Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computer Codes Computer Programs”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Patient-specific QA and delivery verification of scanned ion beam at NIRS-HIMAC

Purpose: To evaluate a patient-specific QA program and system for constancy checking of a scanning delivery system developed at the National Institute of Radiological Sciences.Methods: For the patient-specific QA, all the planned beams are recalculated on a water phantom with treatment planning software (TPS). The recalculated dose distributions are compared with the measured distributions using a 2D ionization chamber array at several depths, and evaluated using gamma index analysis with criteria of 3% and 3 mm and a pass rate of 90%. For the constancy check, the authors developed the multiwire proportional chamber (MWPC), which can record the delivered 2D fluence images in a slice-by-slice manner. During irradiation for dosimetric QA with the 2D ionization chamber array and an accordion-type water phantom, the 2D fluence images are recorded using the MWPC in the delivery system. These recorded images are then compared to those taken in the treatment session to check the constancy check. This analysis also employs gamma index analysis using the same criteria as in the patient-specific QA. These patient-specific QA and constancy check evaluations were performed using the data of 122 patients.Results: In the patient-specific QA, the measured dose distributions agreed well with those calculated by the TPS, and the QA criteria were satisfied in all measurements. The additional check of the fluence comparison ensured the constancy of the delivered field during each treatment irradiation.Conclusions: The authors established a patient-specific QA program and additional check of delivery constancy in every treatment session. Fluence comparison is a strong tool for constancy checking of the delivery system.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Development of the DIFFRACALC program for analyzing the phase composition of alloys

The DiffraCalc software package is developed for automatic indexing of electron diffraction patterns. The program can operate with the existing base of X-ray diffraction data, containing information about the crystal structures of more than 100 thousands of materials. This information has been extracted from open sources. A compatibility with standard crystallographic files is implemented. In the indexing mode, the program automatically analyzes an experimental diffraction pattern, implementing a search for an appropriate phase from the list compiled by a user proceeding from the crystallographic database. The indexing mode algorithm is constructed using the method of pair reflections. In the simulation mode, the program calculates the electron diffraction pattern for a specified material, which is an additional way to verify the indexing results.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

RELAP-7 User's Guide

The document contains a user's guide on how to run the RELAP-7 code. The RELAP-7 code is the next generation nuclear reactor system safety analysis code being developed at the Idaho National Laboratory. RELAP-7 will become the main reactor systems simulation toolkit for the LWRS (Light Water Reactor Sustainability) program’s RISMC (Risk Informed Safety Margin Characterization) effort and the next generation tool in the RELAP reactor safety/systems analysis application series. RELAP-7 is written with object oriented programming language C++. A number of example problems and their associated input files are presented in this document to guide users to run the RELAP-7 code starting with simple pipe problems to problems with increasing complexity.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

ARC integration into the NEAMS Workbench

One of the objectives of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Integration Product Line (IPL) is to facilitate the deployment of the high-fidelity codes developed within the program. The Workbench initiative was launched in FY-2017 by the IPL to facilitate the transition from conventional tools to high fidelity tools. The Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

2009.3 Revision of the Evaluated Nuclear Data Library (ENDL2009.3)

LLNL's Computational Nuclear Data and Theory Group have created a 2009.3 revised release of the Evaluated Nuclear Data Library (ENDL2009.3). This library is designed to support LLNL's current and future nuclear data needs and will be employed in nuclear reactor, nuclear security and stockpile stewardship simulations with ASC codes. The ENDL2009 database was the most complete nuclear database for Monte Carlo and deterministic transport of neutrons and charged particles. It was assembled with strong support from the ASC PEM and Attribution programs, leveraged with support from Campaign 4 and the DOE/Office of Science's US Nuclear Data Program. This document lists the revisions and fixes made in a new release called ENDL2009.3, by com- paring with the existing data in the previous release ENDL2009.2. These changes are made in conjunction with the revisions for ENDL2011.3, so that both the .3 releases are as free as possible of known defects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

NEAMS Workbench Status and Capabilities

The mission of the US Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished by using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to highfidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering-scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process can enable multifidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences compared with native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed herein.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Transient Efficiency Flexibility and Reliability Optimization of Coal-Fired Power Plants: Model-Predictive Control Library Development (Report)

This document pertains to the reporting requirements of DOE contract FE-0031767. The document covers the development of a model predictive control (MPC) library for implementing MPC for a general dynamic system. The library is implemented in a standardized manner in Matlab/Simulink, where core functions on model prediction, linearization and formulation and solution of a quadratic programming (QP) optimization problem is done in the core library - independent of the specific application. The user can provide the application-specific dynamic model in continuous and discrete time, to rapidly implement and test the MPC performance in a desktop simulation. The MPC optimization objective and constraints are also easily configured via an Excel file to allow iterative refinement as needed. Finally, the MPC library enables a rapid deployment to a target environment through auto C-code generation and containerization. The MPC library works seamlessly with the model based estimation (MBE) library to obtain the overall output feedback control solution. In this program, the reduced order model (ROM) of a coal-fired power plant (CFPP) is used to implement and test the MPC solution.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

