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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spiking Markov Reward Process v.0.1

SAND2024-11150O The Spiking Markov Reward Process software is a spiking neural network that streams binary arithmetic and computes the state value function of a Markov reward process. The software will be released to the SpiNNcloud group for development of neuromorphic acceleration. 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.

Wang, Felix↗
DOE OSTI2025

Hydrogen Diffusion Through Stainless Steel

SAND2025-14430O Hydrogen Diffusion Through Stainless Steel is a tool that employs a finite difference method algorithm to solve Fick's law and other mass transport models pertinent to the diffusion of hydrogen through stainless steel. Additionally, the software uses particle swarm optimization to determine physical parameters based on experimental data. 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.

Mason, Tyler [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2026

EFBMC

SAND2026-23986O EFBMC performs elastic Bayesian model calibration by applying Bayesian statistics and functional analysis. The software provides Python, R, and MATLAB scripts that enable users to calibrate models. 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.

Tucker, J. Derek [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2016

Monitoring and Control of Chemical Composition of InGaN Layers During MOCVD

This Department of Energy SBIR Phase II grant targets the inefficiency of the epitaxial processes used to deposit wide band-gap semiconductor thin films that are an essential part of the next generation high-brightness LEDs for solid state lighting. Non-uniformity of the layers over a single wafer and non-repeatability over the wafers in a single batch, separate batches and separate reactors are some of the primary source of reduced manufacturing yield. The team performed extensive research, development and validation of an in-situ spectroscopic optical monitoring and epitaxial process control system. The system monitors the LED thin films as they grow on the individual wafers inside the epitaxial reactor, records the signal in a broad spectral range and processes the information to retrieve essential film quality parameters and their variation over a single wafer, all the wafers in a batch and multiple batches. A novel software algorithm is implemented, which performs real-time spectral pattern recognition and displays any abnormalities or differences in the deposition, which is used by the engineer to optimize the epitaxial process. The project resulted in the optimization of the LED film growth and improvement of its uniformity and repeatability, reducing the cost of the final LED devices.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Development of a diversity and defense-in-depth strategy for the TerraPower TWR-P Advanced nuclear power plant - 380

A Diversity and Defense in Depth (D3) analysis was performed for the Traveling Wave Reactor, which is a Generation IV fast reactor design. The analysis demonstrated that the current I and C design for TWR-P provides sufficient diversity in the reactor shutdown system, the engineered safety features, and the post-accident monitoring system. In addition, an analysis was performed for the Embedded Digital Devices (EDD) to ensure a postulated Software Common Cause Failure (SWCCF) will not affect the safety functions of the TWR-P I and C system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

FastACE

SAND2024-01893O The Fast Adaptive Cosine Estimator (FastACE) algorithm modifies the well-known Adaptive Cosine Estimator for target detection in hyperspectral imagery. Specifically, FastACE modifies the computation of the background precision matrix (C_b^-1) under a Vecchia approximation. That is, each spectral band, conditioned on a local neighborhood of bands around that band, is independent of the other spectral bands. The FastACE algorithm leverages a parameterizable, auto-regressive neighborhood for the conditional independence assumption. The underlying math used for computing detection scores is equivalent to ACE, but it is implemented within FastACE. The software implements a target detection algorithm and associated utilities for target detection in hyperspectral imagery. Provided with a hyperspectral image and corresponding target signature, it produces relevant background statistics and the corresponding detection scores for the target signature in the image. The software is designed to integrate into other end-user applications or processing. 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.

VanderLaan, John↗
DOE OSTI2021

Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) (Technical Reference Manual V.4)

The HyRAM+ software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen, methane, and propane infrastructure and transportation systems. HyRAM+ is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of safety, hazards, and risk. HyRAM+ includes generic probabilities for equipment failures, probabilistic models for the impact of heat flux on humans and structures, and experimentally validated first-order models of release and flame physics. HyRAM+ integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hazards (thermal effects from jet fires, overpressure effects from delayed ignition), and assessing impact on people and structures. HyRAM+ is developed at Sandia National Laboratories to support the development and revision of national and international codes and standards. HyRAM+ is a research software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals. This document provides a description of the methodology and models contained in HyRAM+ version 4.0. The most significant change for HyRAM+ version 4.0 from HyRAM version 3.1 is the incorporation of other alternative fuels, namely methane (as a proxy for natural gas) and propane into the toolkit. This change necessitated significant changes to the installable graphical user interface as well as changes to the back-end Python models. A second major change is the inclusion of physics models for the overpressure associated with the delayed ignition of an unconfined jet/plume of flammable gas.

