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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

PyTUQ: Python Toolkit for Uncertainty Quantification

SAND2025-03661O PyTUQ is a user-friendly software toolkit designed to help researchers and professionals understand and manage uncertainty in several scientific fields. By providing tools for analyzing how uncertainties affect outcomes, PyTUQ can be applied in areas such as energy production, and biology. Its unique approach allows users to make more informed decisions by assessing risks and improving predictions. Whether you're studying combustion processes or exploring complex biological systems, PyTUQ empowers you to gain deeper insights and enhance the reliability of your results. 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

CFD Simulation of Biomass Pyrolysis Vapor Upgrading over a Pt/TiO 2 Catalyst in Fixed and Moving Beds

This report presents a comprehensive computational fluid dynamics (CFD) study of the biomass pyrolysis vapor phase upgrading and the catalyst regeneration in different reactors under various operational conditions. This study used the open-source software Multiphase Flow with Interphase eXchanges (MFiX), developed at National Energy Technology Laboratory (NETL) for simulating hydrodynamics, heat transfer, and chemical reactions in multiphase systems. This simulation solved the conservation of mass, momentum, energy, species for gas and solid phases with chemical reactions.

09 BIOMASS FUELS↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

coloring v.1.0

SAND2024-02435O The software coloring allows applications written in the C++ language to generate publication-quality 2D plots. Its functionality is similar to Microsoft Excel's chart functionality or the Python language's matplotlib package. The value of coloring is that it is written purely in C++ and thus can be compiled on systems where Python is not available and linked into high performance computing software. For drawing shapes onto an image, coloring uses the mathematical definition of quadratic Bezier splines and uses Newton's method to search for the closest point on a spline to a given query point. This is then used to do accurate sub-pixel sampling to get clean, non-pixelated images. For working with the actual plot data, coloring has basic statistical methods such as finding maxima and minima as well as axis scaling such as logarithms. 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.

Ibanez, Daniel↗
DOE OSTI2026

RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development

Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.

Ghawaly, James M. [Division of Computer Science an↗
DOE OSTI2020

Structural Analysis Approach for the Defense Programs Package 3 (DPP-3)

The Pacific Northwest National Laboratory (PNNL) is the design authority for a new Type B hazardous materials transportation package designated as the Defense Programs Package 3 (DPP-3) for the U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA). The DPP-3 has been developed using similar materials and fabrication methods employed in previous U.S. Nuclear Regulatory Commission (NRC), DOE, and NNSA certified packages. The DPP-3 design criteria are derived from the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), NNSA guidance and NRC regulatory guides in order to safely and securely transport a variety of payloads. Final regulatory approval by the NNSA will require physical testing to demonstrate that the containment vessel (CV) remains leaktight after enduring the entire regulatory testing sequence prescribed in Title 10 of the Code of Federal Regulations Part 71 (10 CFR 71). In order to gain confidence that the DPP-3 will remain leaktight after testing, the DPP-3 has been structurally analyzed using the Finite Element Analysis (FEA) software LS-DYNA. The FEA analyses serve two general purposes: first, they aid in design and development of the package, and second, they advise as to which drop orientations are expected to cause the most damage during regulatory testing. This paper will discuss how the design criteria are incorporated into analytical techniques needed to evaluate the FEA structural simulation results for 10 CFR 71 conditions to give confidence the DPP-3 testing campaign will be successful.

Sakalaukus Jr., Peter J.↗
DOE OSTI2020

Sphynx

Sphynx is a software library for parallel graph partitioning and load balancing. It partitions a set of vertices in a graph such that the vertex parts are balanced, and the edge cut is approximately minimized. The primary use case is to partition a computational graph into smaller tasks that can be assigned to processors (cores) for efficient parallel computing. Sphynx is based on the Trilinos scientific computing toolkit so can run on both multi-CPU and multi-GPU systems. SAND2020-12183 M 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.

Boman, Erik↗
DOE OSTI2024

NetDuid

SAND2025-00238O The NetDuid library is a software tool that can manage and manipulate DHCP Unique Identifiers (DUIDs) in .NET applications. It simplifies the process of working with DUIDs by offering functionalities such as parsing, constructing, comparing, checking equality, and formatting DUIDs. The library supports various types of DUIDs, making it easier for developers to handle these identifiers in their applications. The program fills a gap in the .NET/C# ecosystem by providing a specialized tool for managing DHCP Unique Identifiers, offering a straightforward and efficient solution for developers. 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.

