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DOE OSTI2014

ORIGEN Integration into MPACT

This report documents the work performed to meet the objectives of the RTM milestone on ORIGEN Integration into MPACT. The main objective of the milestone was to demonstrate the capability to perform depletion problems with MPACT where the point depletion calculation, which solves the nuclide transmutation equation with ORIGEN. While MPACT already has a depletion capability based on ORIGEN, it is desired to transition to using ORIGEN because ORIGEN has undergone far more validation and software quality assurance than the implementation in MPACT. The integration was completed by compiling and linking the two codes together at build-time. Within the TriBITS build structure used for VERA, the MPACT and SCALE code repositories are already present. An optional dependency was created between MPACT to SCALE. The API available within ORIGEN was modified slightly to be able to be used by MPACT and two new software classes were implemented in MPACT to call the necessary ORIGEN subroutines. To test the ORIGEN integration two benchmark problems were analyzed. The first being the spent fuel isotopics from the Takahama-3 PWR reactor. This involved comparisons to experimentally measured nuclide concentrations for select actinides and fission products, and comparisons to other numerical results. The benchmark defines a 1-D problem limiting most of the effects due to the accuracy of a multi-dimensional transport solver, and thus differences can be assumed to be more likely caused by the point depletion solver methodology and associated nuclear data. The other benchmark analyzed two problems from the reactor physics benchmarks for LWR next generation fuel. The two configurations simulated were a PWR UO 2 pin cell and PWR UO 2 assembly. For all the benchmark problems the results were generally in good agreement with the other numerical results which were computed by several codes, including MPACT with its original depletion implementation. Thus it is concluded that ORIGEN was successfully integrated into MPACT and is capable of providing reasonable results. An additional cursory analysis of the performance of the ORIGEN point depletion compared to native MPACT point depletion solver was also performed. In this analysis it was observed that ORIGEN, on average, needs about 0.28 to 0.30 seconds to perform the point depletion calculation. This is greater than the target performance of 0.1 seconds which means future work to investigate smaller depletion chains will be needed. The MPACT internal depletion solver completes the point depletion calculation in 0.02 seconds, so it may be used for routine analysis until the use of a simplified burnup chain and performance improvements are implemented. Other future work should focus on implementing the post-correction fix-up and quadratic depletion and computation of decay heat and gamma sources.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2011

International Space Station Passive Thermal Control System Analysis, Top Ten Lessons-Learned

The International Space Station (ISS) has been on-orbit for over 10 years, and there have been numerous technical challenges along the way from design to assembly to on-orbit anomalies and repairs. The Passive Thermal Control System (PTCS) management team has been a key player in successfully dealing with these challenges. The PTCS team performs thermal analysis in support of design and verification, launch and assembly constraints, integration, sustaining engineering, failure response, and model validation. This analysis is a significant body of work and provides a unique opportunity to compile a wealth of real world engineering and analysis knowledge and the corresponding lessons-learned. The analysis lessons encompass the full life cycle of flight hardware from design to on-orbit performance and sustaining engineering. These lessons can provide significant insight for new projects and programs. Key areas to be presented include thermal model fidelity, verification methods, analysis uncertainty, and operations support.

Iovine, John↗

NASA NTRS2018

International Space Station Passive Thermal Control System Top Ten Lessons-Learned

Final document not an Abstract attached. The International Space Station (ISS) has been on-orbit for nearly 20 years, and there have been numerous technical challenges along the way from design to assembly to on-orbit anomalies and repairs. The Passive Thermal Control System (PTCS) management team has been a key player in successfully dealing with these challenges. The PTCS team performs thermal analysis in support of design and verification, launch and assembly constraints, integration, sustaining engineering, failure response, and model validation. This analysis is a significant body of work and provides a unique opportunity to compile a wealth of real world engineering and analysis knowledge and the corresponding lessons-learned. The PTCS lessons encompass the full life cycle of flight hardware from design to on-orbit performance and sustaining engineering. These lessons can provide significant insight for new projects and programs. Key areas to be presented include thermal model fidelity, verification methods, analysis uncertainty, and operations support.

