher S.; Bull, Diana L.; Willits, Steven M.; Fontaine, Arnold A.
This Technical Report presents work completed by The Applied Research Laboratory at The Pennsylvania State University, in conjunction with Sandia National Labs, on the optimization of the power conversion chain (PCC) design to maximize the Average Annual Electric Power (AAEP) output of an Oscillating Water Column (OWC) device. The design consists of two independent stages. First, the design of a floating OWC, a Backward Bent Duct Buoy (BBDB), and second the design of the PCC. The pneumatic power output of the BBDB in random waves is optimized through the use of a hydrodynamically coupled, linear, frequency-domain, performance model that links the oscillating structure to internal air-pressure fluctuations. The PCC optimization is centered on the selection and sizing of a Wells Turbine and electric power generation equipment. The optimization of the PCC involves the following variables: the type of Wells Turbine (fixed or variable pitched, with and without guide vanes), the radius of the turbine, the optimal vent pressure, the sizing of the power electronics, and number of turbines. Also included in this Technical Report are further details on how rotor thrust and torque are estimated, along with further details on the type of variable frequency drive selected.
Sandia National Laboratories, working with Energy Sense Finance developed the proof-ofconcept PV Valueª tool in 2011 to provide real estate appraisers a tool that can be used to develop the market value and fair market value of a solar photovoltaic system. PV Valueª moved from a proof-of-concept spreadsheet to a commercial web-based tool developed and operated exclusively by Energy Sense Finance in June 2014. This paper presents the results of a survey aimed at different user categories in order to measure how the tool is being used in the marketplace as well as elicit information that can be used to improve the tools effectiveness.
Oxy-fuel combustion technology with carbon capture and storage could significantly reduce global CO2 emissions, a greenhouse gas. Implementation can be aided by computational fluid dynamics (CFD) simulations, which require an accurate understanding of coal particle kinetics as they go through combustion in a range of environments. To understand the kinetics of pulverized coal char combustion, a heated flow reactor was operated under a wide range of experimental conditions. We varied the environment for combustion by modifying the diluent gas, oxygen concentration, gas flow rate, and temperature of the reactor/reacting gases. Measurements of reacting particle temperatures were made for a sub-bituminous and bituminous coal char, in environments with CO2 or N2 as the diluent gas, with 12, 24, and 36 vol-% oxygen concentration, at 50, 80, 100, and 200 standard liters per minute flowing through the reactor, reactor temperatures of 1200, 1400 K, at pressures slightly above atmospheric. The data shows consistent increasing particle temperature with increased oxygen concentration, reactor temperature and higher particle temperatures for N2 diluent than CO2. We also see the effects of CO2 gasification when different ranks of coal are used, and how the reduction in the temperature due to the CO2 diluent is greater for the coal char that has higher reactivity. Quantitative measurements for temperature are not yet complete due to ongoing calibration of detection systems.
The Water Security Toolkit (WST) is a suite of open source software tools that can be used by water utilities to create response strategies to reduce the impact of contamination in a water distribution network . WST includes hydraulic and water quality modeling software , optimizati on methodologies , and visualization tools to identify: (1) sensor locations to detect contamination, (2) locations in the network in which the contamination was introduced, (3) hydrants to remove contaminated water from the distribution system, (4) locations in the network to inject decontamination agents to inactivate, remove, or destroy contaminants, (5) locations in the network to take grab sample s to help identify the source of contamination and (6) valves to close in order to isolate contaminate d areas of the network. This user manual describes the different components of WST , along w ith examples and case studies. License Notice The Water Security Toolkit (WST) v.1.2 Copyright c 2012 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive license for use of this work by or on behalf of the U.S. government. This software is distributed under the Revised BSD License (see below). In addition, WST leverages a variety of third-party software packages, which have separate licensing policies: Acro Revised BSD License argparse Python Software Foundation License Boost Boost Software License Coopr Revised BSD License Coverage BSD License Distribute Python Software Foundation License / Zope Public License EPANET Public Domain EPANET-ERD Revised BSD License EPANET-MSX GNU Lesser General Public License (LGPL) v.3 gcovr Revised BSD License GRASP AT&T Commercial License for noncommercial use; includes randomsample and sideconstraints executable files LZMA SDK Public Domain nose GNU Lesser General Public License (LGPL) v.2.1 ordereddict MIT License pip MIT License PLY BSD License PyEPANET Revised BSD License Pyro MIT License PyUtilib