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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

New hardware and software platform for experiments on a HUBER-5042 X-ray diffractometer with a DISPLEX DE-202 helium cryostat in the temperature range of 20–300 K

Huber-5042 diffractometer with a closed-cycle Displex DE-202 helium cryostat is a unique scientific instrument for carrying out X-ray diffraction experiments when studying the single crystal structure in the temperature range of 20–300 K. To make the service life longer and develop new experimental techniques, the diffractometer control is transferred to a new hardware and software platform. To this end, a modern computer; a new detector reader unit; and new control interfaces for stepper motors, temperature controller, and cryostat vacuum pumping system are used. The system for cooling the X-ray tube, the high-voltage generator, and the helium compressor and pump for maintaining the desired vacuum in the cryostat are replaced. The system for controlling the primary beam shutter is upgraded. A biological shielding is installed. The new program tools, which use the Linux Ubuntu operating system and SPEC constructor, include a set of drivers for control units through the aforementioned interfaces. A program for searching reflections from a sample using fast continuous scanning and a priori information about crystal is written. Thus, the software package for carrying out the complete cycle of precise diffraction experiment (from determining the crystal unit cell to calculating the integral reflection intensities) is upgraded. High quality of the experimental data obtained on this equipment is confirmed in a number of studies in the temperature range from 20 to 300 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Cumulative $\chi^2$ Metric for VALID for ENDF/B-VII.1 and ENDF/B-VIII.0 in SCALE 6.3b9

Nuclear data testing is generally performed in a manner similar to validation: a series of critical benchmark experiments is modeled, and the differences between calculated and expected results are examined to understand the performance of a code and cross section set. This process is repeated for multiple sets of experiments with different transport codes and sometimes with different datasets. Occasionally, data processing differences are also important in understanding the differences in calculated results. Relevant experts then meet periodically to compare results and set priorities for improvement in the next round of data updates. This process has been largely effective, despite the confounding factors of different benchmark sets, application areas, sponsors, nationalities, and the resulting different priorities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

User Manual for MC 2 -3 Gamma Library Generation

This report provides the documentation for the revised procedure to generate the MC 2 -3 gamma library with the latest versions of PreGAMMA and GenGAMMA which have recently been updated to fix the program errors and inconsistent data processing for some isotopes identified in a recent verification and validation study of the MC 2 -3 gamma library. A few associated errors associated with gamma processing were also identified and fixed in the MC 2 -3 code. Addressing the changes in MC 2 -3, the procedure to generate the PMATRX and GAMISO datasets is discussed as well. The input data of PreGAMMA and GenGAMMA are discussed in detail using sample input data. In addition, the structure and formats of the MC 2 -3 gamma library are provided in detail.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Using Performance Tools to Support Experiments in HPC Resilience

The high performance computing (HPC) community is working to address fault tolerance and resilience concerns for current and future large scale computing platforms. This is driving enhancements in the programming environ- ments, specifically research on enhancing message passing libraries to support fault tolerant computing capabilities. The community has also recognized that tools for resilience experimentation are greatly lacking. However, we argue that there are several parallels between performance tools and resilience tools . As such, we believe the rich set of HPC performance-focused tools can be extended (repurposed) to benefit the resilience community. In this paper, we describe the initial motivation to leverage standard HPC per- formance analysis techniques to aid in developing diagnostic tools to assist fault tolerance experiments for HPC applications. These diagnosis procedures help to provide context for the system when the errors (failures) occurred. We describe our initial work in leveraging an MPI performance trace tool to assist in provid- ing global context during fault injection experiments. Such tools will assist the HPC resilience community as they extend existing and new application codes to support fault tolerances.

Boehm, Swen↗
DOE OSTI2023

Requirements Description of VARPOW

This report presents the modeling and simulation capabilities of Argonne National Laboratory’s VARPOW code that is used in present reactor analysis activities. These capabilities will then be used to establish the set of VARPOW verification tasks necessary to verify VARPOW for usage on commercial projects. A similar approach was taken for the PERSENT, REBUS and DIF3D software packages. The VARPOW program is a post-processing utility program for DIF3D, specifically DIF3D-VARIANT. As covered in [1], the VARPOW program was built to provide input for follow-on steady state thermal-hydraulic analysis. Its primary purpose is to act as the interface between DASSH and DIF3D-VARIANT. The outputs from VARPOW are contained in three output files: Output.VARPOW, VariantMonoExponents.out, and MaterialPower.out.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Specification of the near-Earth space environment with SHIELDS

