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DOE OSTI2025

Sierra/SD – Example Problems Manual – (V5.26)

The Example Problems Manual supplements the User’s Manual and the Theory Manual. The goal of the Example Problems Manual is to reduce learning time for complex end to end analyses. These documents are intended to be used together. See the User’s Manual for a complete list of the options for a solution case. All the examples are part of the Sierra/SD test suite. Each runs as is. The organization is similar to the other documents: How to run, Commands, Solution cases, Materials, Elements, Boundary conditions, and then Contact. The table of contents and index are indispensable. The Geometric Rigid Body Modes section is shared with the Users Manual.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Sierra/SD – Example Problems Manual – 5.28

Describes the set of example problems, input decks, and meshes that are distributed with Sierra/SD. The Example Problems Manual supplements the User’s Manual and the Theory Manual. The goal of the Example Problems Manual is to reduce learning time for complex end to end analyses. These documents are intended to be used together. See the User’s Manual for a complete list of the options for a solution case. All the examples are part of the Sierra/SD test suite. Each runs as is. The organization is similar to the other documents: How to run, Commands, Solution cases, Materials, Elements, Boundary conditions, and then Contact. The table of contents and index are indispensable. The Geometric Rigid Body Modes section is shared with the Users Manual.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Sierra/SD Example Problems Manual - 5.30

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Sierra/SD – Theory Manual – 5.22

Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high fidelity, validated models used in modal, vibration, static and shock analysis of structural systems. This manual describes the theory behind many of the constructs in Sierra/SD. For a more detailed description of how to use Sierra/SD, we refer the reader to User’s Manual. Many of the constructs in Sierra/SD are pulled directly from published material. Where possible, these materials are referenced herein. However, certain functions in Sierra/SD are specific to our implementation. We try to be far more complete in those areas. The theory manual was developed from several sources including general notes, a programmer_notes manual, the user’s notes and of course the material in the open literature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Sierra/SD – Theory Manual (V.5.24)

42 ENGINEERING↗

DOE OSTI2024

Sierra/SolidMechanics 5.22 Verification Tests Manual

Presented in this document is a small portion of the tests that exist in the Sierra/SolidMechanics (Sierra/SM) verification test suite. Most of these tests are run nightly with the Sierra/SM code suite, and the results of the test are checked versus the correct analytical result. For each of the tests presented in this document, the test setup, a description of the analytic solution, and comparison of the Sierra/SM code results to the analytic solution is provided. Mesh convergence is also checked on a nightly basis for several of these tests. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems. Additional example problems are provided in the Sierra/SM Example Problems Manual. Note, many other verification tests exist in the Sierra/SM test suite, but have not yet been included in this manual.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Sierra/SolidMechanics 5.28 Verification Tests Manual

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

ANSI/ASHRAE/IES Standard 90.1-2022 Performance Rating Method Reference Manual

This document is intended to be a reference manual for the Appendix G Performance Rating Method (PRM) of ANSI/ASHRAE/IES Standard 90.1-2022 (Standard 90.1-2022). The PRM can be used to demonstrate compliance with the standard and to rate the energy efficiency of commercial and high-rise residential buildings with designs that exceed the requirements of Standard 90.1. Use of the PRM for demonstrating compliance with Standard 90.1 was a new feature of ANSI/ASHRAE/IES Standard 90.1-2016 (Standard 90.1-2016). The procedures and processes described in the PRM reference manual (PRM-RM) are designed to provide consistency and accuracy by filling in gaps and providing additional details needed by users of the PRM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Federal Water Management Planning Manual

FEMP developed this Federal Water Management Planning Manual to provide a resource for designing a comprehensive water management program. The manual describes strategies to optimize water use at federal facilities, use water balance analysis methods, identify design elements and procurement best practices, and expand use of alternative water.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

User’s Manual for RESRAD-RDD&IND Code Version 2: Vol. 1—Methodology and Models Used in RESRAD-RDD&IND Code