AMPX and ENDF/B-VIII.1 Thermal Scattering Library [Slides]

This presentation provides an enlightening discussion of AMPX and ENDF/B-VIII.1. The talk begins with general ENDF issues encountered. Then narrows down into Issues particular to thermal scattering library. Such as the need for clear communication of the intended use for the file and the question of “Which graphite should I use in model?”. The presentation then looks at TSL files with many Bragg peaks are driving a change in SCALE CE library format and transport codes. This lecture concludes with a look at usage of thermal kernels with sharp peaks with the issues encountered using ENDF/B-VIII.0 data and resolved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

DARMA v. Beta 0.5

DARMA is a portability layer for asynchronous many-task (AMT) runtime systems. AMT runtime systems show promise to mitigate challenges imposed by next generation high performance computing architectures. However, current runtime system technologies are not production-ready. DARMA is a portability layer that seeks to insulate application developers from idiosyncrasies of individual runtime systems, thereby facilitating application-developer use of these technologies. DARMA comprises a frontend application programming interface (API) for application developers, a backend API for runtime system developers, and a translation that translates frontend API calls into backend API calls. Application developers use C++ abstractions to annotate both data and tasks in their code. The DARMA translation layer uses C++ template metaprogramming to capture data-task dependencies, and provides this information to a potential backend runtime system via a series of backend API calls.

Hollman, David↗
DOE OSTI2017

Security-informed safety: integrating security within the safety demonstration of a smart device - 157

Safety and security engineering have, over the years, developed their own regulations, standards, cultures, and practices. However, there's a growing realisation that security is closely connected to safety. Safety must be security-informed: if a safety-critical system isn't secure, it isn't safe. A safety demonstration is incomplete and unconvincing unless it considers security. In our work for government and industry, we have used the Claims, Arguments, Evidence (CAE) framework to analyse the impact of security on a safety justification or safety case and identified the significant changes needed to address security explicitly. This will impact the design and implementation process as well as the assurance and V and V approach. In this paper we discuss the impact of integrating security when developing a safety demonstration of a smart device. A smart device is an instrument, device or component that contains a microprocessor (and therefore contains both hardware and software) and is programmed to provide specialised capabilities, often measuring or controlling a process variable. Examples of smart devices include radiation monitors, relays, turbine governors, uninterruptible power supplies and heating ventilation, and air conditioning controllers. (authors)

42 ENGINEERING↗
DOE OSTI2023

Generating MCNP Input Files for Unstructured Mesh Geometries

The Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle (MCNP)1 transport code version 6.3 (also known as MCNP6.3) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is time-consuming and error-prone as the complexities of geometries increase. A UM geometry model is a collection of finite elements representing a solid geometry. The first step of the MCNP UM calculation is using other software packages to create a finite element mesh representation of a solid 3D geometry because the MCNP code cannot be used to generate a UM model. Computer-aided design (CAD) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. Some mesh generation software packages may also be used to create solid geometries and thus CAD files are not needed. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software suite. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with a 6.3 version, the MCNP code can process HDF5 mesh input files. We only focus on the UM models formatted as Abaqus input files in this report since currently no external software can be used to generate HDF5 mesh input files for MCNP UM calculations. The MCNP code version 6.3 can be used to convert the Abaqus mesh input files into the HDF5 mesh input files, but this option is typically used by the MCNP code development team to test the HDF5 mesh input file feature. Several software packages (such as Abaqus, Attila4MC, or Cubit) can be used to create the Abaqus input files for MCNP UM calculations. An MCNP UM calculation using an Abaqus model requires two input file types: MCNP and Abaqus input files. The Abaqus input files needed for MCNP UM calcu lations must have the correct Abaqus syntax and meet the additional requirements by the MCNP code. The MCNP code can process only Abaqus input files that make use of part and assembly definitions, where elements in each part must be grouped into one or more element sets (i.e., elset) using *Elset keyword lines with specified naming formats. The MCNP and Abaqus input files required for MCNP UM simulations must be related; pseudo-cells in an MCNP input file must be constructed from mesh model data from an Abaqus input file. For large complex UM models, it is tedious to manually create MCNP UM input files. The um pre op (unstructured mesh pre operations) program with the -m option can be used to create a skeleton MCNP input file from an Abaqus input file [6]. Since the um pre op program was written in Fortran and was not written for optimized performance, this program is a deprecated feature in the MCNP code version 6.3 and may be removed in the next release of the code. To improve calculation flow of multiphysics calculations, a Python3 code called write mcnp um input has been developed to generate an MCNP input file instead of using the um_pre_op -m option. This Python code was initially released to the public in 2020. We have updated this Python code for MCNP6.3 and it was used to generate the MCNP input files used to verify the MCNP6.3 code. The write_mcnp_um_input code is included with the MCNP6.3 code package which will be released to the public through the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. This report is a revision of LA-UR-20-27139 report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

SPARC: Demonstrate burst-buffer-based checkpoint/restart on ATS-1.