Iovine, John V.↗

NASA NTRS2018

International Space Station Passive Thermal Control System Top Ten Lessons-Learned

Iovine, John V.↗

DOE OSTI2025

Improving Runtime Performance of Tensor Computations using Rust From Python

In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide variety of applications. Recent runtime performance improvements have been demonstrated using Rust, a compiled language, from Python via extension modules leveraging the Python C API—e.g., web applications, data parsing, data validation, etc. Using this same approach, we study the runtime performance of key tensor kernels of increasing complexity, from simple kernels involving sums of products over data accessed through single and nested loops to more advanced tensor multiplication kernels that are key in low-rank tensor decomposition and tensor regression algorithms. In numerical experiments involving synthetically generated tensor data of various sizes and these tensor kernels, we demonstrate consistent improvements in runtime performance when using Rust from Python over 1) using Python alone, 2) using Python and the Numba just-in-time Python compiler (for loop-based kernels), and 3) using the NumPy Python package for scientific computing (for pyttb kernels).

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1981

Forecast of future aviation fuels: The model

A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

Ayati, M. B.↗

DOE OSTI2025

Reimagining Disassembly Interfaces With Visualization: Combining Instruction Tracing and Control Flow With DisViz

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. Here, we design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code.

Computer science↗

DOE OSTI2025

Lowering and Runtime Support for Fortran’s Multi-Image Parallel Features using LLVM Flang, PRIF, and Caffeine

This paper provides an overview of the multi-image parallel features in Fortran 2023 and their implementation in the LLVM flang compiler and the Caffeine parallel runtime library. The features of interest support a Single-Program, Multiple-Data (SPMD) programming model based on executing multiple “images”, each of which is a program instance. The features also support a Partitioned Global Address Space (PGAS) in the form of “coarray” distributed data structures. The paper discusses the lowering of multi-image features to the Parallel Runtime Interface for Fortran (PRIF) and the implementation of PRIF in the Caffeine parallel runtime library. This paper also provides an early view into the design of a new multi-image dialect of the LLVM Multi-Level Intermediate Representation (MLIR). We describe validation and testing of the resulting software stack, and demonstrate that performance compares favorably to another open-source compiler and runtime library: GNU Compiler Collection (GCC) gfortran and OpenCoarrays, respectively.

Bonachea, Dan↗

DOE OSTI2015

Social cost impact assessment of pipeline infrastructure projects

A key advantage of trenchless construction methods compared with traditional open-cut methods is their ability to install or rehabilitate underground utility systems with limited disruption to the surrounding built and natural environments. The equivalent monetary values of these disruptions are commonly called social costs. Social costs are often ignored by engineers or project managers during project planning and design phases, partially because they cannot be calculated using standard estimating methods. In recent years some approaches for estimating social costs were presented. Nevertheless, the cost data needed for validation of these estimating methods is lacking. Development of such social cost databases can be accomplished by compiling relevant information reported in various case histories. This paper identifies eight most important social cost categories, presents mathematical methods for calculating them, and summarizes the social cost impacts for two pipeline construction projects. The case histories are analyzed in order to identify trends for the various social cost categories. The effectiveness of the methods used to estimate these values is also discussed. These findings are valuable for pipeline infrastructure engineers making renewal technology selection decisions by providing a more accurate process for the assessment of social costs and impacts. - Highlights: • Identified the eight most important social cost factors for pipeline construction • Presented mathematical methods for calculating those social cost factors • Summarized social cost impacts for two pipeline construction projects • Analyzed those projects to identify trends for the social cost factors.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Evidence Report: Risk of Acute and Late Central Nervous System Effects from Radiation Exposure

