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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 181 records · Page 10

Development and validation of a software for simulating γ-γ coincidence emission and detection probabilities

Gamma-gamma coincidence spectrometers have the potential to significantly enhance detection sensitivity for ultra-trace radionuclide measurements. The implementation of these spectrometers, however, is limited by the complexity of acquisition hardware, data processing and quantification. This work reports development of a novel radionuclide quantification software for γ-γ coincidence measurements. For any radionuclide, the software parses the Evaluated Nuclear Structure Data File (ENSDF) database, recursively simulating all possible γ-γ coincidence signatures and their respective emission and detection probabilities. Implemented using Python programming language, the software employs several strategies to boost overall computational performance. Since coincidence-based spectrometers are of notable interest in monitoring compliance for the Comprehensive Nuclear-Test-Ban Treaty (CTBT), the software’s execution was tested for 84 CTBT-relevant radionuclides. To date, the software has been experimentally validated for 15 radionuclides using the Advanced Radionuclide Gamma spectrOmeter (ARGO) at Pacific Northwest National Laboratory, USA (PNNL). Notably, the software can be operated in convergence mode, whereby coincidence detection efficiency’s convergence behavior can help avoid unreliable radionuclide activity estimates. With growing number of coincidence spectrometers worldwide, this paper aims to assist the radiation metrology community in developing similar software for their system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Generalizable coordination of large multiscale workflows: challenges and learnings at scale

The advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.

Bhatia, Harsh↗

IER 519 Experiment Execution Report: TEX Hanford

The TEX-Hanford experiments were performed at the National Criticality Experiments Research Center (NCERC) at the Device Assembly Facility (DAF) at the Nevada Nuclear Security Site (NNSS). Three configurations were measured, with the measurements occurring in December 2025 and January 2026. The Fe-14 configuration was executed in December 2025; the Fe-11 configuration began its handstack on the Planet critical assembly machine in December 2025, but was taken critical in January 2026; the Fe-16 configuration was executed entirely in January 2026. The TEX-Hanford experiments were performed on the Planet critical assembly machine utilizing using PANN (Plutonium Aluminum No Nickel) ZPPR (Zero Power Physics Reactor) plutonium plates as fuel. The configurations were moderated with varying thicknesses of HDPE and iron absorber. This document details the configurations that were measured, preliminary reactivity measurements of the measured configurations, data files from the neutron detection systems, and results from the coordinate-measuring machine (CMM) measurements of the final stack height measurements each configuration.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

TF9 Dataset Analysis

Incident Overview: In the time between November 2, 2019 and November 11, 2019, WheelByte was plagued by breaches in security. These insecurities led to breaches in customer data, company data, and even the death of an employee, Matthew Swift. They have launched an investigation into the company’s computer systems in hopes to find the root cause. We have been provided with the following artifacts from WheelByte: memory images, disk images, network packet captures, and emails. We have found multiple cyber-system attacks against WheelByte. Our investigation lasted from July 13th - August 3rd, 2023. WheelByte allowed us to look at any and every file, and there were no restrictions on what we could or could not use in our investigation. By the end of our investigation, we have been able to deduce who is behind the attack, what they have done, and why they did it. A company that is closely related to WheelByte is called Slyde. Slyde sells electric scooters and it is known that the Chief Executive Officer (CEO) of Slyde, Kimberly Holmes, sees WheelByte as a threat to business, as Wheelbyte sells electric skateboards. We have been able to deduce that Slyde is likely behind many of the malicious attacks. We have seen exfiltration addresses to Slyde domains, along with other Slyde information within their malware. We can see lots of traffic to and from Slyde Internet Protocol (IP) addresses. This may be an attempt to cripple WheelByte’s productivity to remove Slyde’s competitor from the market.

97 MATHEMATICS AND COMPUTING↗

Project Description for Student Symposium 2022: Modeling Approach to Critical in the Upcoming CERBERUS Experiment

The Critical Experiment Reflected By copper to BettEr Understand Scattering [CERBERUS] is an integral experiment that will be executed at the end of FY23. The experiment is designed to study the intermediate energy range, in particular elastic and inelastic scattering of neutrons in copper. Approaching criticality safely is of utmost importance, and having a computer mode is a useful tool when determining changes that can safely be made to the experiment. Multiple simulations are needed to analyze the approach to critical, and manually creating MCNP input files is both tedious and error prone. To solve this problem, a python script was created to develop and run these input files.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗

