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The Scientific sofTware Research for User experience, Design, Engagement, and Learning project (STRUDEL) will create, using user research methodologies, a planning framework and design system for scientific software that will guide and support the development of highly functional and usable user interfaces.
This user guide describes the macro system model (MSM). The MSM has been designed to allow users to analyze the financial, environmental, transitional, geographical, and R&D issues associated with the transition to a hydrogen economy. Basic end users can use the MSM to answer cross-cutting questions that were previously difficult to answer in a consistent and timely manner due to variousassumptions and methodologies among different models.
This document is a user s guide for the IDL software REFoffSpec version 1.5.4 whose purpose is to aggregate for analysis NeXus data files from the magnetism and liquids reflectometer experiments at the Oak Ridge National Laboratory Spallation Neutron Source. The software is used to scale and align multiple data files that constitute a continuous set for an experimental run. The User s Guide for REFoffSepc explains step by step the process using a specific example run. Output screens are provided to orient the user at each step. The guide documents in detail changes made to the original REFoffSpec code during the period November 2009 and January 2011. At the time of the completion of this version of the code it was accessible from the sns_tools interface as a beta version.
The Geothermal Jobs and Economic Development Impact (JEDI) model, developed through the National Renewable Energy Laboratory (NREL), is an Excel-based user-friendly tools that estimates the economic impacts of constructing and operating hydrothermal and Enhanced Geothermal System (EGS) power generation projects at the local level for a range of conventional and renewable energy technologies. The JEDI Model Geothermal User Reference Guide was developed to assist users in using and understanding the model. This guide provides information on the model's underlying methodology, as well as the parameters and references used to develop the cost data utilized in the model. This guide also provides basic instruction on model add-in features, operation of the model, and a discussion of how the results should be interpreted.
Large-scale simulation datasets can be modeled as high-dimensional scalar functions defined over a discrete sample of the domain. The goals of our proposed research are two-fold. First, we would like to provide structural analysis of a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. TopoXG is a software package that is designed to address these goals. The unique contribution of TopoXG lies in exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations. We provide a user’s guide to TopoXG, by highlighting its analysis and visualization capabilities, and giving several use cases involving datasets from nuclear reactor safety simulations.
User's guide for VSP 7.0 This user's guide describes Visual Sample Plan (VSP) Version 7.0 and provides instructions for using the software. VSP selects the appropriate number and location of environmental samples to ensure that the results of statistical tests performed to provide input to risk decisions have the required confidence and performance. VSP Version 7.0 provides sample-size equations or algorithms needed by specific statistical tests appropriate for specific environmental sampling objectives. It also provides data quality assessment and statistical analysis functions to support evaluation of the data and determine whether the data support decisions regarding sites suspected of contamination. The easy-to-use program is highly visual and graphic. VSP runs on personal computers with Microsoft Windows operating systems (XP, Vista, Windows 7, and Windows 8). Designed primarily for project managers and users without expertise in statistics, VSP is applicable to two- and three-dimensional populations to be sampled (e.g., rooms and buildings, surface soil, a defined layer of subsurface soil, water bodies, and other similar applications) for studies of environmental quality. VSP is also applicable for designing sampling plans for assessing chem/rad/bio threat and hazard identification within rooms and buildings, and for designing geophysical surveys for unexploded ordnance (UXO) identification.
