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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

rNets: a standalone package to visualize reaction networks

In the study of chemical processes, visualizing reaction networks is pivotal for identifying crucial compounds and transformations. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity. Alternatives capable of leading with complexity include graph methods, but they are not user-friendly, lacking simplicity and modularity, which hinders their integration with widely-used research software. This work introduces rNets an innovative tool designed for the efficient visualization of reaction networks with a user-friendly interface, modularity, and seamless integration with existing software packages. The effectiveness of rNets is demonstrated through its application in analyzing three catalytic reactions, showcasing its potential to significantly enhance research both in homogeneous and heterogeneous catalysis fields. This tool not only simplifies the visualization process but also opens new avenues for exploring complex reaction networks in diverse research contexts.

Pablo-García, Sergio↗
DOE OSTI2026

DISARM: Target Electronic Device Informed Mitigation of Software Runtime Side-Channel Vulnerabilities

Program runtime/timing attacks exploit variations in a program’s execution times to extract sensitive information from the program (e.g. encryption keys, sensitive variable data, intellectual property). State-of-the-art solutions to runtime side-channel attacks attempt to balance the execution time of the sensitive code for different control flow paths to eliminate the timing leakage. However, during the mitigation process, most techniques do not consider the underlying hardware/device on which the target program is supposed to run on. This can lead to over-fixing (unnecessary extra operations), under-fixing (not solving the imbalance properly), and even failures. Here, we propose DISARM, a joint hardware-software methodology (unlike any existing solution) for mitigating runtime side-channel vulnerabilities that utilizes timing values from real embedded devices to generate targeted software fixes. We implement DISARM to support C/C++/Java source codes and validate it across 22 standard benchmarks. DISARM outperforms state-of-the-art solutions such as PENDULUM and DifFuzzaR in terms of execution time overhead, code size overhead, and correctness on five different embedded/edge devices.

Timing/runtime side-channel↗
DOE OSTI2020

Nonadiabatic Excited-State Molecular Dynamics for Open-Shell Systems

Nonadiabatic Molecular Dynamics (NAMD) of excited states has been widely used in the simulation of photoinduced phenomena. However, the inability to treat bond breaking and forming processes with single-reference electronic structure methods limits their application in photochemistry for extended molecular systems. In this work, the extension of excited-state NAMD for open-shell systems is developed and implemented in the NEXMD software. We present the spin-unrestricted CIS and TDSCF formalism for the ground and excited states, analytical derivatives, and nonadiabatic derivative couplings for the respective potential energy surfaces. Next, this methodology is employed to study the photochemical reaction of three model molecules. The results demonstrate the advantage of the open-shell approach in modeling photochemical reactions, especially involving bond breaking processes. We find that the open-shell method lowers the reaction barrier at the bond-breaking limits resulting in larger calculated photochemical quantum yields compared to the respective closed-shell results. We also address problems related to spin contamination in the open-shell method, especially when molecular geometries are far from equilibrium.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting Deposition Rate and Closing the Loop on Aerosol Jet Printing with In‐Line Light Scattering Measurements

Aerosol jet printing is a compelling noncontact, digital manufacturing technology for flexible, hybrid, and conformal electronics, but batch‐to‐batch variability and process drift inhibit systematic study and translation to production environments. Emerging measurement techniques for the aerosol volume fraction, a key parameter governing deposition rate, have shown promise for real‐time response, including a recent demonstration of human‐in‐the‐loop control. By integrating improved light scattering measurements with custom software, a robust environment for real‐time monitoring is established and fully automated closed‐loop control over the deposition rate during printing is enabled. The physical basis for scattering measurements as a feedback source is examined and the quantitative insight this provides for ink development and fundamental process studies is highlighted. Three candidate process variables for closed‐loop control, namely the atomizer voltage, carrier gas flow rate, and print speed, are then evaluated. The atomizer voltage demonstrates the best extended duration validity because it addresses atomization variability directly, but carrier gas flow rate and print speed provide faster and more deterministic responses. This methodology is then demonstrated for electrical properties using a silver nanoparticle ink, maintaining 98% of printed patterns within 10% of the mean resistance value over 3 h of printing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2020

A deep insight on the coal ash-to-slag transformation behavior during the entrained flow gasification process