Production of Salable Rare Earths Products from Coal and Coal Byproducts in the U.S. Using Advanced Separation Processes (Final Technical Report)

The entirety of the report that follows this brief Executive Summary Chapter is the product of a Funding Announcement by the US Department of Energy (DOE), Office of Fossil Energy, originally issued 09/28/2016, otherwise known as Funding Opportunity Number: DE-FOA-0001627, under the title of “Production of Rare Earth Elements from Domestic U.S. Coal and Coal By-Products” The solicitation was subsequently amended several times ending in amendment 000005. Ultimately a consortium designated “Minerals Refining and Rare Earth Consortium” (to which many of the remaining participants recited in this report joined) filed a proposal on December 8, 2016. On June 9, 2017, the contracting office of the US DOE, National Energy Technology Laboratory (NETL), alerted Dr. Keim, the Authorized Representative of the December 08, 2016, proposal, that the consortium proposal had been selected. In summary, this report presents the results of the efforts in meeting the stated project objectives as outlined in DE-FOA-0001627 to wit: “The main objective of the proposed work is to design and construct a demonstration plant that can extract rare earth elements (REEs) from domestic U.S. coal and coal by-products. The plant will produce salable rare earth elements and intermediates, including at a minimum three individual REEs (preferably heavy rare earths) at 90-99% purities, with output capacities in excess of 10 pounds per day of composite rare earth products (reported as oxides). In addition, the plant will produce high value premium solid fuels, magnetite and other materials to increase the economic viability of the plant and to create jobs. If successful, the proposed work will help develop a domestic supply chain of the critical materials that are essential for the development of renewable energy resources, high-tech industries and a strong national defense.” The principal investigators immediately launched the tasks outlined in the Statement of Project Objectives (SOPO) in accordance to its Project Management Plan (PMP) based on their collective knowledge of the subject matter and the expectations generated by preliminary laboratory results. Events and circumstances related in part to the vagaries of the coal mining, processing, and marketing industry triggered a series of carefully deliberated step-wise transitions from the original Lower Kittanning coal source in West Virginia to an ultimate stopping point with the Fire Clay Seam (Hazard No. 4) rejects from Blue Diamond Coal Company in Kentucky (Operated by Blackhawk Mining LLC). These transitional steps are discussed in substantial detail in the body of this report. Notably, substantial data of potential future significance, both in terms of sample collection, processing, and testing, were collected in the process. Unfortunately, as reported in the Techno-Economic Chapter of this report, none of these efforts resulted in an anticipated economically viable venture. Thus, the principals associated with this project elected not to transition into a “Pilot Plant” operation. A proposal for funding that pilot plant was therefore not requested. The outcome of this initiative under DE-FOA-0001627 notwithstanding, the following portions of this Executive Summary outline the activities of the investigators. That said, the reader is encouraged to examine the main body of this report in which each chapter is assembled as a free-standing product, complete with Table of Contents, for practical and easy reference purposes.

01 COAL, LIGNITE, AND PEAT↗

Using Parameter Sweep in WaterTAP to Analyze New Water Treatment Technologies

We describe a powerful and generalized parameter sweep tool in this report that was originally developed to analyze the performance of existing and novel water treatment models being developed in WaterTAP. Since WaterTAP is built upon IDAES and Pyomo, the parameter sweep tool can be used to systematically explore and debug the behavior of most Pyomo and IDAES numerical models. In order to enable meaningful analyses, the parameter sweep tool has been designed with the following features: 1) Model flexibility: The parameter sweep tool does not enforce any restrictions on the types of models that can be used with it. As long as a Pyomo model can be solved and the parameter is active and mutable, the tool only needs functions that describe how to run the model, the sweep parameters, and the output quantities of interest. 2) Flexible sampling: The parameter sweep tool has inbuilt functions to generate samples from a random distribution or a multidimensional Euclidean space. Furthermore, the users have to ability to supply samples generated from a tool of their choice. 3) Multiple sweep types: A user can choose from one of 3 types of parameter sweeps depending on their needs. 4) Detailed outputs: Outputs generated by the parameter sweep tool can be stored in detailed H5 file or user-friendly CSV files for post processing. 5) Parallel computing: The parameter sweep supports shared and distributed memory parallel computing to enable the use of high performance computers (HPC) for large-scale analyses. 6) Modular: The parameter sweep tool is self-contained and can easily be integrated within an outer-loop analysis or as desired by the user. 7) Ease of use: The tool is well documented and a simple sweep can be easily executed by following the online documentation in a few lines of code. We demonstrate the use of the parameter sweep tool on a simple water treatment system from the WaterTAP repository and show its parallel scaling performance on an Apple laptop and NREL's Eagle HPC. The parameter sweep tool is actively being used with models currently being developed within WaterTAP and we expect its use to grow beyond it to other IDAES and Pyomo models.