Here, predicting variations in the near-Earth space environment that can lead to spacecraft damage and failure is one example of “space weather” and a big space physics challenge. A project recently funded through the Los Alamos National Laboratory (LANL) Directed Research and Development (LDRD) program aims at developing a new capability to understand, model, and predict Space Hazards Induced near Earth by Large Dynamic Storms, the SHIELDS framework. The project goals are to understand the dynamics of the surface charging environment (SCE), the hot (keV) electrons representing the source and seed populations for the radiation belts, on both macro- and micro-scale. Important physics questions related to particle injection and acceleration associated with magnetospheric storms and substorms, as well as plasma waves, are investigated. These challenging problems are addressed using a team of world-class experts in the fields of space science and computational plasma physics, and state-of-the-art models and computational facilities. A full two-way coupling of physics-based models across multiple scales, including a global MHD (BATS-R-US) embedding a particle-in-cell (iPIC3D) and an inner magnetosphere (RAM-SCB) codes, is achieved. New data assimilation techniques employing in situ satellite data are developed; these provide an order of magnitude improvement in the accuracy in the simulation of the SCE. SHIELDS also includes a post-processing tool designed to calculate the surface charging for specific spacecraft geometry using the Curvilinear Particle-In-Cell (CPIC) code that can be used for reanalysis of satellite failures or for satellite design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-C-BRC-07: Parametrized GPU Accelerated Electron Monte Carlo Second Check

Purpose: I am presenting a parameterized 3D GPU accelerated electron Monte Carlo second check program. Method: I wrote the 3D grid dose calculation algorithm in CUDA and utilized an NVIDIA GeForce GTX 780 Ti to run all of the calculations. The electron path beyond the distal end of the cone is governed by four parameters: the amplitude of scattering (AMP), the mean and width of a Gaussian energy distribution (E and α), and the percentage of photons. In my code, I adjusted all parameters until the calculated PDD and profile fit the measured 10×10 open beam data within 1%/1mm. I then wrote a user interface for reading the DICOM treatment plan and images in Python. In order to verify the algorithm, I calculated 3D dose distributions on a variety of phantoms and geometries, and compared them with the Eclipse eMC calculations. I also calculated several patient specific dose distributions, including a nose and an ear. Finally, I compared my algorithm’s computation times to Eclipse’s. Results: The calculated MU for all of the investigated geometries agree with the TPS within the TG-114 action level of 5%. The MU for the nose was < 0.5 % different while the MU for the ear at 105 SSD was ∼2 %. Calculation times for a 12MeV 10×10 open beam ranged from 1 second for a 2.5 mm grid resolution with ∼15 million particles to 33 seconds on a 1 mm grid with ∼460 million particles. Eclipse calculation runtimes distributed over 10 FAS workers were 9 seconds to 15 minutes respectively. Conclusion: The GPU accelerated second check allows quick MU verification while accounting for patient specific geometry and heterogeneity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Programmer-guided reliability for extreme-scale applications

We present “programmer-guided reliability” (PGR) as a systematic conceptual approach to address the expected rise in soft errors in coming extreme-scale systems at the application level. The approach involves instrumentation of the application with code to detect data corruption errors. The location and nature of these error detectors are at the discretion of the programmer, who uses their knowledge and experience with the problem domain, the application, the solution algorithms, etc., to determine the most vulnerable areas of the code and the most appropriate ways to detect data corruption. To illustrate the approach, we provide examples of error detectors from four different benchmark-scale applications. We also describe a simple control framework that allows for runtime configuration of the error detectors without recompilation of the application, as well as dynamic reconfiguration during the execution of the application. Lastly, we discuss a number of future directions building on the basic PGR approach, including the incorporation of some general error detectors into the programming environment in order to make them more easily usable by the programmer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

October 2019 SAPHIRE 8 Software Quality Assurance Oversight Status Report

The software quality assurance oversight consists of updating and maintaining revision control of the System Analysis Program for Hands-on Integrated Reliability Evaluations (SAPHIRE) Version 8 quality assurance program documentation and of monitoring revision control of the SAPHIRE Version 8 source code. This report summarizes the oversight efforts through description of the revision control system (RCS) setup, operation and contents. Documents maintained under revision control include the Acceptance Test Plan (ATP), Configuration Management Plan, Quality Assurance Plan, Software Project Plan, Requirements Traceability Matrix (RTM), System Test Plan, Significance Determination Process (SDP) Interface Training Manual, and the SAPHIRE 8 “New Features and Capabilities Overview.” These documents are referred to and can be transmitted upon request of the Nuclear Regulatory Commission (NRC) Contracting Officer’s Representative (COR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

ACOPF↗
DOE OSTI2016

Lecture Notes on Criticality Safety Validation Using MCNP & Whisper

Training classes for nuclear criticality safety, MCNP documentation. The need for, and problems surrounding, validation of computer codes and data area considered first. Then some background for MCNP & Whisper is given--best practices for Monte Carlo criticality calculations, neutron spectra, S(α,β) thermal neutron scattering data, nuclear data sensitivities, covariance data, and correlation coefficients. Whisper is computational software designed to assist the nuclear criticality safety analyst with validation studies with the Monte Carlo radiation transport package MCNP. Whisper's methodology (benchmark selection – C k 's, weights; extreme value theory – bias, bias uncertainty; MOS for nuclear data uncertainty – GLLS) and usage are discussed.