Recent research provided deep insight on the coal ash-to-slag transformation characterization during the entrained flow gasification process, with experimentation on a 40 kg/hr (dry basis) coal-fed opposed multi-burner (OMB) entrained flow gasifier and simulation via FactSage™ software. A general mechanism is presented to relate the gasifier design temperature, ash fluid temperature, and operating temperature with the degree of the slag polymerization. The change of the high temperature zone, the corresponding particle residence time in the high temperature zone, and syngas composition have obvious effects on the slag mineral transformation behavior. Mineral types formed on the wall of the gasifier chamber were mainly anorthite (CaAl 2 Si 2 O 8 ), aluminum oxide (Al 2 O 3 ), and calcium sulfide (CaS). These minerals transformed to anorthite and diopside (CaMgSi 2 O 6 ) at the slag hole zone, while the minerals at the lock hopper were anorthite, orthoclase (KAlSi 3 O 8 ), quartz (SiO 2 ), gypsum (CaSO 4 ), calcite (CaCO 3 ), and halite (NaCl). FactSage™ predicted minerals as anorthite, diopside, orthoclase, and albite (NaAlSi 3 O 8 ), etc., where the slag temperature was below the ash fluid temperature and when the ratios of CO/CO 2 and (CO + H 2 )/CO 2 were lower than 1.0 and 2.0, respectively. By simulation, residual carbon was found to be the dominant factor over syngas composition to cause mineral transformation, and this was verified experimentally. The Ca-based crystals, typically anorthite, was shifted to diopside, near the slag hole zone, and a linear relationship was found between the content ratios of diopside/(anorthite + diopside), CaO/SiO 2 , and (CaO + MgO)/SiO 2 . A dimensionless number, θ, was defined to characterize the changing chemical composition and the degree of slag polymerization, with temperature deviation from the design condition. Three zones of θ were identified and related to the deviation between the actual gasification condition from the design condition. Finally, a low slag polymerization degree corresponded with a higher temperature deviation between the actual condition and design condition, and this proved that increased residual carbon content and changing iron valence state increased the mineral types when the slag temperature was below the ash fluid temperature.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage

Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Neutrino identification with scintillation light in MicroBooNE

MicroBooNE is a neutrino experiment which employs a liquid argon (LAr) time projection chamber (TPC) to record neutrino interactions from Fermilab's neutrino beamlines. The experiment's primary objective is to study low-energy νe interactions from the Booster Neutrino Beamline (BNB). Located on the surface, the detector is affected by a continuous rate of cosmic-rays. This leads to one neutrino interaction for every 104 cosmic rays observed in the TPC, making it difficult to isolate neutrino interactions in the detector using charge alone. MicroBooNE's trigger makes use of prompt scintillation light and plays an essential role in both performing strong background rejection and significantly reducing data-rates. Furthermore, a series of novel techniques relying on scintillation light are used to isolate beam-induced activity. This document briefly presents MicroBooNE's scintillation-light based trigger and novel Flash-Matching pattern recognition techniques for cosmic-rejection. This work serves as the foundation of neutrino analyses in LArTPC detectors and is therefore of interest to the broader short- and long-baseline neutrino programs being launched at Fermilab.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2023

The Distributed Grid Sensing Services (DGSS) platform: A scalable and decentralized sensor dissemination network

In this paper, a scalable, decentralized sensor-oriented, communication blueprint will be introduced. The proposed architecture has been specifically designed to support the mass deployment of sensors while enabling data consumers to access the underlying data streams using a distributed systems approach. With the proposed approach, sensors and data clients can be decoupled from the physical communication infrastructure and migrated into a modern, software-defined infrastructure ecosystem that can be configured to suit the end application’s demands. In its current iteration, the blueprint defines the interactions, processes, and expected outcomes that each individual component within the architecture must fulfill in order to support the overall ecosystem’s needs. Within this work, such assemble of services is referred the databus, and represents the heart of the Distributed Grid Sensing Services (DGSS) architecture, a multi-year project that aims to design, implement and test a dedicated sensor dissemination network that can support the needs of the extended grid state. It is our expectation, that the proposed blueprint presented in this paper will serve as a future guide to our implementation efforts