Revised BSD License PyYAML MIT License runpy2 Python Software Foundation License setuptools Python Software Foundation License / Zope Public License six MIT License TinyXML zlib License unittest2 BSD License Utilib Revised BSD License virtualenv MIT License Vol Common Public License vpykit Revised BSD License Additionally, some precompiled WST binary distributions might bundle other third-party executables files: Coliny Revised BSD License (part of Acro project) Dakota GNU Lesser General Public License (LGPL) v.2.1 PICO Revised BSD License (part of Acro project) i Revised BSD License Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Sandia National Laboratories nor Sandia Corporation nor the names of its con- tributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IM- PLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUD- ING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ii Acknowledgements This work was supported by the U.S. Environmental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The material in this document has been subject to technical and policy review by the U.S. EPA, and approved for publication. The views expressed by individual authors, however, are their own, and do not necessarily reflect those of the U.S. Environmental Protection Agency. Mention of trade names, products, or services does not convey official U.S. EPA approval, endorsement, or recommendation. The Water Security Toolkit is an extension of the Threat Ensemble Vulnerability Assessment-Sensor Place- ment Optimization Tool (TEVA-SPOT), which was also developed with funding from the U.S. Environ- mental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The authors acknowledge the following individuals for their contributions to the devel- opment of TEVA-SPOT: Jonathan Berry (Sandia National Laboratories), Erik Boman (Sandia National Laboratories), Lee Ann Riesen (Sandia National Laboratories), James Uber (University of Cincinnati), and Jean-Paul Watson (Sandia National Laboratories). iii Acronyms ATUS American Time-Use Survey BLAS Basic linear algebra sub-routines CFU Colony-forming unit CVAR Conditional value at risk CWS Contamination warning system EA Evolutionary algorithm EDS Event detection system EPA U.S. Environmental Protection Agency EC Extent of Contamination ERD EPANET results database file GLPK GNU Linear Programming Kit GRASP Greedy randomized adaptive sampling process HEX Hexadecimal HTML HyperText markup language INP EPANET input file LP Linear program MC Mass consumed MILP Mixed integer linear program MIP Mixed integer program MSX Multi-species extension for EPANET NFD Number of failed detections NS Number of sensors NZD Non-zero demand PD Population dosed PE Population exposed PK Population killed TAI Threat assessment input file TCE Tailed-conditioned expectation TD Time to detection TEC Timed extent of contamination TEVA Threat ensemble vulnerability assessment TSB Tryptic soy broth TSG Threat scenario generation file TSI Threat simulation input file VAR Value at risk VC Volume consumed WST Water Security Toolkit YML YAML configuration file format for WST iv Symbols Notation Definition Example { , } set brackets { 1,2,3 } means a set containing the values 1,2, and 3. [?] is an element of s [?] S means that s is an element of the set S . [?] for all s = 1 [?] s [?] S means that the statement s = 1 is true for all s in set S . P summation P n i =1 s i means s 1 + s 2 + * * * + s n . \ set minus S \ T means the set that contains all those elements of S that are not in set T . %7C given %7C is used to define conditional probability. P ( s %7C t ) means the prob- ability of s occurring given that t occurs. %7C ... %7C cardinality Cardinality of a set is the number of elements of the set. If set S = { 2,4,6 } , then %7C S %7C = 3. v
SANSMIC is solution mining software that was developed and utilized by SNL in its role as geotechnical advisor to the US DOE SPR for planning purposes. Three SANSMIC leach modes - withdrawal, direct, and reverse leach - have been revalidated with multiple test cases for each mode. The withdrawal mode was validated using high quality data from recent leach activity while the direct and reverse modes utilized data from historical cavern completion reports. Withdrawal results compared very well with observed data, including the location and size of shelves due to string breaks with relative leached volume differences ranging from 6 - 10% and relative radius differences from 1.5 - 3%. Profile comparisons for the direct mode were very good with relative leached volume differences ranging from 6 - 12% and relative radius differences from 5 - 7%. First, second, and third reverse configurations were simulated in order to validate SANSMIC over a range of relative hanging string and OBI locations. The first-reverse was simulated reasonably well with relative leached volume differences ranging from 1 - 9% and relative radius differences from 5 - 12%. The second-reverse mode showed the largest discrepancies in leach profile. Leached volume differences ranged from 8 - 12% and relative radius differences from 1 - 10%. In the third-reverse, relative leached volume differences ranged from 10 - 13% and relative radius differences were %7E4 %. Comparisons to historical reports were quite good, indicating that SANSMIC is essentially the same as documented and validated in the early 1980's.
