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DOE OSTI2015

Mixed Initiative Visual Analytics Using Task-Driven Recommendations

Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support tasks involved in discovery and sensemaking, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems, at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with such analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Researchers studying the sensemaking process have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present a candidate set of design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences on user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach.

active data↗

DOE OSTI2020

Exploit, Malware And Vulnerability Scoring Application

Exploit, Malware and Vulnerability (EMV) is a way for users to create and use their own scoring schema against various configurations they might be scoring cyber issues against. This application was created to take the EMV process and develop a GUI to accomplish this task. EMV application is customizable and configurable, interfaces with STIX objects and allows exporting scored objects.

Priest, ZacharyM↗

DOE OSTI2019

SimCCS: An open-source tool for optimizing CO 2 capture, transport, and storage infrastructure

Commercial-scale carbon capture and storage (CCS) technology will involve deploying infrastructure on a massive and costly scale. This effort will require careful and comprehensive planning to ensure that capture locations, storage sites, and the dedicated CO 2 distribution pipelines are selected in a robust and cost-effective manner. Presented in 2009, SimCCS is an optimization model for integrated system design that enables researchers, stakeholders, and policy makers to design CCS infrastructure networks. SimCCS 2.0 is a complete, ground-up redesign that is now a portable software package, useable and shareable by the CCS research, industrial, policy, and public communities. SimCCS 2.0 integrates multiple new capabilities including a refined optimization model, novel candidate network generation techniques, and optional integration with high-performance computing platforms. Accessing user-provided CO 2 source, sink, and transportation data, SimCCS 2.0 creates candidate transportation routes and formalizes an optimization problem that determines the most cost-effective CCS system design. This optimization problem is further solved either through a high-performance computing interface, or through third-party software on a local desktop computing platform. Finally, SimCCS 2.0 employs an open-access geographic information system framework to enable analysis and visualization capabilities. SimCCS 2.0 is written in Java and is publicly available via GitHub to encourage collaboration, modification, and community development.

42 ENGINEERING↗

DOE OSTI2015

MarFS, Version 1

There is a sea (mar is Spanish for sea) of data out there that needs to be handled efficiently. Object Stores are filling the hole of managing large amounts of data efficiently. However, in many cases, and our HPC case in particular, we need a traditional file (POSIX) interface to this data as HPC I/O models have not moved to object interfaces, such as Amazon S3, CDMI, etc.Eventually Object Store providers may deliver file interfaces to their object stores, but at this point those interfaces are not ready to do the job that we need done. MarFS will glue together two existing scalable components: a file system's scalable metadata component that provides the file interface; and existing scalable object stores (from one or more providers). There will be utilities to do work that is not critical to be done in real-time so that MarFS can manage the space used by objects and allocated to individual users.

ECP↗

DOE OSTI2026

SPAROW: Stochastic Programming and Related Optimization Workflows

SAND2026-16703O SPAROW: Stochastic Programming and Related Optimization Workflows is a Python library tool that facilitates the development and solution of stochastic programming problems. It provides a user-friendly class structure for defining stochastic programs through scenario-based representations of uncertainties. SPAROW incorporates multiple optimization strategies, including integer programming with all scenarios, progressive hedging, Benders decomposition, and Snoglode, a novel technique developed by Carnegie Mellon University. It also features interfaces to external solvers and functions that are commonly used in analysis workflows, making it applicable to a wide range of scientific and engineering design challenges, particularly in power grid planning. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-NM), Albu↗