97 MATHEMATICS AND COMPUTING↗

h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms

Summary Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high‐performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real‐world applications, we have created h5bench , a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one‐dimensional arrays, two‐dimensional meshes, three‐dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre‐exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

97 MATHEMATICS AND COMPUTING↗

Implementation of the Windowed Multipole Method in Shift

The windowed multipole (WMP) method has been implemented in the Shift Monte Carlo (MC) radiation transport code with support for both CPU and GPU execution. With this method, small WMP data libraries (~100 MB) can be used to accurately Doppler broaden cross sections to arbitrary temperatures “on the fly” during an MC simulation. This approach yields significant memory savings relative to traditional methods, making it ideal for high-fidelity analysis such as coupled multiphysics simulations. This document provides the exact forms of the WMP equations used by Shift, as well as a detailed description of the structure of WMP HDF5 data files provided by the Massachusetts Institute of Technology (MIT). The Shift implementation has been validated against the OpenMC radiation transport code, with excellent agreement demonstrated for 70 nuclides across an operative range of temperatures. CPU and GPU performance testing using a small module reactor (SMR) problem demonstrated that this method decreases the neutron tracking rate by a factor of ~2 on the Summitdev machine. A new set of WMP data being developed in-house will employ novel methods to improve tracking rates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Increased accuracy of multiphysics simulations through flexible execution, transient algorithms, and modular physics

The MOOSE framework is a foundational capability used by the NEAMS program to create over 15 different simulation tools for advanced nuclear reactors. Due to MOOSE’s broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. Such improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work described in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities were implemented in the same order as they are covered in this report: multiple time integrators in the same input file, initial design of framework Components, an input file Application block, extension of NetGen to 3D geometries in MOOSE, and deployment of executors in the multi-system paradigm. These five additions are fundamental capabilities that will be leveraged by many NEAMS applications.

97 MATHEMATICS AND COMPUTING↗

Occupancy Simulator Solver v1.0

Occupant schedules at room level are crucial information to building energy modeling which quantifies occupant-related energy efficiency technologies and controls for new building design as well as existing building retrofits to reduce energy use and GHG emissions in buildings. The Occupancy Simulator Solver is an executable software component that simulates the stochastic behavior of occupant presence and movement in a building using the Markov-chain model. Occupancy Simulator Solver is the calculation engine of the web App Occupancy Simulator, which takes high level input on occupants, spaces and events, then calls the Solver to simulate occupant movement and generate occupant schedules for each space. The generated schedules capture diversity and stochastic nature of occupant activities (e.g., in office, meetings, breaks, lunch). These schedules are stored in CSV files and can be downloaded from the App and used for building performance simulation.

Hong, Tianzhen↗

TRACE Input Modernization

This work presents a Tom’s Obvious Minimal Language (TOML)-based representation of input for the US Nuclear Regulatory Commission’s TRAC/RELAP Advanced Computational Engine (TRACE) thermal hydraulics code. Implemented using the Workbench Analysis Sequence Processor (WASP), the approach maps traditional TRACE input structures to a hierarchical format composed of named parameters, typed values, and native data collections. The resulting representation preserves TRACE’s existing modeling capabilities while providing a modern, structured interface for model development and management. WASP further extends TOML through a file import directive that supports modular model composition and reusable input organization. In addition, WASP provides extended array data entry convenience with various data repeat and interpolation capabilities. Examples of the new TOML syntax are provided for major TRACE input categories, including hydraulic components, heat structures, control systems, and trip logic. The TOML representation establishes a foundation for improved validation, tooling, automation, and model maintainability while remaining compatible with existing TRACE workflows. To facilitate migration to the TOML-based input format, the TRACE executable now supports conversion of native TRACE input into an intermediate JSON representation. A Python utility subsequently transforms the JSON data into an equivalent TOML model. Lastly, the TRACE executable now supports execution using TOML-formatted input.