This document provides a user’s guide for the most recent version of the hysteretic code, which runs within iTOUGH2 (Finsterle, 1999a,b,c) or TOUGH2 V2.1 (Pruess et al., 2012). The usage of the hysteretic module is the same in both codes, which for brevity here are both referred to simply as TOUGH2. The current code differs only slightly from what was presented in Doughty (2007), hence that document provides the basic information on the processes being modeled and how they are conceptualized. This document focuses on a description of the user-specified parameters required to run hysteretic TOUGH2. In the few instances where the conceptualization differs from that of Doughty (2007), the features described here are the current ones. Sample problems presented in this user’s guide use the equation-of-state module ECO2N (Pruess, 2005). The components present in ECO2N are H{sub 2}O, NaCl, and CO{sub 2}. Two fluid phases and one solid phase are considered: an aqueous phase, which primarily consists of liquid H{sub 2}O and may contain dissolved NaCl and CO{sub 2}; a supercritical phase which primarily consists of CO{sub 2}, but also includes a small amount of gaseous H{sub 2}O; and a solid phase consisting of precipitated NaCl. Details of the ECO2N formulation may be found in Pruess (2005). The aqueous phase is the wetting phase and is denoted ‘liquid’, whereas the supercritical phase is the non-wetting phase and is denoted ‘gas’. The hysteretic formalism may be applied to other TOUGH2 equation-of-state modules that consider two fluid phases, as long as the liquid phase is the wetting phase and the gas phase is the non-wetting phase.
The fifth Fundamental Science with Pulsed Power: Research Opportunities and User Meeting was held in Albuquerque, NM, July 20-23, 2014. The purpose of the workshop was to bring together leading scientists in four research areas with active fundamental science research at Sandia’s Z facility: Magnetized Liner Inertial Fusion (MagLIF), Planetary Science, Astrophysics, and Material Science. The workshop was focused on discussing opportunities for high-impact research using Sandia’s Z machine, a future 100 GPa class facility, and possible topics for growing the academic (off-Z-campus) science relevant to the Z Fundamental Science Program (ZFSP) and related projects in astrophysics, planetary science, MagLIF- relevant magnetized HED science, and materials science. The user meeting was for Z collaborative users to: a) hear about the Z accelerator facility status and plans, b) present the status of their research, and c) be provided with a venue to meet and work as groups. Following presentations by Mark Herrmann and Joel Lash on the fundamental science program on Z and the status of the Z facility where plenary sessions for the four research areas. The third day of the workshop was devoted to breakout sessions in the four research areas. The plenary- and breakout sessions were for the four areas organized by Dan Sinars (MagLIF), Dylan Spaulding (Planetary Science), Don Winget and Jim Bailey (Astrophysics), and Thomas Mattsson (Material Science). Concluding the workshop were an outbrief session where the leads presented a summary of the discussions in each working group to the full workshop. A summary of discussions and conclusions from each of the research areas follows and the outbrief slides are included as appendices.
This guide -- the JEDI Fast Pyrolysis Biorefinery Model User Reference Guide -- was developed to assist users in operating and understanding the JEDI Fast Pyrolysis Biorefinery Model. The guide provides information on the model's underlying methodology, as well as the parameters and data sources used to develop the cost data utilized in the model. This guide also provides basic instruction on model add-in features and a discussion of how the results should be interpreted. Based on project-specific inputs from the user, the JEDI Fast Pyrolysis Biorefinery Model estimates local (e.g., county- or state-level) job creation, earnings, and output from total economic activity for a given fast pyrolysis biorefinery. These estimates include the direct, indirect and induced economic impacts to the local economy associated with the construction and operation phases of biorefinery projects.Local revenue and supply chain impacts as well as induced impacts are estimated using economic multipliers derived from the IMPLAN software program. By determining the local economic impacts and job creation for a proposed biorefinery, the JEDI Fast Pyrolysis Biorefinery Model can be used to field questions about the added value biorefineries might bring to a local community.