DOE OSTI2025

popclass: A Python Package for Classifying Microlensing Events

popclass is a Python package that provides a flexible, probabilistic framework for classifying the lens of a gravitational microlensing event. Gravitational microlensing occurs when a massive foreground object (e.g., a star, white dwarf or black hole) passes in front of and lenses the light from a distant background source. This causes an apparent brightening, and shift in position, of the background source. In most cases, characteristics of the microlensing signal do not contain enough information to definitively identify the lens type. Different lens types lie in different but overlapping regions of the characteristics of the microlensing signal. For example, black holes tend to be more massive than stars and therefore cause microlensing signals that are longer. Current Galactic simulations enable us to predict where different lens types lie in the observational space and can therefore be used to classify events (e.g., Lam et al., 2020). popclass allows the user to match the characteristics of a microlensing signal with a simulation of the Galaxy to calculate lens type probabilities for the event (see Figure 1). Constraints on any microlensing signal properties and any Galactic model can be used. popclass comes with an interface to ArviZ (Kumar et al., 2019) and PyMultiNest (Buchner et al., 2014) for microlensing signal constraints, as well as pre-loaded Galactic models, plotting functionality, and methods to quantify the classification uncertainty. The probabilistic framework for popclass was developed in Perkins et al. (2024), used in Fardeen et al. (2024) and has been applied to classifying events in Kaczmarek et al. (2025).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Inventory and Waste Characterization Status Report and OWL Update.

This report represents completion of milestone deliverable M2SF-18SNO10309013 "Inventory and Waste Characterization Status Report and OWL Update that reports on FY2018 activities for the work package (WP) SF-18SNO1030901. This report provides the detailed final information for completed FY2018 work activities for WP SF-18SN01030901, and a summary of priorities for FY2019. This status report on FY2018 activities includes evaluations of waste form characteristics and waste form performance models, updates to the OWL development, and descriptions of the two planned management processes for the OWL. Updates to the OWL include an updated user's guide, additions to the OWL database content for wastes and waste forms, results of the Beta testing and changes implemented from it. There are two processes being planned in FY2018, which will be implemented in FY2019. One process covers methods for interfacing with the DOE SNF DB (DOE 2007) at INL on the numerous entries for DOE managed SNF, and the other process covers the management of updates to, and version control/archiving of, the OWL database. In FY2018, we have pursued three studies to evaluate/redefine waste form characteristics and/or performance models. First characteristic isotopic ratios for various waste forms included in postclosure performance studies are being evaluated to delineate isotope ratio tags that quantitatively identify each particular waste form. This evaluation arose due to questions regarding the relative contributions of radionuclides from disparate waste forms in GDSA results, particularly, radionuclide contributions of DOE-managed SNF vs HLW glass. In our second study we are evaluating the bases of glass waste degradation rate models to the HIP calcine waste form. The HIP calcine may likely be a ceramic matrix material, with multiple ceramic phases with/without a glass phase. The ceramic phases are likely to have different degradation performance from the glass portion. The distribution of radionuclides among those various phases may also be a factor in the radionuclide release rates. Additionally, we have an ongoing investigation of the performance behavior of TRISO particle fuels and are developing a stochastic model for the degradation of those fuels that accounts for simultaneous corrosion of the silicon carbide (SiC) layer and radionuclide diffusion through it. The detailed model of the TRISO particles themselves, will be merged with models of the degradation behavior(s) of the graphite matrix (either prismatic compacts or spherical "pebbles") containing the particles and the hexagonal graphite elements holding the compacts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Structure, dynamics and stability of water/scCO 2 /mineral interfaces from ab initio molecular dynamics simulations

