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All sources NASA NTRS DOE OSTI

NASA NTRS2012

A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures

A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.

Bednarcyk, Brett A.↗

DOE OSTI2015

WE-G-BRA-02: SafetyNet: Automating Radiotherapy QA with An Event Driven Framework

Purpose: Quality assurance is an essential task in radiotherapy that often requires many manual tasks. We investigate the use of an event driven framework in conjunction with software agents to automate QA and eliminate wait times. Methods: An in house developed subscription-publication service, EventNet, was added to the Aria OIS to be a message broker for critical events occurring in the OIS and software agents. Software agents operate without user intervention and perform critical QA steps. The results of the QA are documented and the resulting event is generated and passed back to EventNet. Users can subscribe to those events and receive messages based on custom filters designed to send passing or failing results to physicists or dosimetrists. Agents were developed to expedite the following QA tasks: Plan Revision, Plan 2nd Check, SRS Winston-Lutz isocenter, Treatment History Audit, Treatment Machine Configuration. Results: Plan approval in the Aria OIS was used as the event trigger for plan revision QA and Plan 2nd check agents. The agents pulled the plan data, executed the prescribed QA, stored the results and updated EventNet for publication. The Winston Lutz agent reduced QA time from 20 minutes to 4 minutes and provided a more accurate quantitative estimate of radiation isocenter. The Treatment Machine Configuration agent automatically reports any changes to the Treatment machine or HDR unit configuration. The agents are reliable, act immediately, and execute each task identically every time. Conclusion: An event driven framework has inverted the data chase in our radiotherapy QA process. Rather than have dosimetrists and physicists push data to QA software and pull results back into the OIS, the software agents perform these steps immediately upon receiving the sentinel events from EventNet. Mr Keranen is an employee of Varian Medical Systems. Dr. Moran’s institution receives research support for her effort for a linear accelerator QA project from Varian Medical Systems. Other quality projects involving her effort are funded by Blue Cross Blue Shield of Michigan, Breast Cancer Research Foundation, and the NIH.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗

NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗

NASA NTRS2018

Internship in Augmented and Virtual Reality - Rapid Model Import Tool

The integration of virtual and augmented reality, sometimes called mixed reality, is an emerging technology which will likely skyrocket overnight much in the way smartphones did a decade ago. Kennedy Space Center's Augmented and Virtual Reality (AVR) Lab is developing a Rapid Model Import Tool (RMIT) to create a quick and efficient way to bring NASA's complex engineering 3D models into virtual and augmented environments. The long-term objective is to create a tool that will ultimately benefit KSC engineers. Its various uses within NASA can potentially span from astronaut training, to marketing, to public outreach, to name a few. Unity is a prolific cross-platform game engine that allows users to build high quality 2D and 3D games for desktop, mobile, web, and game console platforms. It is perhaps also the most widely used software for virtual reality game development. At the AVR lab, we are looking at alternative uses of Unity to build tools for NASA engineers to perform design, development, testing, and training on spacecraft, rocket delivery systems, ground support equipment, and facilities at KSC. As an intern for the RMIT project, I am charged with the task of performing research on Unity-compatible file types to develop an efficient, affordable, preservative process to bring models from CATIA 3D engineering software into the Unity environment. With a tool called the NASA Enterprise Visualization Application (NEVA), developed by the Boeing Design Visualization group at KSC, we are able to easily convert CATIA's design models to. DAE (also known as COLLADA) and .OBJ file formats. I first reduce the polygon count of the model within CATIA itself, make any necessary tweaks to reduce the model further, and then export using NEVA. The .OBJ or. DAE files that I am left with are then converted by another intern to a Unity-compatible file format using a custom Python script. I have generated extensive documentation of this process in a NEVA User Guide. By the end of this semester, we will have built a solid framework for RMIT based on a thorough understanding of virtual reality specifications and file requirements, allowing future software development teams to go forward with development on the custom tool.

Leap Motion↗

DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRA's of nuclear power plants (NPPs), and, (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware , development of a philosophical basis for defining software failure , and identification of desirable attributes of quantitative software reliability methods . Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. It is widely recognized that software failures are due to the triggering of pre-existing defects by the software's operational environment. Software defects can arise from errors made in user requirements or coding errors introduced during the developmental process. The software's operational environment includes factors such as the time history of digital system inputs, communication interfaces, the internal state of the digital system, and external conditions. Thus, software reliability is a function of both the number of pre-existing defects and the presence of a triggering condition caused by the manner in which the software is used. In this paper, we describe a statistical software-testing approach for quantifying software reliability and applied it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The work involved collaboration between BNL and INL. The objectives of the study include: (1) Development of a statistical testing approach for estimating software failure probability on demand, the results of which are suitable for including in a probabilistic risk assessment (PRA); and, (2) Application of the approach to the LOCS to estimate its failure probability, and obtain insights into the feasibility, practicality, and usefulness of the estimation in models of digital systems for inclusion in nuclear power plants' PRAs. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2002