Engelhardt, Robert↗
DOE OSTI2025

Machine Learning-Driven Conservative-to-Primitive Conversion in Hybrid Piecewise Polytropic and Tabulated Equations of State

We present a novel machine learning (ML)-based method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch (2.0+) and optimized for GPU inference using NVIDIA TensorRT (8.4.1), achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves 𝐿 1 and 𝐿 ∞ errors of 4.54 × 10 −7 and 3.44 × 10−6, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (dual AMD 64-core 2.45 GHz Milan processors and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. By exploiting the underlying symmetries within the equation of state, these findings highlight the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations.

conservative-to-primitive conversion↗
DOE OSTI2023

CCSI Toolset 3.17 Release

CCSI Toolset 3.17 Release Highlights A workaround was developed to allow complex Aspen Custom Modeler (ACM) models to be used in FOQUS. This workaround uses Visual Basic for Applications to connect the ACM models to FOQUS. The ability for User plugins to be uploaded to FOQUS Cloud was added. The documentation was updated to include Optional Software Install and Tutorial Notes to clarify the usage of Turbine and SimSinter in installation instructions and adds a link to the relevant tutorial page. The Sequential Design of Experiments documentation was updated with current screenshots. The copyright was updated to include 2023.

AS↗
DOE OSTI2024

An Organic, Direct Bonded Copper, Multi-Layered, Ultra-Low Inductance Package for High-Power UWBG MOSFETs

The most common metalized substrates used in high-power switching packages consist of a ceramic layer such as Aluminum Nitride (AlN) sandwiched between two copper layers. Ceramic substrates are used because it has the key characteristic of having high dielectric strength while being thermally conductive. A large drawback to ceramic substrates is that they do not allow for a multi-layered circuit design. By replacing the traditional ceramic substrate with organic direct bonded copper (ODBC) we can open a wide range of possibilities when it comes to power module layout such as multi-layered circuits and double-sided cooling. Both benefits are critical while packaging high-performance Gallium Oxide (Ga2O3) MOSFETs. Because of Ga2O3's relatively poor thermal conductivity, a double-sided cooled package becomes necessary. Therefore, the use of ODBC provides the flexibility to fabricate copper traces carrying much higher currents, and by creating a multi-layered package, we can drastically reduce the parasitic inductance inside the power module. Achieving lower parasitic inductance is critical for an ultra-fast Ga2O3 package to avoid excessive voltage overshoot and ringing. Using ODBC, we have designed novel packages capable of handling the challenges presented by fast Ga2O3 switching. Using multi-physics modeling software, we can validate our design before building the prototype. Due to the simple process parameters needed to work with ODBC, we can rapidly create prototypes without using external vendors. This flexibility allows us to quickly design, build, and validate highly complex switching power modules to accommodate next generation, Ga2O3 switching devices.

ADVANCED PROPULSION SYSTEMS,ENGINEERING↗
DOE OSTI2022

[Presentation Slides] Code-to-Code Benchmark Study for Thermal Stress Modeling and Preliminary Analysis of the High-temperature Single Heat-Pipe Experiment

In the heat-pipe-cooled microreactor, heat pipes remove heat from the reactor core as a passive heat-transfer device, so the fluid circulation is not required for cooling, which can substantially simplify the overall reactor design. However, given the extremely high temperatures in the core region and potentially large temperature gradients across the structure materials, thermal stresses need to be well-analyzed to ensure structural integrity during normal operations and accident scenarios. This presentation slides discuss finite element method-based thermal-stress analysis for the high-temperature single heat-pipe test article in the Single Primary Heat Extraction and Removal Emulator (SPHERE) facility at Idaho National Laboratory (INL), using two commercial software packages, Abaqus and Star-CCM+. A code-to-code benchmark study was performed to crosscheck the model setup and capability of each code and to gain preliminary insights into the potential thermal stress concerns from the current experimental setup. It is observed the significant thermal stresses happen at the inner surface of the heat pipe hole surrounded by electric heaters where the largest temperature gradients appear. The temperature fields have good agreements between Abaqus and Star-CCM+, while the induced thermal stresses show modest deviations probably due to differences of meshing engines used in these two codes. It is found that the local maximum thermal stresses may reach close to the ultimate tensile strength and yield strength of structural material depending on the heater power. Ultimately, the coupled thermal-structural analysis will help guide the current experimental plan and ensure the facility safety for future experimental study.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Poplar

SAND2025-00683O Poplar is a software tool that generates a phylogenetic tree from input gene and genome sequences. It integrates established tools to identify genes within genomes, group sequences, construct gene trees, and infer a species tree. Poplar processes nucleotide sequences, identifies similar sequences using Nucleotide BLAST, groups them with DBSCAN, aligns sequences with MAFFT, constructs gene trees with RAxML-NG, and infers a species tree using ASTRAL-Pro3. This pipeline provides a structured approach to phylogenetic analysis, facilitating the study of evolutionary relationships among species. 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.