RESRAD-RDD&IND is part of the RESRAD family of codes that Argonne National Laboratory developed for the U.S. Department of Energy (DOE). An earlier version, RESRAD-RDD published in 2009, dealt only with radiological dispersal device (RDD) incidents (DOE 2009). This new version, RESRAD-RDD&IND, is designed to evaluate both RDD incidents and improvised nuclear device (IND) incidents. This report is Volume 1 of the RESRAD-RDD&IND User’s Manual that documents the methodology, models, and radionuclide-specific data used in RESRAD-RDD&IND code Version 2.0. Volume 2 of the RESRAD-RDD&IND User’s Manual is called the User’s Guide for RESRAD-RDD&IND Code . It describes how to use RESRAD-RDD&IND code Version 2.0 and includes screen shots and parameter information.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

User’s Manual for RESRAD-RDD&IND Code Version 2: Vol. 2—User’s Guide for RESRAD-RDD&IND Code

Version 2.0 of the RESRAD-RDD&IND computer code is designed to support the implementation of protective action guides (PAGs) after a nuclear emergency incident including a radiological dispersal device (RDD) and/or an improvised nuclear device (IND) incident (EPA 2017). Eight different group types, addressing various decisions, are available for selection. The RESRAD-RDD&IND code calculates radiological doses, stay times, etc., for the selected group that the user wishes to focus on. (That is, the results for all the groups are not calculated simultaneously, and the input for those other groups do not matter, although some parameter values are shared between groups.) Version 2.0 has a user-friendly interface so that the RESRAD-RDD&IND code can be used with minimal training. For example, the user can select the major characteristics of the problem-event type, source term, and decision type from the left side of the interface and then calculate the results with the default assumptions for the exposure scenarios. More in-depth analysis would include specifying site-specific exposure scenario characteristics in the right side of the interface. The procedures for data entry and results viewing are self-explanatory. This is because common window maneuvering features and text instructions were incorporated in the interface design. General and context-specific help are available to aid users entering parameter values, as well. The RESRAD-RDD&IND computer code gives the user the option to select either an RDD or IND incident for analysis. For an RDD event analysis, 11 radionuclides (Am-241, Cf-252, Cm-244, Co-60, Cs-137, Ir-192, Po-210, Pu-238, Pu-239, Ra-226, and Sr-90) are included. These 11 radionuclides are the radionuclides most likely used for an RDD. More than 90 radionuclides can be selected for an IND event analysis. Initial default concentrations are provided for 44 radionuclides for a uranium-fueled IND event. These 44 radionuclides are those that would contribute significantly to the radiation dose associated with a uranium-fueled bomb detonation. The radionuclides generated from ingrowth of these 44 initial radionuclides are also automatically included in the analysis. Pu-239, Cs-134m, Ru-105, and Rb-89 and their progeny can be selected for analysis if they are detected and their concentrations are determined. This user’s guide, which is Volume 2 of the User’s Manual for RESRAD-RDD&IND Code Version 2, provides instructions to users on how to install the RESRAD-RDD&IND code, navigate the interface, and use the various features, including those discussed above, to set up an analysis and view/print the results in text outputs. Volume 1 of the User’s Manual for RESRAD-RDD&IND Code Version 2 (Yu et al. 2026), which contains descriptions of the methodology and theoretical basis for dose modeling and the mathematical equations implemented in the code, can be accessed and viewed through the Help menu in the code or can be downloaded from the RESRAD website (https://resrad.evs.anl.gov).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Risk Analysis of Radiological Release from Pu-238 Targets During Manual Handling

Pu-238 Isotope Production Targets are routinely installed in the Advanced Test Reactor (ATR) core, transferred, and stored in the spent fuel canal. These evolutions involve manual handling and manipulation of the targets underwater using long handled tools. The ATR Safety Analysis Report (SAR) postulates a design basis accident which results in damage from manual manipulation of targets, and radiological consequences must be determined for receptors inside the reactor facility, as well as public receptors. This presentation presents the analysis used to determine the radiological consequences due to potential target damage in the ATR canal. The analysis considered radionuclide release fractions, damage ratios for handling evolutions, and entrainment of radionuclides in the canal water.