Here, the interface between a solid and a complex multi-component liquid forms a unique reaction environment whose structure and composition can significantly deviate from either bulk or liquid phase and is poorly understood due the innate difficulty to obtain molecular level information. Feldspar minerals, as typified by the Ca-end member Anorthite, serve as prototypical model systems to assess the reactivity and ion mobility at solid/water-bearing supercritical fluid (WBSF) interfaces due to recent X-ray based measurements that provide information on water-film formation, and cation vacancies at these surfaces. Using density functional theory based molecular dynamics, which allows the evaluation of reactivity and condensed phase dynamics on equal footing, we report on the structure and dynamics of water nucleation and surface aggregation, carbonation and Ca mobilization under geologic carbon sequestration scenarios (T = 323 K and P = 90 bar). We find that water has a strong enthalpic preference for aggregation on a Ca-rich, O-terminated anorthite (001) surface, but entropy strongly hinders the film formation at very low water concentrations. Carbonation reactions readily occur at electron-rich terminal Oxygen sites adjacent to cation vacancies, when in contact with supercritical CO 2 . Cation vacancies of this type can form readily in the presence of a water layer that allows for facile and enthalpicly favorable Ca 2+ extraction and solvation. Apart from providing unprecedented molecular level detail of a complex three component (mineral, water and scCO 2 ) system), this work highlights the ability of modern capabilities of AIMD methods to begin to qualitatively and quantitatively address structure and reactivity at solid-liquid interfaces of high chemical complexity. This work was supported by the US Department of Energy, Office of Fossil Energy (M.-S. L., B. P. M. and V.-A. G.) and the Office of Basic Energy Science, Division of Chemical Sciences, Geosciences and Biosciences (R.R.), and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for DOE by Battelle. Computational resources were provided by PNNL’s Platform for Institutional Computing (PIC), the W. R. Wiley Environmental Molecular Science Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research located at PNNL and the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

TH-A-19A-08: Intel Xeon Phi Implementation of a Fast Multi-Purpose Monte Carlo Simulation for Proton Therapy

Purpose: Recent studies have demonstrated the capability of graphics processing units (GPUs) to compute dose distributions using Monte Carlo (MC) methods within clinical time constraints. However, GPUs have a rigid vectorial architecture that favors the implementation of simplified particle transport algorithms, adapted to specific tasks. Our new, fast, and multipurpose MC code, named MCsquare, runs on Intel Xeon Phi coprocessors. This technology offers 60 independent cores, and therefore more flexibility to implement fast and yet generic MC functionalities, such as prompt gamma simulations. Methods: MCsquare implements several models and hence allows users to make their own tradeoff between speed and accuracy. A 200 MeV proton beam is simulated in a heterogeneous phantom using Geant4 and two configurations of MCsquare. The first one is the most conservative and accurate. The method of fictitious interactions handles the interfaces and secondary charged particles emitted in nuclear interactions are fully simulated. The second, faster configuration simplifies interface crossings and simulates only secondary protons after nuclear interaction events. Integral depth-dose and transversal profiles are compared to those of Geant4. Moreover, the production profile of prompt gammas is compared to PENH results. Results: Integral depth dose and transversal profiles computed by MCsquare and Geant4 are within 3%. The production of secondaries from nuclear interactions is slightly inaccurate at interfaces for the fastest configuration of MCsquare but this is unlikely to have any clinical impact. The computation time varies between 90 seconds for the most conservative settings to merely 59 seconds in the fastest configuration. Finally prompt gamma profiles are also in very good agreement with PENH results. Conclusion: Our new, fast, and multi-purpose Monte Carlo code simulates prompt gammas and calculates dose distributions in less than a minute, which complies with clinical time constraints. It has been successfully validated with Geant4. This work has been financialy supported by InVivoIGT, a public/private partnership between UCL and IBA.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2017

FPZIP

fpzip is a C/C++ library for lossless and lossy compression of 1D, 2D, and 3D single- and double-precision floating-point arrays. Although designed for lossless compression, the user may optionally request lossy compression by specifying how many bits of precision per floating-point value to retain. Fpzip supports streaming compression and decompression with either a memory buffer or a file as source or destination. In addition to a simple C interface, fpzip comes with an executable for (de)compressing binary files.