Infrared (IR) imagery can provide valuable data to building energy auditors and the developers of Building Energy Models (BEM). IR by itself is not a panacea to the challenges facing energy auditors and modelers, but when combined with other techniques and Open Studio software, IR can play an important role in the minimization of the burden of creating building energy models and the diagnosing of building energy problems. While IR can be used to detect and quantify many aspects of a building’s thermal and energy performance, in Phase I we focused on a single capability: determining the R value of exterior walls. Two key attributes of using IR methods for R value determination is that IR methods measure the actual, installed performance in-situ – as opposed to relying on manufacturer or handbook values – and they can integrate performance over a wide area and thus can accurately account for thermal bridging and spatial anomalies such as “spotty” insulation installation. There is a trade between user burden and accuracy when using IR to determine a wall’s R value. The least burdensome approach is computing R values from a single IR snapshot. The most burdensome method we considered was to combine IR images with thermocouple and heat flux sensor (HFS) measurements made over the course of several days. The more effort users expend in making the measurements, the more accurate and robust the method becomes. Through a series of field tests at the ThermoAnalytics Inc. (TAI) HQ at Calumet, MI and a simulation of a 1980s era office building in Flagstaff, AZ we investigated methods to compute the R value of exterior walls using data from IR cameras, thermocouples and heat flux sensors. To compute R values the heat flux through the wall, along with temperatures on either side of the wall, must be known, modeled, or estimated – and the temperature difference through the wall should be at least 10 C. If only outdoor IR images are used in the method, the inside ambient air temperature must be estimated, and a simulation model created to predict heat fluxes and the indoor wall temperature. The accuracy of the resulting R value depends on the accuracy of the simulation and the assumptions it employs. Applying thermocouple sensors, either as iButtons or as part of a wireless sensor network, to the walls allows for the acquisition of temperatures over time. The longer the duration of the measurements, the more accurate and robust the method will be. Even with this level of measurement effort, modeling is still required to calculate the heat flux through the wall. The addition of a heat flux sensor alleviates the need for any modeling, and that results in improved accuracy. Driving the accuracy of the methods are several factors that can be quantified. How well are the wall surface temperatures known? Modeling the heat flux through walls requires knowing the details of the surrounding radiant environment and the heat transfer due to convection. How well are these boundary conditions known? Even when the heat flux is measured, error can arise when some of the measured heat is not conducted through the wall but rather goes into the thermal mass of the wall. Thermal mass effects occur when conditions and the heat flux values are fluctuating. A technique that is not robust will suffer in accuracy when these fluctuations occur. The R value determination method must be incorporated into the Open Studio process. In our proposed process, IR images and other measured sensor data are input into a module that would send R value estimates to a building library developed by team member Skidmore, Owings, & Merrill (SOM). This library of parameterized building models would use these R values along with information about the building’s age, type, and location to query the Open Studio BCL to identify the most likely constructions for walls, roof, and windows. Being based on the IMF feature of Energyplus, the SOM building library can quickly produce Energyplus models of buildings based on a limited number of user inputs.
This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself; For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.
This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself; For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.
This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. ;For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.
In 2014, the Deputy Assistant Secretary for Science and Technology Innovation initiated the Nuclear Energy (NE)–Infrastructure Management Project by tasking the Nuclear Science User Facilities, formerly the Advanced Test Reactor National Scientific User Facility, to create a searchable and interactive database of all pertinent NE-supported and -related infrastructure. This database, known as the Nuclear Energy Infrastructure Database (NEID), is used for analyses to establish needs, redundancies, efficiencies, distributions, etc., to best understand the utility of NE’s infrastructure and inform the content of infrastructure calls. The Nuclear Science User Facilities developed the database by utilizing data and policy direction from a variety of reports from the U.S. Department of Energy, the National Research Council, the International Atomic Energy Agency, and various other federal and civilian resources. The NEID currently contains data on 802 research and development instruments housed in 377 facilities at 84 institutions in the United States and abroad. The effort to maintain and expand the database is ongoing. Detailed information on many facilities must be gathered from associated institutions and added to complete the database. The data must be validated and kept current to capture facility and instrumentation status as well as to cover new acquisitions and retirements. This document provides a short tutorial on the navigation of the NEID web portal at NSUF-Infrastructure.INL.gov.