Recent high-performance computing (HPC) platforms such as the Trinity Advanced Technology System (ATS-1) feature burst buffer resources that can have a dramatic impact on an application’s I/O performance. While these non-volatile memory (NVM) resources provide a new tier in the storage hierarchy, developers must find the right way to incorporate the technology into their applications in order to reap the benefits. Similar to other laboratories, Sandia is actively investigating ways in which these resources can be incorporated into our existing libraries and workflows without burdening our application developers with excessive, platform-specific details. This FY18Q1 milestone summaries our progress in adapting the Sandia Parallel Aerodynamics and Reentry Code (SPARC) in Sandia’s ATDM program to leverage Trinity’s burst buffers for checkpoint/restart operations. We investigated four different approaches with varying tradeoffs in this work: (1) simply updating job script to use stage-in/stage out burst buffer directives, (2) modifying SPARC to use LANL’s hierarchical I/O (HIO) library to store/retrieve checkpoints, (3) updating Sandia’s IOSS library to incorporate the burst buffer in all meshing I/O operations, and (4) modifying SPARC to use our Kelpie distributed memory library to store/retrieve checkpoints. Team members were successful in generating initial implementation for all four approaches, but were unable to obtain performance numbers in time for this report (reasons: initial problem sizes were not large enough to stress I/O, and SPARC refactor will require changes to our code). When we presented our work to the SPARC team, they expressed the most interest in the second and third approaches. The HIO work was favored because it is lightweight, unobtrusive, and should be portable to ATS-2. The IOSS work is seen as a long-term solution, and is favored because all I/O work (including checkpoints) can be deferred to a single library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Implementation of Relativistic Coupled Cluster Theory for Massively Parallel GPU-Accelerated Computing Architectures

In this paper, we report reimplementation of the core algorithms of relativistic coupled cluster theory aimed at modern heterogeneous high-performance computational infrastructures. The code is designed for parallel execution on many compute nodes with optional GPU coprocessing, accomplished via the new ExaTENSOR back end. The resulting ExaCorr module is primarily intended for calculations of molecules with one or more heavy elements, as relativistic effects on the electronic structure are included from the outset. In the current work, we thereby focus on exact two-component methods and demonstrate the accuracy and performance of the software. The module can be used as a stand-alone program requiring a set of molecular orbital coefficients as the starting point, but it is also interfaced to the DIRAC program that can be used to generate these. We therefore also briefly discuss an improvement of the parallel computing aspects of the relativistic self-consistent field algorithm of the DIRAC program.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Speeding up software with VecCore

Portable and efficient vectorization is a significant challenge in large software projects such as GeantV, ROOT, and experiments’ frameworks. Nevertheless, fully exploiting SIMD parallelism will be a required step in order to bridge the widening gap between the needs and availability of computing resouces for data analysis and processing in particle physics. Although there are SIMD libraries that wrap compiler intrinsics into a convenient interface, they do not always support all available architectures, or they only perform well in some of them. The VecCore library was created to address some of these performance and portability issues by providing a unified abstraction layer on top of existing libraries, such as Vc or UME::SIMD. In this article, we discuss VecCore’s programming model for SIMD code and some use cases in HEP software packages such as VecGeom and GeantV.

Amadio, G.↗
DOE OSTI2015

Center for Center for Technology for Advanced Scientific Component Software (TASCS)

A resounding success of the Scientific Discovery through Advanced Computing (SciDAC) program is that high-performance computational science is now universally recognized as a critical aspect of scientific discovery [71], complementing both theoretical and experimental research. As scientific communities prepare to exploit unprecedented computing capabilities of emerging leadership-class machines for multi-model simulations at the extreme scale [72], it is more important than ever to address the technical and social challenges of geographically distributed teams that combine expertise in domain science, applied mathematics, and computer science to build robust and flexible codes that can incorporate changes over time. The Center for Technology for Advanced Scientific Component Software (TASCS)1 tackles these these issues by exploiting component-based software development to facilitate collaborative high-performance scientific computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

NEAMS Workbench 1.0 Beta

The mission of the US Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to high-fidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process enables multi-fidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences over the native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed here.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Warthog: Coupling Nek5000 Thermal Hydraulics to BISON Fuel Performance through the Giraffe Interface

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program under the US Department of Energy (DOE) consists of three product lines. The Fuels Product Line (FPL), hosted at Idaho National Laboratory (INL), has produced a multiphysics framework with the name Multiphysics Object Oriented Simulation Environment (MOOSE) (Gaston et al. 2015). The FPL has also produced several high-fidelity physics codes including the fuel performance code Bison (Hales et al. 2016). The Reactors Product Line (RPL), hosted at Argonne National Laboratory (Argonne), has developed the high-order spectral-element Computational Fluid Dynamics (CFD) code Nek5000 (Fischer et al. 2015). The Integration Product Line (IPL), hosted at Oak Ridge National Laboratory (ORNL), is tasked with ensuring tools from the other product lines can be coupled together so that users may perform high-fidelity multiphysics simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