Sebastian Cardenas, David J.↗
DOE OSTI2022

Grid Utility Asset Vulnerability Assessment (GUAVA) Software Tool

Increasing demand and changes in generation portfolios is pushing power grid to operate towards the limit. However, due to lack of analytical tools for understanding various scales of impact on grid, it is becoming more vulnerable to wide scale power outages and blackouts. A vulnerable grid operating at its limit can be easily disrupted by asset failures caused by devastating hurricanes which has been known to damage transmission and distribution lines along its track. In this direction, researchers have focused on determining these assets by conducting Monte Carlo simulations of hurricanes with uncertainties and collected a large set of simulation data. To determine the infrastructure updates necessary for mitigating wide scale impact of hurricanes on the grid, we propose a software tool named “Grid Utility Asset Vulnerability Analysis” (GUAVA) framework. GUAVA presents a novel data-driven probabilistic analytical approach to (1) post-process hurricane failure scenarios, (2) identify/rank assets that are most vulnerable and critical to failing and are associated with highest impact/risk, and (3) to inform system upgrade decisions & prioritization. Based on the observed results and employed data-driven methodology, it is expected GUAVA can be adapted to provide power system planners with a recommendation engine for making informed decisions to improve resilience of grid.

Mahapatra, Kaveri↗
DOE OSTI2024

Reliable edge machine learning hardware for scientific applications

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.

Baldi, Tommaso↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
DOE OSTI2024

Control System for RF OPM

SAND2025-01910O This software built in LabVIEW FPGA provides experimental control of the RF OPM prototype via a reconfigurable NI I/O card. It samples the analog input, processes it on the FPGA in real time, and generates control and feedback outputs to control and optimize the operation of the device. 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.

Bainbridge, Jonathan↗
DOE OSTI2024

scANN

SAND2025-00656O scANN, also known as sampling by coinflips artificial neural networks, is a software tool that estimates uncertainty in artificial intelligence by performing Monte Carlo analysis on the weight matrices of feedforward neural networks. This computationally intensive process aims to explore the potential value added by future probabilistic hardware. The program’s output helps researchers gain insights into how probabilistic neural networks work. 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.

SciDAC↗
DOE OSTI2022

Status of the NEAMS and ARC fast reactor tools integration to the NEAMS Workbench

The Workbench initiative was launched in FY-2017 within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to facilitate the transition from conventional tools to high-fidelity tools. The NEAMS Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes. The integration of the Argonne Reactor Computation (ARC) suite of codes into the NEAMS Workbench through the PyARC module was initiated in FY-2017. The ARC codes, which focus on fast reactor multiphysics analysis, contain both legacy codes like DIF3D and REBUS-3 that were developed with over 30 years of experience, and newer NEAMS additions like MC 2 -3, PERSENT, and PROTEUS. Recent work expended this integration to other tools to support the U.S. fast reactor community such as DASSH for sub-channel thermal-hydraulics, Griffin for high-fidelity deterministic transport calculations, and OpenMC for Monte Carlo simulations (with Shift integration planned for FY-2023). The integration of the “extended ARC” suite of codes into the NEAMS Workbench interface relies on the PyARC and PyGriffin modules to handles the pre- and post-processing of these codes input, and the runtime environment. The PyARC module together with the NEAMS Workbench interface are both released under Open Source Software licenses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

CTF Theory Manual: Version 4.4

Coolant-Boiling in Rod Arrays – Two Fluids (COBRA-TF) is a thermal/hydraulic (T/H) simulation code designed for light-water reactor (LWR) vessel analysis. It uses a two-fluid, three-field (i.e., fluid film, fluid drops, and vapor) modeling approach. Both subchannel and 3D Cartesian forms of its governing equations are available for the solution. The code was originally developed by Pacific Northwest Laboratory in 1980 and has been used and modified by several institutions over the last few decades. COBRA-TF also found use at the Pennsylvania State University (PSU) by the Reactor Dynamics and Fuel Modeling Group (RDFMG) and has been improved, updated, and subsequently rebranded as CTF. CTF was later adopted in the early 2010s by Oak Ridge National Laboratory (ORNL) for use in the Consortium for Advanced Simulation of Light Water Reactors (CASL) program, which led to a significant advancement of the code’s software quality, modeling accuracy, testing systems, and capabilities for improved support in modeling common LWR nominal and transient behavior. As part of the improvement process, it was necessary to generate sufficient documentation for the public domain code which had lacked such material upon being adopted by RDFMG. This document serves as a theory manual for CTF, detailing the many two-phase heat transfer, drag, and important accident scenario models contained in the code, as well as the numerical solution process utilized. Additional documents available in the CTF documentation suite include the user manual and the verification and validation manual.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