Possible acute and late risks to the central nervous system (CNS) from galactic cosmic rays (GCR) and solar particle events (SPE) are a documented concern for human exploration of space. Acute CNS risks include: altered cognitive function, reduced motor function, and behavioral changes, all of which may affect performance and human health. Late CNS risks include neurological disorders such as Alzheimer's disease (AD), dementia and premature aging. Although detrimental CNS changes are observed in humans treated with high-dose radiation (e.g., gamma rays and protons) for cancer and are supported by experimental evidence showing neurocognitive and behavioral effects in animal models, the significance of these results on the morbidity to astronauts has not been elucidated. There is a lack of human epidemiology data on which to base CNS risk estimates; therefore, risk projection based on scaling to human data, as done for cancer risk, is not possible for CNS risks. Research specific to the spaceflight environment using animal and cell models must be compiled to quantify the magnitude of CNS changes in order to estimate this risk and to establish validity of the current permissible exposure limits (PELs). In addition, the impact of radiation exposure in combination with individual sensitivity or other space flight factors, as well as assessment of the need for biological/pharmaceutical countermeasures, will be considered after further definition of CNS risk occurs.

Nelson, Gregory A.↗

NASA NTRS2016

Evidence Report: Risk of Acute and Late Central Nervous System Effects from Radiation Exposure

Possible acute and late risks to the central nervous system (CNS) from galactic cosmic rays (GCR) and solar particle events (SPE) are concerns for human exploration of space. Acute CNS risks may include: altered cognitive function, reduced motor function, and behavioral changes, all of which may affect performance and human health. Late CNS risks may include neurological disorders such as Alzheimer's disease (AD), dementia and premature aging. Although detrimental CNS changes are observed in humans treated with high-dose radiation (e.g., gamma rays and 9 protons) for cancer and are supported by experimental evidence showing neurocognitive and behavioral effects in animal models, the significance of these results on the morbidity to astronauts has not been elucidated. There is a lack of human epidemiology data on which to base CNS risk estimates; therefore, risk projection based on scaling to human data, as done for cancer risk, is not possible for CNS risks. Research specific to the spaceflight environment using animal and cell models must be compiled to quantify the magnitude of CNS changes in order to estimate this risk and to establish validity of the current permissible exposure limits (PELs). In addition, the impact of radiation exposure in combination with individual sensitivity or other space flight factors, as well as assessment of the need for biological/pharmaceutical countermeasures, will be considered after further definition of CNS risk occurs.

Nelson, Gregory A.↗

DOE OSTI2016

WE-DE-201-04: Cross Validation of Knowledge-Based Treatment Planning for Prostate LDR Brachytherapy Using Principle Component Analysis

Purpose: To validate a knowledge-based algorithm for prostate LDR brachytherapy treatment planning. Methods: A dataset of 100 cases was compiled from an active prostate seed implant service. Cases were randomized into 10 subsets. For each subset, the 90 remaining library cases were registered to a common reference frame and then characterized on a point by point basis using principle component analysis (PCA). Each test case was converted to PCA vectors using the same process and compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best-matched library case. The seed arrangement was extracted from the best-matched case and used as a starting point for planning the test case. Any subsequent modifications were recorded that required input from a treatment planner to achieve V100>95%, V150<60%, V200<20%. To simulate operating-room planning constraints, seed activity was held constant, and the seed count could not increase. Results: The computational time required to register test-case contours and evaluate PCA similarity across the library was 10s. Preliminary analysis of 2 subsets shows that 9 of 20 test cases did not require any seed modifications to obtain an acceptable plan. Five test cases required fewer than 10 seed modifications or a grid shift. Another 5 test cases required approximately 20 seed modifications. An acceptable plan was not achieved for 1 outlier, which was substantially larger than its best match. Modifications took between 5s and 6min. Conclusion: A knowledge-based treatment planning algorithm for prostate LDR brachytherapy is being cross validated using 100 prior cases. Preliminary results suggest that for this size library, acceptable plans can be achieved without planner input in about half of the cases while varying amounts of planner input are needed in remaining cases. Computational time and planning time are compatible with clinical practice.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Study of a Docker Use-Case for HEP