Lefebvre, Robert A. [Oak Ridge National Laboratory↗

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗

GIWAXS-SIIRkit: scattering intensity, indexing and refraction calculation toolkit for grazing-incidence wide-angle X-ray scattering of organic materials

Grazing-incidence wide-angle X-ray scattering (GIWAXS) has become an increasingly popular technique for quantitative structural characterization and comparison of thin films. For this purpose, accurate intensity normalization and peak position determination are crucial. At present, few tools exist to estimate the uncertainties of these measurements. Here, a simulation package is introduced called GIWAXS-SIIRkit , where SIIR stands for scattering intensity, indexing and refraction. Furthermore, the package contains several tools that are freely available for download and can be executed in MATLAB. The package includes three functionalities: estimation of the relative scattering intensity and the corresponding uncertainty based on experimental setup and sample dimensions; extraction and indexing of peak positions to approximate the crystal structure of organic materials starting from calibrated GIWAXS patterns; and analysis of the effects of refraction on peak positions. Additionally, each tool is based on a graphical user interface and designed to have a short learning curve. A user guide is provided with detailed usage instruction, tips for adding functionality and customization, and exemplary files.

36 MATERIALS SCIENCE↗

Data and Scripts associated with “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling.”

This data package is associated with the publication “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling” submitted to Geoscientific Model Development (Muller et al., 2024). In this manuscript, organic matter chemistry and thermodynamics are directly connected to reactive transport simulators through the newly developed Lambda-PFLOTRAN (Parallel Reactive Flow and Transport model) workflow tool that succinctly incorporates organic matter chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate aerobic respiration of the organic matter and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow, executed through a Jupyter Notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow, and transport code PFLOTRAN. This data package contains Jupyter Notebook based workflows for two test cases for running biogeochemical simulations of organic matter oxidation identified by FTICR-MS. It contains four primary folders (workflow, data, src, and analysis), a file-level metadata file (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_flmd.csv) that lists all the files contained in this data package with a short description of each, and a data dictionary (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_dd.csv) file that describes the tabular column headers. The ‘workflow’ folder contains the Jupyter Notebook based workflows for running the lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘data’ folder contains the FTICR-MS data, initial conditions, and incubation data for test cases 1 and 2 in folders titled ‘WHONDRS’ and ‘Colloids’, respectively. The data folder also has a ‘Database’ folder containing a reaction network for bulk organic matter (assumed to be CH2O) and a general database for PFLOTRAN (hanford_rxn_network). The CH2O reaction network defines bulk organic matter oxidation. Biogeochemical simulations are completed for both the lambda binned organic matter and bulk organic matter reaction networks. The ‘hanford_rxn_network’ database includes information required for PFLTORAN simulations including ion size, molar mass, and charge of the aqueous species, gases, and minerals phases. The ‘src’ folder contains python source codes for performing lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘analysis’ folder contains outputs from the test cases 1 and 2 including lambda analysis, PFLOTRAN runs and the calibration results.

54 ENVIRONMENTAL SCIENCES↗

SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking (FY2020 Progress Report)

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g. height, color and grayscale values; all as functions of a location in a plane projection). It is the objective of this project to produce a user-friendly interface that incorporated all operations needed to perform surface examination. For this reason, a Matlab-based Graphical User Interface (GUI) was created to integrate data input with software developed for processing and evaluation. In summary, the GUI permits selective downloading of binary data, interrogation of attributes, data labeling, flagging of significant features, execution of Machine Learning (ML) algorithms, output of parameters for trained ML algorithms, reports of ML model accuracy with respect to labeled data, and generation of graphical representations of various analyses.

3013 corrosion↗

Sierra/SolidMechanics 5.6 User's Manual

Sierra/SolidMechanics (Sierra/SM) is a three-dimensional solid mechanics code with a versatile element library, nonlinear material models, large deformation capabilities, and contact. It is built on the SIERRA Framework. SIERRA provides a data management framework in a parallel computing environment that allows the addition of capabilities in a modular fashion. Contact capabilities are parallel and scalable. This document provides information about the functionality in Sierra/SM and the command structure required to access this functionality in a user input file. This document is divided into chapters based primarily on functionality. For example, the command structure related to the use of various element types is grouped in one chapter; descriptions of material models are grouped in another chapter. Sierra/SM provides both explicit transient dynamics and implicit quasistatics and dynamics capabilities. Both the explicit and implicit modules are highly scalable in a parallel computing environment. In the past, the explicit and implicit capabilities were provided by two separate codes, known as Presto and Adagio, respectively. These capabilities have been consolidated into a single code. The executable is named Adagio, but it provides the full suite of solid mechanics capabilities, for both implicit and explicit. The Presto executable has been disabled as a consequence of this consolidation.

97 MATHEMATICS AND COMPUTING↗