42 ENGINEERING↗
DOE OSTI2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs

Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Rodriguez, Alex [Argonne National Laboratory (ANL)↗
DOE OSTI2021

The case for free and open source software in research and scholarship

Free and open source software (FOSS) is any computer program released under a licence that grants users rights to run the program for any purpose, to study it, to modify it, and to redistribute it in original or modified form. Here, our aim is to explore the intersection between FOSS and computational reproducibility. We begin by situating FOSS in relation to other ‘open’ initiatives, and specifically open science, open research, and open scholarship. In this context, we argue that anyone who actively contributes to the research process today is a computational researcher, in that they use computers to manage and store information. We then provide a primer to FOSS suitable for anyone concerned with research quality and sustainability—including researchers in any field, as well as support staff, administrators, publishers, funders, and so on. Next, we illustrate how the notions introduced in the primer apply to resources for scientific computing, with reference to the GNU Scientific Library as a case study. We conclude by discussing why the common interpretation of ‘open source’ as ‘open code’ is misplaced, and we use this example to articulate the role of FOSS in research and scholarship today.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Recent Progress on the Stochastic Objective Decision Aide (SODA) Application

Idaho State University, in collaboration with Idaho National Laboratory, is engaged in development of a simple to use software application that utilizes stochastic techniques for the calculation of radiation doses associated with hypothetical radioactive material releases. This work is being performed through a grant from the U.S. Department of Energy (DOE) Nuclear Safety Research and Development (NSR and D) Program. Initial progress on the application was reported previously. Recent additions to the application are reported here. The Stochastic Objective Decision Aide (SODA) provides an easy to use software application to help decision makers support their judgment about the need or lack of need for safety structures, systems, and components. SODA is not intended to replace traditional design basis accident dose calculation methodology, nor is it intended to replace existing software used to perform formal dose consequence calculations. Rather, SODA is intended to supplement information obtained from existing codes to give a decision maker more information about an accident scenario. SODA also provides a simple method to perform parametric or sensitivity studies on individual input parameters. Non-reactor nuclear facilities operated under the approval authority of the US DOE use unmitigated hazard evaluations to determine if potential radiological doses associated with design basis events challenge dose evaluation guidelines. Unmitigated events that sufficiently challenge dose evaluation guidelines merit the selection of safety structures, systems, or components (SSCs) to prevent or mitigate the hazard. Dose consequence calculations traditionally involve use of the 'five-factor' formula: ST = MAR.DR.ARF.RF.LPF where ST is the source term (Bq), MAR is the total available material-at-risk (Bq), DR is the damage ratio (no units), ARF is the airborne release fraction (no units), RF is the respirable fraction (no units), and LPF is the leak path factor (no units). Potential radiation doses are then calculated using: CED = χ/Q.BR.ST.DCF (2) where CED is the committed effective dose (Sv), χ/Q is the plume dispersion (s/m{sup 3}), BR is the breathing rate (m{sup 3}/s), ST is the source term (Bq), and DCF is the dose conversion factor (Sv/Bq). Conservative single value input parameters are typically used to represent ARF, RF, LPF and BR. The traditional methodology, while conservative, can lead to skewed conclusions in the balance between cost and risk reduction resulting in over engineered systems with greater design, construction and operating costs. Rather than using a bounding single point value for each parameter in the dose consequence calculation, SODA uses distributions for some or all of the parameters. Each input parameter distribution is stochastically sampled and the resulting dose consequence calculation is repeated many times to develop a dose consequence distribution. The resulting distribution provides more information for the decision maker to rely upon when determining the need for safety SSCs. The SODA application was developed using MATLAB and it incorporates the use of Monte Carlo techniques as well as a graphical user interface (GUI). The application includes user selection of the governing distribution for parameters such as the MAR, DR, ARF, RF, LPF,χ/Q, BR, and DCF. While MATLAB was used for the development work, the application is distributed as a self-contained executable program. Features of the application include pull down menus with available distributions for the various parameters. The user has the option to plot the input parameter distribution that results from the random sampling process. The resulting dose consequence distribution is automatically analyzed to determine its similarity to well know distributions. This was done using the Bayesian Information Criterion method. If the resulting distribution is sufficiently similar to a well-known distribution, the application provides information about the distributions such as the mean value and other parameters. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