The Manufacturing Cost Levelization Model is a cost-performance techno-economic model that estimates total large-scale manufacturing costs for necessary to produce a given product. It is designed to provide production cost estimates for technology researchers to help guide technology research and development towards an eventual cost-effective product. The model presented in this user’s guide is generic and can be tailored to the manufacturing of any product, including the generation of electricity (as a product). This flexibility, however, requires the user to develop the processes and process efficiencies that represents a full-scale manufacturing facility. The generic model is comprised of several modules that estimate variable costs (material, labor, and operating), fixed costs (capital & maintenance), financing structures (debt and equity financing), and tax implications (taxable income after equipment and building depreciation, debt interest payments, and expenses) of a notional manufacturing plant. A cash-flow method is used to estimate a selling price necessary for the manufacturing plant to recover its total cost of production. A levelized unit sales price ($ per unit of product) is determined by dividing the net-present value of the manufacturing plant’s expenses ($) by the net present value of its product output. A user defined production schedule drives the cash-flow method that determines the levelized unit price. In addition, an analyst can increase the levelized unit price to include a gross profit margin to estimate a product sales price. This model allows an analyst to understand the effect that any input variables could have on the cost of manufacturing a product. In addition, the tool is able to perform sensitivity analysis, which can be used to identify the key variables and assumptions that have the greatest influence on the levelized costs. This component is intended to help technology researchers focus their research attention on tasks that offer the greatest opportunities for cost reduction early in the research and development stages of technology invention.
To understand the importance of capturing penetrating forms of iodine as well as the other volatile radionuclides, a calculation tool was developed in the form of an Excel ® spreadsheet to estimate the overall plant decontamination factor (DF). The tool requires the user to estimate splits of the volatile radionuclides within the major portions of the reprocessing plant, speciation of iodine and individual DFs for each off-gas stream within the Used Nuclear Fuel reprocessing plant. The Impact to the overall plant DF for each volatile radionuclide is then calculated by the tool based on the specific user choices. The Excel ® spreadsheet tracks both elemental and penetrating forms of iodine separately and allows changes in the speciation of iodine at each processing step. It also tracks 3 H, 14 C and 85 Kr. This document provides a basic user's guide to the manipulation of this tool.
Slycat™ is a web-based system for performing data analysis and visualization of potentially large quantities of remote, high-dimensional data. Slycat™ specializes in working with ensemble data. An ensemble is a group of related data sets, which typically consists of a set of simulation runs exploring the same problem space. An ensemble can be thought of as a set of samples within a multi-variate domain, where each sample is a vector whose value defines a point in high-dimensional space. To understand and describe the underlying problem being modeled in the simulations, ensemble analysis looks for shared behaviors and common features across the group of runs. Additionally, ensemble analysis tries to quantify differences found in any members that deviate from the rest of the group. The Slycat™ system integrates data management, scalable analysis, and visualization. Results are viewed remotely on a user’s desktop via commodity web clients using a multi-tiered hierarchy of computation and data storage, as shown in Figure 1. Our goal is to operate on data as close to the source as possible, thereby reducing time and storage costs associated with data movement. Consequently, we are working to develop parallel analysis capabilities that operate on High Performance Computing (HPC) platforms, to explore approaches for reducing data size, and to implement strategies for staging computation across the Slycat™ hierarchy. Within Slycat™, data and visual analysis are organized around projects, which are shared by a project team. Project members are explicitly added, each with a designated set of permissions. Although users sign-in to access Slycat™, individual accounts are not maintained. Instead, authentication is used to determine project access. Within projects, Slycat™ models capture analysis results and enable data exploration through various visual representations. Although for scientists each simulation run is a model of real-world phenomena given certain conditions, we use the term model to refer to our modeling of the ensemble data, not the physics. Different model types often provide complementary perspectives on data features when analyzing the same data set. Each model visualizes data at several levels of abstraction, allowing the user to range from viewing the ensemble holistically to accessing numeric parameter values for a single run. Bookmarks provide a mechanism for sharing results, enabling interesting model states to be labeled and saved.
In order to develop a healthy community around the Kokkos programming model it is necessary to put a support infrastructure in place. That infrastructure must serve the users needs in finding information about Kokkos, get connected to experts to answer questions about how to use Kokkos as well as provide mechanisms to report bugs and request new capabilities. One key aspect of such an infrastructure is that it should be scalable, i.e. that the approach works in the early stages with just a few users as well as in later stages, where Kokkos may have thousands of active users.