Advanced Test Reactor↗

DOE OSTI2025

SPRUCE Root Production Assessed with Manual Minirhizotrons Resolved to Plant Functional Type, 2015-2021

This dataset contains raw root length and diameter for individual roots and estimated root population production measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Measurements started at the beginning of whole ecosystem warming manipulations in 2015 through 2021 (2015-05-26 to 2021-09-01). Root morphology and estimated production were quantified throughout the peat profile with manual minirhizotrons deployed within SPRUCE plots. Images were processed using commercial software to quantify the length and diameter of individual roots. Roots were visually assigned to a plant functional type (PFT) of either (ericaceous) shrub, herb (sedges and Maianthemum trifolium), or tree (Larix laricina, Picea mariana) based on expert opinion. The biomass of individual roots was estimated using PFT-specific allometric equations (Iversen et al., 2018). Production per day was estimated as the length of new roots produced between imaging sessions, divided by the number of days between imaging sessions. These values were placed on a m2 aboveground area basis and scaled to a standard depth of 1m (roots are not evenly distributed with depth, do not interpret value as being on a m3 basis). Maximum and average (weighted by production length) depth of each PFT were also estimated within each minirhizotron tube. Annual production was interpolated as the average of four methods to scale these data (see Weber et al, 2026). Standing crop of roots was estimated for each tube as the maximum visible amount (both length and mass) of roots of that PFT for that year. These data expand the ability of researchers to accurately estimate the belowground dynamics of peatland vegetation, as well as the role that fine roots may play in impacting the fluxes of carbon within peatlands. This dataset contains three data files in comma-separate values (*.csv) format. This dataset contains one data file in comma-separate values (.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

DOE OSTI2025

EOSPAC User's Manual: Version 6.5 Second Edition (Rev. 3)

The EOSPAC utility package is a collection of interface routines, which can be used to access the SESAME data library and perform various data adjustments and interpolations on the SESAME data. The SESAME data library contains both thermodynamic (e.g., equation of state) and transport coefficients (e.g., opacity and conductivity). Note, for simplicity, the term EOS (equation of state) used herein includes both thermodynamic variables and transport coefficients. The EOSPAC utility package is designed to be used by physics codes (henceforth ”host codes”) written in multiple languages and on multiple platforms. The remainder of this manual is organized into several sections. Chapter 2 discusses conventions such as data organization and routine names. Chapter 3 provides a general overview of basic theory and models implemented within EOSPAC. Chapter 4 provides a general overview of how to use the EOSPAC interface library. Chapters 5 to 7 describe the public interfaces of EOSPAC in detail. Chapter 8 provides a brief introduction to some related tools, which may be of use to the user. Chapter 9 provides details related to some selected numerical features of EOSPAC. Chapter 10 gives examples for using the interface routines described in chapters 5 to 7. Chapter 11 provides technical support contact information. Chapter 12 contains a brief set of acknowledgments. Chapter 13 contains a list of referenced documents. Finally, chapter 14 lists the “table types: mnemonic conventions”, “table types: grouped by category, sorted by name”, “table types: eospac version 5 cross reference”, “options: setup phase”, “data information parameters”, “meta-data information parameters”, “options: interpolation phase”, and the “error codes”.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Sierra/SolidMechanics 5.22: Example Problems Manual

Presented in this document are tests that exist in the Sierra/SolidMechanics example problem suite, which is a subset of the Sierra/SM regression and performance test suite. These examples showcase common and advanced code capabilities. A wide variety of other regression and verification tests exist in the Sierra/SM test suite that are not included in this manual.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

FECM/NETL CO 2 Saline Storage Cost Model (2024): User’s Manual

The U.S. Department of Energy's (DOE) Office of Fossil Energy and Carbon Management (FECM), in collaboration with the National Energy Technology Laboratory (NETL), has developed the FECM/NETL CO 2 Saline Storage Cost Model (CO2_S_COM). This Excel-based tool provides a comprehensive framework for estimating the costs and breakeven prices associated with storing carbon dioxide (CO 2 ) in deep saline formations. Designed from the perspective of a CO 2 storage site owner, the CO2_S_COM incorporates four integrated modules—project management, financial analysis, activity cost estimation, and geological evaluation—to deliver fast, robust, and actionable insights for evaluating project finances. This is the user's manual for CO2_S_COM. The model may be accessed at this link: FECM/NETL CO2 Saline Storage Cost Model CO2_S_COM 2024 (v4) - Submissions - EDX

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

PETSc/TAO Users Manual Revision 3.23

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations (PDEs) and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for implementing large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication.

97 MATHEMATICS AND COMPUTING↗