SU-E-P-05: Electronic Brachytherapy: A Physics Perspective On Field Implementation

Purpose: We want to summarize our experience implementing a successful program of electronic brachytherapy at several dermatology clinics with the help of a cloud based software to help us define the key program parameters and capture physics QA aspects. Optimally developed software helps the physicist in peer review and qualify the physical parameters. Methods: Using the XOFT™ Axxent™ electronic brachytherapy system in conjunction with a cloud-based software, a process was setup to capture and record treatments. It was implemented initially at about 10 sites in California. For dosimetric purposes, the software facilitated storage of the physics parameters of surface applicators used in treatment and other source calibration parameters. In addition, the patient prescription, pathology and other setup considerations were input by radiation oncologist and the therapist. This facilitated physics planning of the treatment parameters and also independent check of the dwell time. From 2013–2014, nearly1500 such calculation were completed by a group of physicists. A total of 800 patients with multiple lesions have been treated successfully during this period. The treatment log files have been uploaded and documented in the software which facilitated physics peer review of treatments per the standards in place by AAPM and ACR. Results: The program model was implemented successfully at multiple sites. The cloud based software allowed for proper peer review and compliance of the program at 10 clinical sites. Dosimtery was done on 800 patients and executed in a timely fashion to suit the clinical needs. Accumulated physics data in the software from the clinics allows for robust analysis and future development. Conclusion: Electronic brachytherapy implementation experience from a quality assurance perspective was greatly enhanced by using a cloud based software. The comprehensive database will pave the way for future developments to yield superior physics outcomes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Current status of tritium calorimetry at TLK

Inside a tritium facility, calorimetry is an important analytical method as it is the only reference method for accountancy (it is based on the measurement of the heat generated by the radioactive decay). Presently, at Tritium Laboratory Karlsruhe (TLK), 4 calorimeters are in operation, one of isothermal type and three of inertial guidance control type (IGC). The volume of the calorimeters varies between 0.5 and 20.6 liters. About two years ago we started an extensive work to improve our calorimeters with regard to reliability and precision. We were forced to upgrade 3 of our 4 calorimeters due to the outdated interfaces and software. This work involved creating new LabView programs driving the devices, re-tuning control loops and replacing obsolete hardware components. In this paper we give a review on the current performance of our calorimeters, comparing it to recently available devices from the market and in the literature. We also show some ideas for a next generation calorimeter based on experiences with our IGC calorimeters and other devices reported in the literature. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Applying Constrained Bayesian Optimization to the Design of Critical Experiments

Often when planning a criticality experiment, many design configurations are iteratively investigated with a Monte Carlo transport code. The goal is that the experiment will be optimal with respect to some variable, like the fraction of fissions occurring at a certain energy range, while simultaneously being critical. Unfortunately, the Monte Carlo transport simulations are expensive, which can ultimately limit the number of configurations that can be explored. In this work, we present how Gaussian processes (GPs) can be used as a reduced-order model in a constrained Bayesian optimization (CBO) algorithm to design a criticality experiment. The GPs replace the Monte Carlo transport simulations that explore the design space. The CBO algorithm efficiently identifies new points in the design space to run the Monte Carlo transport code while respecting the criticality constraint. It does so in a manner that both improves the accuracy of the GP and finds the approximate global optimum. We demonstrate the performance of CBO with the design of a Thermal Epithermal eXperiment (TEX) for the criticality safety validation of nuclear waste models of the Hanford Tank Farm.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines

Abstract. The article presents a symbolic framework (also called computer algebra program) that is used to obtain, in symbolic mathematical form, the linear and nonlinear equations of motion of a mid-fidelity multibody system including rigid and flexible bodies. Our approach is based on Kane's method and a nonlinear shape function representation for flexible bodies. The shape function approach does not represent the state of the art for flexible multibody dynamics but is an effective trade-off to obtain mid-fidelity models with few degrees of freedom, taking advantage of the separation of space and time. The method yields compact symbolic equations of motion with implicit account of the constraints. The general and automatic framework facilitates the creation and manipulation of models with various levels of complexity by adding or removing degrees of freedom. The symbolic treatment allows for analytical gradients and linearized equations of motion. The linear and nonlinear equations can be exported to Python code or dedicated software. There are multiple applications, such as time domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins. In this article, we describe the method we used to systematically generate the equations of motion of multibody systems and present the implementation of the framework using the Python package SymPy. We apply the framework to generate illustrative land-based and offshore wind turbine models. We compare our results with OpenFAST simulations and discuss the advantages and limitations of the method. The Python implementation is provided as an open-source project.

Branlard, Emmanuel (ORCID:0000000277506128)↗