Over the past few years, container technology has become increasingly promising as a means to seamlessly make our software available across a wider range of platforms. In December 2015, we decided to put together a set of docker images that serve as a demonstration of this container technology for managing a run-time environment for art-related software projects, and also serve as a set of test cases for evaluation of performance. Docker[1] containers provide a way to “wrap up a piece of software in a complete filesystem that contains everything it needs to run”. In combination with Shifter[2], such containers provide a way to run software developed and deployed on “typical” HEP platforms (such as SLF 6, in common use at Fermilab and on OSG platforms) on HPC facilities at NERSC. Docker containers provide a means of delivering software that can be run on a variety of hosts without needing to be compiled specially for each OS to be supported. This could substantially reduce the effort required to create and validate a new release, since one build could be suitable for use on both grid machines (both FermiGrid and OSG) as well as any machine capable of running the Docker container. In addition, docker containers may provide for a quick and easy way for users to install and use a software release in a standardized environment. This report contains the results and status of this demonstration and evaluation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Determining the range of validity of quasar X-ray and UV flux measurements for constraining cosmological model parameters

ABSTRACT We use six different cosmological models to study the recently released compilation of X-ray and UV flux measurements of 2038 quasars (QSOs) which span the redshift range 0.009 ≤ z ≤ 7.5413. We find, for the full QSO data set, that the parameters of the X-ray and UV luminosities LX−LUV relation used to standardize these QSOs depend on the cosmological model used to determine these parameters, i.e. it appears that the full QSO data set includes QSOs that are not standardized and so cannot be used for the purpose of constraining cosmological parameters. Subsets of the QSO data, restricted to redshifts z ≲ 1.5–1.7 obey the LX−LUV relation in a cosmological-model-independent manner, and so can be used to constrain cosmological parameters. The cosmological constraints from these lower z, smaller QSO data subsets are mostly consistent with, but significantly weaker than, those that follow from baryon acoustic oscillation and Hubble parameter measurements.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

A Similarity-Based Analysis Tool for Porting OpenMP Applications. In: Facing the Multicore-Challenge III. Lecture Notes in Computer Science, vol 7686.

Exascale computers are expected to exhibit an unprecedented level of complexity, thereby posing significant challenges for porting applications to these new systems. One of the ways to support this transition is to create tools that allow their users to benefit from prior successful porting experiences. The key to such an approach is the manner in which we define source code similarity, and whether similar codes can be ported in the same way to a given system. In this paper, we propose a novel approach based on the notion of similarity that uses static and dynamic code features to check if two serial subroutines can be ported with the same OpenMP strategy. Our approach creates an annotated family distance tree based on the syntactic structure of subroutines, where subroutines that belong to the same syntactic family and share the similar code features have a greater potential to be optimized in the same way. We describe the design and implementation of a tool, based upon a compiler and performance tool, that is used to gather the data to build this porting planning tree. We then validate our approach by analyzing the similarity in subroutines of the serial version of the NAS benchmarks and comparing how they were ported in the OpenMP version of the suite.

Ding, Wei↗

DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Location Identifiers, Metadata, and Map for Field Measurements at the East-Taylor Watershed Community Observatory, Colorado, USA (Version 3.3)

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East-Taylor Watershed Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.3 of the dataset and replaces the prior version 3.2 (see below for details on changes between the versions). Dataset description: The East River-Taylor Watershed is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East-Taylor Watershed in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East-Taylor Watershed and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.3. This version contains 167 new point locations, 1 new plot, and 2 new geographic regions. Overall, there are a total of 1439 point locations, 75 plots, and 54 geographic regions. Additionally, the kml map of locations and image now includes two boundaries (Upper Ohio Creek (UO) and Carbon Creek (CA)) outside of the East River watershed (USGS HUC-10) and accompanying stream network that represents areas of focus. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East-Taylor Watershed measurement locations added to this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgments: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the 2018 NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

2018 NEON and 2025 CHESS Campaigns↗