A Facility and Architecture for Autonomy Research

Autonomy is a key enabling factor in the advancement of the remote robotic exploration. There is currently a large gap between autonomy software at the research level and software that is ready for insertion into near-term space missions. The Mission Simulation Facility (MST) will bridge this gap by providing a simulation framework and suite of simulation tools to support research in autonomy for remote exploration. This system will allow developers of autonomy software to test their models in a high-fidelity simulation and evaluate their system's performance against a set of integrated, standardized simulations. The Mission Simulation ToolKit (MST) uses a distributed architecture with a communication layer that is built on top of the standardized High Level Architecture (HLA). This architecture enables the use of existing high fidelity models, allows mixing simulation components from various computing platforms and enforces the use of a standardized high-level interface among components. The components needed to achieve a realistic simulation can be grouped into four categories: environment generation (terrain, environmental features), robotic platform behavior (robot dynamics), instrument models (camera/spectrometer/etc.), and data analysis. The MST will provide basic components in these areas but allows users to plug-in easily any refined model by means of a communication protocol. Finally, a description file defines the robot and environment parameters for easy configuration and ensures that all the simulation models share the same information.

Pisanich, Greg↗

DOE OSTI2021

LLNL Response to the DOE ASCR RFI, "Stewardship of Software for Scientific and High-Performance Computing"

For decades, Lawrence Livermore National Laboratory (LLNL) has been engaged in significant research, development, and support for software to enable scientific computing and, particularly, the use of high performance computing (HPC) in the NNSA mission space. In particular, the move in the mid-1990’s to simulation as a leading component of stockpile stewardship through the ASCI and the successor ASC programs, as well as the need for reliable data acquisition and control software for the National Ignition Facility, have been important drivers in building expertise in production-quality software development at LLNL. LLNL has also been a leader in the DOE SciDAC FASTMath Institute and the DOE Exascale Computing Project (ECP), both of which have striven to make scientific computing software – in particular, the enabling technologies underpinning simulation capabilities – more widely adopted and sustainable. As such, we believe that our experience can inform the broader goal of software stewardship for scientific and high-performance computing. LLNL strongly supports the formation of a new DOE ASCR program element in software stewardship and sustainment. Historically, DOE ASCR has funded applied mathematics and computer science research that has led to the development of important new capabilities and algorithms that are expressed as artifacts in research software. Such frameworks, libraries, and tools have seldom been directly funded to address the important issues of code maintenance, documentation, robustness, and community building. Software engineering and support have typically been done on the side in support of the ASCR-driven research products. DOE funding priorities have been slow to recognize that good software engineering, the kind that ensures research investments have more adoption and longevity, requires significant resources. Based upon our experiences, we have prepared this response to highlight the concerns and issues we believe to be important as DOE ASCR considers its role in scientific software stewardship. We believe that role is important and will require a significant investment of new funding to legitimately support the technologies past and future DOE ASCR investments have and will produce to facilitate their uptake and adoption in the broader scientific computing community. Following a summary of our involvement in scientific software development, the remainder our response is organized around the nine topics specifically identified in the RFI.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Integrate FARM with PID controllers: IES Simulation Ecosystem Control System Development

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM was designed to support the HERON software module in the solution of the optimal dispatch problem for IES units. As the result of HERON-FARM dispatch simulation, the set-point trajectories are optimized to meet constraints on both the production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and the process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.) at a coarse time resolution (every 10 or 100 seconds) over long time horizons (several days or weeks). In case the operational constraints need to be met at finer time resolution, the computational burden of HERON-FARM would linearly increase with the sampling rate, and sub-optimal solutions might be obtained. System responses characterized by overshoots and damped oscillations temporarily violating the imposed constraints might occur during abrupt power transients. In this report, a hierarchical control system architecture for the operation of the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility constructed at INL was proposed. First, the preliminary studies on the proposed control strategy for operating the facility and the designed PI controllers were reviewed. In particular, the current approach for generating the set-point trajectories was studied, and its limits were identified. To this aim, the inclusion of a Supervisory Control layer embedding a modified version of the FARM algorithm for preserving the system safe operation over both long and real-time horizons was proposed. In this way, FARM would be applied twice, i.e., the original version (“FARM Validator”) aiding the solution of the power dispatch problem, and the modified version (“FARM Supervisory” coordinating the PI controllers to address the real-time control tasks. Despite the kernel of the two modules is the same algorithm, their roles, tasks, and capabilities are quite different. A detailed description of the role of FARM at addressing low-level control tasks is provided, along with tentative operational procedures for training the models embedded into the algorithm by using the collected experimental data.