Lindstrom, PeterG↗

DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗

DOE OSTI2013

A Multiscale Modeling Approach to Analyze Filament-Wound Composite Pressure Vessels

A multiscale modeling approach to analyze filament-wound composite pressure vessels is developed in this article. The approach, which extends the Nguyen et al. model [J. Comp. Mater. 43 (2009) 217] developed for discontinuous fiber composites to continuous fiber ones, spans three modeling scales. The microscale considers the unidirectional elastic fibers embedded in an elastic-plastic matrix obeying the Ramberg-Osgood relation and J2 deformation theory of plasticity. The mesoscale behavior representing the composite lamina is obtained through an incremental Mori-Tanaka type model and the Eshelby equivalent inclusion method [Proc. Roy. Soc. Lond. A241 (1957) 376]. The implementation of the micro-meso constitutive relations in the ABAQUS® finite element package (via user subroutines) allows the analysis of a filament-wound composite pressure vessel (macroscale) to be performed. Failure of the composite lamina is predicted by a criterion that accounts for the strengths of the fibers and of the matrix as well as of their interface. The developed approach is demonstrated in the analysis of a filament-wound pressure vessel to study the effect of the lamina thickness on the burst pressure. The predictions are favorably compared to the numerical and experimental results by Lifshitz and Dayan [Comp. Struct. 32 (1995) 313].

Filament-wound composite, pressure vessel, multisc↗

DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗

DOE OSTI2024

Taking the MPI standard and the open MPI library to exascale

The Open MPI for Exascale (OMPI-X) project was one of two in the Exascale Computing Project (ECP) focused on advancing the MPI ecosystem. The OMPI-X team worked with other MPI Forum members to champion several important features for inclusion in the MPI 4.0, 4.1, and upcoming 5.0 MPI standard versions, in support of the needs of exascale applications and systems. The team also worked with the larger Open MPI community to bring implementations of these new features and other enhancements into Open MPI, one of the leading open-source implementations of the MPI interface. Here, this paper describes the motivation for the work of the OMPI-X project in the context of exascale computing needs, the nature of the resulting new capabilities in the MPI standard, and how they were implemented in the Open MPI library. Features include improved support for “MPI + X” programming models through partitioned communications and support for user-level threading, sessions, fault tolerance through the user-level fault mitigation (ULFM) and Reinit models, and other features. We also discuss enhancements to Open MPI providing improved performance and scalability for existing features, such as collective operations, one-sided operations, support for the Slingshot-11 interconnect of the initial exascale systems, and how the OMPI-X team worked to improve quality assurance for the Open MPI library, particularly on platforms of interest to the Department of Energy community.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Tigres Workflow Library: Supporting Scientific Pipelines on HPC Systems

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterative workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechanisms) that affect the performance of scientific workflows on HPC systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Analysis of DOE's Roof Savings Calculator with Comparison to other Simulation Engines

A web-based Roof Savings Calculator (RSC) has been deployed for the Department of Energy as an industry-consensus tool to help building owners, manufacturers, distributors, contractors and researchers easily run complex roof and attic simulations. This tool employs the latest web technologies and usability design to provide an easy input interface to an annual simulation of hour-by-hour, whole-building performance using the world-class simulation tools DOE-2.1E and AtticSim. Building defaults were assigned based on national averages and can provide estimated annual energy and cost savings after the user selects nothing more than building location. In addition to cool reflective roofs, the RSC tool can simulate multiple roof and attic configurations including different roof slopes, above sheathing ventilation, radiant barriers, low-emittance surfaces, HVAC duct location, duct leakage rates, multiple layers of building materials, ceiling and deck insulation levels, and other parameters. A base case and energy-efficient alternative can be compared side-by-side to generate an energy/cost savings estimate between two buildings. The RSC tool was benchmarked against field data for demonstration homes in Ft. Irwin, CA. However, RSC gives different energy savings estimates than previous cool roof simulation tools so more thorough software and empirical validation proved necessary. This report consolidates much of the preliminary analysis for comparison of RSC s projected energy savings to that from other simulation engines.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Specification of Fenix MPI Fault Tolerance library version 1.0.1

This document provides a specification of Fenix, a software library compatible with the Message Passing Interface (MPI) to support fault recovery without application shutdown. The library consists of two modules. The first, termed process recovery , restores an application to a consistent state after it has suffered a loss of one or more MPI processes (ranks). The second specifies functions the user can invoke to store application data in Fenix managed redundant storage, and to retrieve it from that storage after process recovery.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