02 PETROLEUM↗
DOE OSTI2016

Predictive Engineering Tools for Injection-Molded Long-Carbon-Fiber Thermoplastic Composites. Topical Report

This project aimed to integrate, optimize, and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk® Simulation Moldflow® Insight (ASMI) software package for injection-molded long-carbon-fiber (LCF) thermoplastic composite structures. The project was organized into two phases. Phase 1 demonstrated the ability of the advanced ASMI package to predict fiber orientation and length distributions in LCF/polypropylene (PP) and LCF/polyamide-6, 6 (PA66) plaques within 15% of experimental results. Phase 2 validated the advanced ASMI package by predicting fiber orientation and length distributions within 15% of experimental results for a complex three-dimensional (3D) Toyota automotive part injection-molded from LCF/PP and LCF/PA66 materials. Work under Phase 2 also included estimate of weight savings and cost impacts for a vehicle system using ASMI and structural analyses of the complex part. The present report summarizes the completion of Phases 1 and 2 work activities and accomplishments achieved by the team comprising Pacific Northwest National Laboratory (PNNL); Purdue University (Purdue); Virginia Polytechnic Institute and State University (Virginia Tech); Autodesk, Inc. (Autodesk); PlastiComp, Inc. (PlastiComp); Toyota Research Institute North America (Toyota); Magna Exteriors and Interiors Corp. (Magna); and University of Illinois. Figure 1 illustrates the technical approach adopted in this project that progressed from compounding LCF/PP and LCF/PA66 materials, to process model improvement and implementation, to molding and modeling LCF/PP and LCF/PA66 plaques. The lessons learned from the plaque study and the successful validation of improved process models for fiber orientation and length distributions for these plaques enabled the project to go to Phase 2 to mold, model, and optimize the 3D complex part.

42 ENGINEERING↗
DOE OSTI2017

Modeling variability in porescale multiphase flow experiments

Microfluidic devices and porescale numerical models are commonly used to study multiphase flow in biological, geological, and engineered porous materials. In this work, we perform a set of drainage and imbibition experiments in six identical microfluidic cells to study the reproducibility of multiphase flow experiments. We observe significant variations in the experimental results, which are smaller during the drainage stage and larger during the imbibition stage. We demonstrate that these variations are due to sub-porescale geometry differences in microcells (because of manufacturing defects) and variations in the boundary condition (i.e.,fluctuations in the injection rate inherent to syringe pumps). Computational simulations are conducted using commercial software STAR-CCM+, both with constant and randomly varying injection rate. Stochastic simulations are able to capture variability in the experiments associated with the varying pump injection rate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

System Modeling of Gas Engine Driven Heat Pump

To improve the system performance of the GHP, modeling and experimental study has been made by using desiccant system in cooling operation (particularly in high humidity operations) and suction line waste heat recovery to augment heating capacity and efficiency. The performance of overall GHP system has been simulated by using ORNL Modulating Heat Pump Design Software, which is used to predict steady-state heating and cooling performance of variable-speed vapor compression air-to-air heat pumps for a wide range of operational variables. The modeling includes: (1) GHP cycle without any performance improvements (suction liquid heat exchange and heat recovery) as a baseline (both in cooling and heating mode), (2) the GHP cycle in cooling mode with desiccant system regenerated by waste heat from engine incorporated, (3) GHP cycle in heating mode with heat recovery (recovered heat from engine). According to the system modeling results, by using desiccant system regenerated by waste heat from engine, the SHR can be lowered to 40%. The waste heat of the gas engine can boost the space heating efficiency by 25% in rated operating conditions.

Gas Driven Heat Pump↗
DOE OSTI2020

Hydrogen Risk Assessment Models (HyRAM) (Version 3.0 Technical Reference Manual)

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM includes generic probabilities for hydrogen equipment failures, probabilistic models for the impact of heat flux on humans and structures, and computationally and experimentally validated first-order models of hydrogen release and flame physics. HyRAM integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is developed at Sandia National Laboratories for the U.S. Department of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. HyRAM is a research software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals. This document provides a description of the methodology and models contained in the HyRAM version 3.0. HyRAM 3.0 includes the new ability to model cryogenic hydrogen releases from liquid hydrogen systems, using a different property calculation method and different equations of state. Other changes include modifications to the ignition probability calculations, component leak frequency calculations, and addition of default impulse data.

08 HYDROGEN↗
DOE OSTI2017

Control and Information Systems for the National Ignition Facility

Orchestration of every National Ignition Facility (NIF) shot cycle is managed by the Integrated Computer Control System (ICCS), which uses a scalable software architecture running code on more than 1950 front-end processors, embedded controllers, and supervisory servers. The ICCS operates laser and industrial control hardware containing 66 000 control and monitor points to ensure that all of NIF’s laser beams arrive at the target within 30 ps of each other and are aligned to a pointing accuracy of less than 50 μm root-mean-square, while ensuring that a host of diagnostic instruments record data in a few billionths of a second. NIF’s automated control subsystems are built from a common object-oriented software framework that distributes the software across the computer network and achieves interoperation between different software languages and target architectures. A large suite of business and scientific software tools supports experimental planning, experimental setup, facility configuration, and post-shot analysis. Standard business services using open-source software, commercial workflow tools, and database and messaging technologies have been developed. An information technology infrastructure consisting of servers, network devices, and storage provides the foundation for these systems. Thus, this work is an overview of the control and information systems used to support a wide variety of experiments during the National Ignition Campaign.

42 ENGINEERING↗
DOE OSTI2024

NEML2: A High Performance Library for Constitutive Modeling

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Occupancy Simulator Solver v1.0

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

Hong, Tianzhen↗
DOE OSTI2021

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗
DOE OSTI2023

STPPHAWKES

SAND2024-08541O STTPHAWKES software solution is a Bayesian estimation approach to a Hawkes process model that treats missing data as a latent variable in the estimation. The open-source software will be released to the R CRAN repository and Sandia National Labs’ Github. 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.

Rowe, Stephen↗