42 ENGINEERING↗

DOE OSTI2013

AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Pronghorn: A Multidimensional Coarse Mesh Application for Advanced Reactor Thermal-Hydraulics

This paper presents an overview of Pronghorn, a multiscale thermal-hydraulic (T/H) application developed by Idaho National Laboratory and the University of California, Berkeley. Pronghorn, built on the open-source finite element Multiphysics Object-Oriented Simulation Environment (MOOSE), leverages state-of-the-art physical models, numerical methods, and nonlinear solvers to deliver fast-running advanced reactor T/H simulation capabilities within a modern software engineering environment. This work summarizes the physical models, multiphysics and multiscale coupling, and numerical discretization in Pronghorn with emphasis on our initial target application to pebble bed reactors (PBRs). A diverse set of applications are shown to depressurized natural circulation in the SANA experiments, forced convection in the Pebble Bed Modular Reactor, three-dimensional (3-D)/one-dimensional coupling of Pronghorn and RELAP-7 systems T/H for loop analysis in the High Temperature Reactor Power Module, and forced convection in the Mark-1 Pebble Bed Fluoride-Salt-Cooled High-Temperature Reactor. A multiphysics coupling of Pronghorn, RELAP-7, and Griffin deterministic neutronics for a gas-cooled PBR demonstrates the capability of the MOOSE framework for reactor design calculations. These applications highlight the verification and validation underlying Pronghorn’s software development while emphasizing features that improve upon capabilities offered by legacy tools in areas such as 3-D unstructured meshing, physics modeling, and multiphysics coupling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

WindWatts Computational Framework and Web UI [SWR-20-100]

This software provides a collection of algorithms, an API, and a functional Web UI for the Distributed Wind’s WindWatts project. DW WindWatts is a DOE WETO-funded project aimed at the development of tools to supporting the distributed wind industry, particularly with respect to siting and resource assessment. The computational framework and the back-end API are powering the easy to use front-end services available at: https://windwatts.nrel.gov. https://github.com/NREL/dw-tap-api; https://github.com/NREL/dw-tap; https://github.com/NREL/windwatts-data For reference, this software was previously known as DW TAP Computational Framework.

Phillips, Caleb↗

DOE OSTI2024

Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PHILMS) (Technical Report)

The research work at University of California Santa Barbara (UCSB) resulted in several new developments in the areas of scientific machine learning, numerical analysis, and practical methods for data-driven modeling, prediction, reductions, and simulation. Many of the projects were carried out in collaboration with members of the national laboratories at Sandia National Laboratories (SNL), Pacific Northwestern National Laboratories (PNNL), and other institutions. Results included developing new scientific machine learning methods, related theory and mathematical frameworks for analysis and training, data-driven numerical solvers, and related tools and software for scientific computation. During the support period, over 16+ papers were submitted for publication, and 4 open-source software packages were developed and released (available at http://atzberger.org/). In addition, 7+ students and 2 post-docs were mentored in collaboration with the laboratory staff for future careers in academia, government labs, and industry.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Quantum Networks for Resilient Power Grids: Theory and Simulated Evaluation

Quantum networks are considered the future of secure communication in the coming quantum era. Yet there lack significant efforts on developing practical quantum networks for power grids. Here, in this paper, we establish a quantum network-based power grid (QNetGrid) framework and develops a real-time, reliable, flexible, programmable, and cost-effective QNetGrid software testbed containing repeater-based quantum communication, quantum routing, real software-defined networking (SDN) switches, and real-time networked microgrids (NMs) operations. It makes the following contributions: 1) a repeater and routing based quantum network simulator (QNSim) is developed, 2) repeaters with and without quantum memories are respectively simulated in QNSim, 3) different routing scenarios in QNetGrid are investigated, 4) a real-time QNetGrid software testbed is built in RTDS incorporating QNSim and real SDN switches, and 5) various test cases are designed, and experimental results produced with the QNetGrid testbed provide valuable insights for building quantum networks in power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Real Vector Framework

SAND2025-11463O The Real Vector Framework (RVF) is a modern and flexible C++ vector math library for developing scientific computing software that involves vector computations. RVF allows an opt-in approach to functionality that parallels the familiar base-class and override structures of object-oriented programming. Users can reuse and customize the code without inheritance entanglements and dynamic dispatch, while enabling seamless interoperability between diverse container types. 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.

von Winckel, Gregory [Sandia National Lab. (SNL-CA↗

NASA NTRS2006

Utilizing a Common Model Architecture for the Simulation of On-Orbit Human Spaceflight Operations

Advances in computing and simulation technology during the 1980 s promoted the development of multiple simulations for on-orbit human spaceflight operations at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). By the late 1980 s, it became increasingly clear that redundancy risked leading to inefficiencies in dissemination and incorporation of models across simulations, institutional organizations, and spaceflight programs. Software development tools, such as the Trick Simulation Environment, began to emerge providing a generalized framework for the development, integration, and operation of simulations. Throughout the 1990s, engineering, operations, and training simulations began to migrate to Trick in order to take advantage of a common simulation environment. Due to forecasted reductions in funding to the Shuttle and International Space Station (ISS) programs in the early 2000s, the common framework that arose from this simulation migration was formalized as the Common Model Architecture (CMA). The CMA was established in order to further reduce redundancy, while promoting model sharing and enhancing model integrity. A minimal set of standards for data flows, functional interactions, and model organization based on Trick were defined for those features found to be most important in facilitating model interaction. Model-providing organizations within NASA JSC took ownership of models for their specific areas of expertise, and agreements were reached on common model sharing and usage. This paper describes the evolution of commonality enabled by the Trick Simulation Environment at JSC leading to the CMA, the standards defined by the CMA, a recent CMA simulation example, and potential application of the CMA to NASA s new Exploration program.

Leslie J Quiocho↗