Results for “Software Framework” — Page 78

DOE OSTI2026

High-Voltage DC MULTI-terminal SIMulation (HVDC MULTISIM): Technical Program Summary

Expansion of the power grid in the USA is essential to fulfilling the rising energy demand of the country. In particular, the transmission grid, a network of electrical energy corridors that enable the flow of large amounts of energy from the point of generation to the point of consumption, needs significant expansion to cater to this growth of electrification. The current infrastructure is also dated, and any new transmission corridor should be based on a vision of building a modern infrastructure that is futureproof. High voltage DC (HVDC) transmission technology falls in this category: it is the most economical way to build large transmission lines and relies on sophisticated electronics and flexible controls, rather than just passive components like switchgear and transformers. This project is aimed at establishing the technical and economic feasibility of a network of HVDC lines that are interconnected to form a multi-terminal DC network. HVDC transmission is not a new technology and United States has a number of such lines; however, these are point to point transmissions and do not form a DC grid. Modeling and analyzing DC grids is challenging since there are no established modeling tools and the traditional ways of modeling AC grids fall short of providing the fidelity required the fast dynamics of the DC grid. This project aims to build a software-in-the-loop simulator for a multi-terminal HVDC grid, called MULTISIM and, establish new control and protection algorithms to operate such a connected DC system. NLR, a key partner, established the core framework of the MULTISIM simulator and validated its functionality. The GE Vernova team, created a full-scale model of a four-terminal HVDC network using PSCAD (Power Systems Computer Aided Design) software and established the baseline performance of the system during normal and fault operation. The project was started on 10/12024 with a kick-off meeting held on 12/11/2024 and ran through two quarters till termination. All the tasks, milestones and deliverables during this period were met. Several interim reports detailing the various tasks were submitted. The following sections provide details on the completed tasks till the project pause and termination after Q2.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’

The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards open data and open-source tools to propel the field. Despite the increasing usefulness and capabilities of these tools, developers neglecting to follow reproducible practices presents a significant barrier for other researchers looking to use or build upon their work. In this study, we investigate the challenges encountered while attempting to reproduce a section of the results presented in “A general-purpose machine learning framework for predicting properties of inorganic materials.” Our analysis identifies four major categories of challenges: (1) reporting software dependencies, (2) recording and sharing version logs, (3) sequential code organization, and (4) clarifying code references within the manuscript. The result is a proposed set of tangible action items for those aiming to make material informatics tools accessible to, and useful for the community.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Streaming Readout of the CLAS12 Forward Tagger Using TriDAS and JANA2

An effort is underway to develop streaming readout data acquisition system for the CLAS12 detector in Jefferson Lab’s experimental Hall-B. Successful beam tests were performed in the spring and summer of 2020 using a 10GeV electron beam from Jefferson Lab’s CEBAF accelerator. The prototype system combined elements of the TriDAS and CODA data acquisition systems with the JANA2 analysis/reconstruction framework. This successfully merged components that included an FPGA stream source, a distributed hit processing system, and software plugins that allowed offline analysis written in C++ to be used for online event filtering. Details of the system design and performance are presented.

Ameli, Fabrizio↗

DOE OSTI2023

Progress toward Accelogic compression in ROOT

For the last 7 years, Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom-line outcome will be to provide state-of-the-art compression algorithms --and accompanying software libraries-- able to surpass the performance of the compression engines currently available in the ROOT [7] framework. The resulting technology has the capability to enable substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4 [6].As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.In this contribution, we will present our efforts integrating our concepts of “functionally lossless compression” within the ROOT framework implementation, with the purpose of producing a basic solution readily integrated into HENP applications. We will also present our progress applying this compression through realistic examples of analysis from both the STAR and CMS experiments.

Canal, Ph.↗

DOE OSTI2017

SharP Hash: A High-Performing Distributed Hash for Extreme-Scale Systems

A high-performing distributed hash is critical for achieving performance in many applications and system software using extreme-scale systems. It is also a central part of many Big-Data frameworks including Memcached, file systems, and job schedulers. However, there is a lack of high-performing distributed hash implementations. In this work, we propose, design, and implement, SharP Hash, a high-performing, RDMA-based distributed hash for extreme-scale systems. SharP Hash's high performance is obtained through the use of high-performing networks and one-sided semantics. We perform an evaluation of SharP Hash and demonstrate its performance characteristics with a synthetic micro-benchmark and implementation of a Key Value (KV) store, Memcached.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fault Injection for TensorFlow Applications

As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilience (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the application’s resilience. In this work, we present TensorFI 1 and TensorFI 2, high-level fault injection (FI) frameworks for TensorFlow-based applications. TensorFI 1 and 2 are able to inject both hardware and software faults in any general TensorFlow 1 and 2 program respectively. Both are configurable FI tools that are flexible, easy to use, and portable. They can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., bit-flips in particular operations or layers). We use the TensorFI 1 and TensorFI 2 to evaluate the resilience of 12 and 10 ML programs written in TensorFlow, including DNNs used in the autonomous vehicle domain. The results give us insights into why some of the models are more resilient. We also measure the performance overheads of the two injectors, and present 4 case studies, two for each tool, to demonstrate their utility.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