Combined with a Herriott-type multi-pass slow flow reactor, high-resolution differential direct absorption spectroscopy has been used to probe, in situ and quantitatively, hydroxyl (OH), hydroperoxy (HO 2 ) and formaldehyde (CH 2 O) molecules in fuel oxidation reactions in the reactor, with a time resolution of about 1 micro-second. While OH and CH 2 O are probed in the mid-infrared (MIR) region near 2870nm and 3574nm respectively, HO 2 can be probed in both regions: near-infrared (NIR) at 1509nm and MIR at 2870nm. Typical sensitivities are on the order of 10 10 - 10 11 molecule cm -3 for OH at 2870nm, 10 11 molecule cm -3 for HO 2 at 1509nm, and 10 11 molecule cm -3 for CH 2 O at 3574nm. Measurements of multiple important intermediates (OH and HO 2 ) and product (CH 2 O) facilitate to understand and further validate chemical mechanisms of fuel oxidation chemistry.
Antineutrino monitoring of nuclear reactors has been demonstrated many times, however the technique has not as of yet been developed into a useful capability for treaty verification purposes. The most notable drawback is the current requirement that detectors be deployed underground, with at least several meters-water-equivalent of shielding from cosmic radiation. In addition, the deployment of liquid-based detector media presents a challenge in reactor facilities. We are currently developing a detector system that has the potential to operate above ground and circumvent deployment problems associated with a liquid detection media: the system is composed of segments of plastic scintillator surrounded by 6LiF/ZnS:Ag. ZnS:Ag is a radio-luminescent phosphor used to detect the neutron capture products of lithium-6. Because of its long decay time compared to standard plastic scintillators, pulse-shape discrimination can be used to distinguish positron and neutron interactions resulting from the inverse beta decay (IBD) of antineutrinos within the detector volume, reducing both accidental and correlated backgrounds. Segmentation further reduces backgrounds by identifying the positron’s annihilation gammas, which are absent for most correlated and uncorrelated backgrounds. This work explores different configurations in order to maximize the size of the detector segments without reducing the intrinsic neutron detection efficiency. We believe this technology will ultimately be applicable to potential safeguards scenarios such as those recently described.
The intermediate light gas gun at the STAR facility is used for shock wave physics testing with projectile speeds between 25 m/s and 1000 m/s. In order to operate the gun, there are several remote valves, pumps, and sensors that must be operated from the control room. In an effort to improve the engineered safety and efficiency of the gun's operation, a new gas plumbing and controls system must be implemented to simplify operator interaction with high pressure and lower the chance of human error. A new plumbing system has been designed which will allow the bottle farm system, where high pressure gas is stored, to be remotely operated during gun pressurization in addition to a new control system. This new system utilizes LabVIEW, which will communicate directly with a data acquisition and control device located in the gun bay to easily operate the gun pressurization and firing.
This project utilizes Graphics Processing Units (GPUs) to compute radiograph simulations for arbitrary objects. The generation of radiographs, also known as the forward projection imaging model, is computationally intensive and not widely utilized. The goal of this research is to develop a massively parallel algorithm that can compute forward projections for objects with a trillion voxels (3D pixels). To achieve this end, the data are divided into blocks that can each t into GPU memory. The forward projected image is also divided into segments to allow for future parallelization and to avoid needless computations.
A method is presented that ascribes proper statistical variability to simulations that are derived from longer-duration measurements. This method is applicable to simulations of either real-value or integer-value data. An example is presented that demonstrates the applicability of this technique to the synthesis of gamma-ray spectra.