TH-D-BRB-04: Pinnacle Scripting: Improving Efficiency While Maintaining Safety

Scripting capabilities and application programming interfaces (APIs) are becoming commonly available in modern treatment planning systems. These links to the treatment planning system (TPS) allow users to read data from the TPS, and in some cases use TPS functionality and write data back to the TPS. Such tools are powerful extensions, allowing automation of routine clinical tasks and supporting research, particularly research involving repetitive tasks on large patient populations. The data and functionality exposed by scripting/API capabilities is vendor dependent, as are the languages used by script/API engines, such as the Microsoft .NET framework or Python. Scripts deployed in a clinical environment must be commissioned and validated like any other software tool. This session will provide an overview of scripting applications and a discussion of best practices, followed by a practical introduction to the scripting capabilities of three commercial treatment planning systems. Learning Objectives: Understand the scripting capabilities available in several treatment planning systems Learn how to get started using scripting capabilities Understand the best practices for safe script deployment in a clinical environment R. Popple, Varian Medical Systems has provided research support unrelated to the topic of this session.R. Cardan, Varian Medical Systems for grant research, product evaluation, and teaching honorarium.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.2)

We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers’ understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In this version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different effciency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-effcient manner despite frequent faults, errors, and failures of various types.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Status Report on IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENviroment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report provides discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity is maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In addition to the investigative work being conducted on FMUs and FMIs, a series of updates to the hybrid repository has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and opensourcing the HYBRID repository with an integrated regression system.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

Developing Fugitive Emissions Sensor Networks

This document summarizes research performed under the Laboratory Directed Research and Development (LDRD) project titled Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring, Measurement and Verification. The purpose of this project is to develop methods and software to enhance detection programs through optimal design of the sensor network. This project includes both software development and field work. While this project is focused on methane emissions, the sensor placement optimization framework can be applied to a wide range of applications, including the placement of water quality sensors, surveillance cameras, fire and chemical detectors. This research has the potential to improve national security by improving the way sensors are deployed in the field.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

14 SOLAR ENERGY↗

DOE OSTI2021

Status Report on the INL IES Plug-and-Play Framework

This report discusses the advancements and status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and usage, which aims to ease the sharing and simulation of complex dynamic models. This report discusses the FMI/FMU development advancements, overall focusing on the deployment of methodologies for RAVEN to export and use FMI/FMU of both Python-based models and advanced AI-constructed algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Complete Demonstration of a Prototype Version of FORCE User Interface and Conduct Analyst Survey Collecting Feedback on Interface Features and Usability

In 2024 the US Department of Energy (DOE) Office of Nuclear Energy (NE) Integrated Energy System (IES) program continued to develop the Framework for Optimization of Resources and Economics (FORCE) analysis ecosystem into a more traditional toolset with simplified software installation, automated workflows, and interactive results visualization. The DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench continued to be leveraged for user input, application workflow and runtime environment, and interactive results visualization capabilities. This report documents the demonstration of a FORCE User Interface (UI) prototype and the results of a survey of analysts’ using the Holistic Energy Resource Optimization Network (HERON) tool in FORCE with the prototype UI.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Software Quality Assurance Plan: Cardinal

The Cardinal Software Quality Assurance (SQA) Program aims to provide the controls and processes necessary to enable continuous, high-quality software development while meeting user and program sponsor requirements. This SQA Plan (SQAP) delineates the SQA Program framework for Cardinal by describing the Program activities, organization, and documentation, and by clearly defining the interconnection of all Program items. It should be noted that this SQAP is aligned with the current version of the Argonne Quality Assurance Program Plan, which was designed to align with DOE O 414.1D. This SQAP is also aligned with the revision 10 of the SQAP for MOOSE and MOOSE-based applications.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

CryoTran user's manual, version 1.0

The development of cryogenic fluid management systems for space operation is a major portion of the efforts of the Cryogenic Fluids Technology Office (CFTO) at the NASA Lewis Research Center. Analytical models are a necessary part of experimental programs which are used to verify the results of experiments and are also used as a predictor for parametric studies. The CryoTran computer program is a bridge to obtain analytical results. The object of CryoTran is to coordinate these separate analyses into an integrated framework with a user-friendly interface and a common cryogenic property database. CryoTran is an integrated software system designed to help solve a diverse set of problems involving cryogenic fluid storage and transfer in both ground and low-g environments.

Cowgill, Glenn R.↗

NASA NTRS1991

The AI Bus architecture for distributed knowledge-based systems

The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.

Schultz, Roger D.↗

NASA NTRS1996

Theory and ontology for sharing temporal knowledge

Using current technology, the sharing or re-using of knowledge-bases is very difficult, if not impossible. ARPA has correctly recognized the problem and funded a knowledge sharing initiative. One of the outcomes of this project is a formal language called Knowledge Interchange Format (KIF) for representing knowledge that could be translated into other languages. Capturing and representing design knowledge and reasoning with them have become very important for NASA who is a pioneer of innovative design of unique products. For upgrading an existing design for changing technology, needs, or requirements, it is essential to understand the design rationale, design choices, options and other relevant information associated with the design. Capturing such information and presenting them in the appropriate form are part of the ongoing Design Knowledge Capture project of NASA. The behavior of an object and various other aspects related to time are captured by the appropriate temporal knowledge. The captured design knowledge will be represented in such a way that various groups of NASA who are interested in various aspects of the design cycle should be able to access and use the design knowledge effectively. To facilitate knowledge sharing among these groups, one has to develop a very well defined ontology. Ontology is a specification of conceptualization. In the literature several specific domains were studied and some well defined ontologies were developed for such domains. However, very little, or no work has been done in the area of representing temporal knowledge to facilitate sharing. During the ASEE summer program, I have investigated several temporal models and have proposed a theory for time that is flexible to accommodate the time elements, such as, points and intervals, and is capable of handling the qualitative and quantitative temporal constraints. I have also proposed a primitive temporal ontology using which other relevant temporal ontologies can be built. I have investigated various issues of sharing knowledge and have proposed a formal framework for modeling the concept of knowledge sharing. This work may be implemented and tested in the software environment supplied by Knowledge Based System, Inc.

Loganantharaj, Rasiah↗