Krishnakumar, Raga↗
DOE OSTI2025

SOHIP Abel Transform and Onion Peeling Model Module

This software provides tools for analyzing and modeling physical systems using mathematical transforms and layered models. It includes (1) functions for performing the Abel transform, which is used to relate measurements of bending angles to properties such as refractive index and radius in a medium. The code can compute bending angles from input profiles and also reconstruct these profiles from observed data; (2) the functions for modeling systems with multiple layers using an onion-peeling approach, allowing users to simulate and analyze the behavior of layered materials or structures. These capabilities are useful for researchers and engineers working in fields such as optics, atmospheric science, and materials analysis, enabling them to interpret and model data from experiments or simulations relates to refraction in spherical symmetric medium.

Xu, Shuang [Lawrence Livermore National Laboratory↗
DOE OSTI2021

CFD Analysis of RLUOB Zone 1 HEPA Filter Plenum and Testing Manifolds

The purpose of this computational fluid dynamics (CFD) analysis is to ensure that Camfil Farr’s upstream and downstream injection and sampling manifolds can meet or exceed the requirements outlined in the ASME AG-1 1997 a(2000) Code on Nuclear Air and Gas Treatment for the testing of HEPA and adsorbent filters. This paper will present a numerical simulation of airflow in the Radiological Laboratory Utility Office Building (RLUOB) zone 1 HEPA filter plenum and testing manifolds using the commercial CFD software ANSYS FLUENT 2020R1. The CFD analysis focuses on the investigation of the air flow distribution and air-aerosol mixing uniformity. The evaluation was done for all steps of the modeling process: grid generation, physics setup, simulation, and post-processing. The mass flow rate in each section of the zone 1 injection and sampling manifolds is also reported.

42 ENGINEERING↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2023

As the United States' largest source of renewable energy, wind is already playing a vital role in the nation's shift to electricity generated from sustainable resources. Scientists, engineers, and analysts at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) are propelling our country closer to this clean energy future with new efficient, reliable, and cost-competitive wind technologies and deployment strategies. In Fiscal Year 2023 (FY 2023), NREL received national acclaim for recent wind technology breakthroughs, applauded sustained decades of progress in wind energy research, and heralded new initiatives to support the next generation of wind development. This report and NREL's achievements of the last year set the stage for innovative research to come. Two NREL software innovations used to assess wind technology options were recognized with R&D 100 Awards. The Renewable Energy Potential (reV) model helps planners, developers, and researchers calculate wind energy capacity, generation, and cost for specific facilities, fleets and scenarios. NREL's Simulation and Emulation for Advanced Systems validates transmission and distribution solutions to minimize implementation risks. Both of these tools can be applied to a range of renewable solutions to cut costs and improve the reliability of systems in even the most remote locations. The laboratory commemorated landmark anniversaries for wind energy research facilities and programs managed by NREL. The National Wind Technology Center (NWTC) celebrated 30 years as a world-class hub for renewable energy research. An important component of the NWTC on NREL's Flatirons Campus, the one-of-a-kind 7-megawatt (MW) Controllable Grid Interface wind energy research apparatus, turned 10 this year. The Collegiate Wind Competition completed its tenth annual competition in May 2023. Six years after the grand challenges in wind energy science were first compiled, NREL brought together more than 100 wind energy experts from 15 countries to revisit the field's most pressing research needs. Other key new research initiatives examined ways to simultaneously address the needs for skilled wind energy workers and equitable job opportunities. NREL released the first national-level report evaluating offshore wind industry workforce gaps and ways to build a large, diverse pool of job candidates. The lab also co-hosted the International Partnering Forum's Offshore Wind Workforce Summit as part of its ongoing efforts to address the special challenges of that industry sector. NREL research and development (R&D) for the DOE Wind Energy Technologies Office (WETO) continues to build momentum in its efforts to combat climate change, create clean energy jobs, and promote energy justice. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2023 (between Oct. 1, 2022, and Sept. 30, 2023).

accomplishments↗
DOE OSTI2023

Towards elastic in situ analysis for high-performance computing simulations

In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. We demonstrate the use of Colza with the Deep Water Impact and the AMR-Wind simulations, coupling them with the ParaView Catalyst and Ascent in situ libraries, and show that Colza enables dynamic rescaling of these widely-used frameworks with no interruption to the simulation or staging service. Here, we highlight the challenges of enabling such elasticity, which requires overcoming these frameworks' reliance on MPI, using distinct engineering approaches, namely dependency injection and dependency overload. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Predicting Deformation of Parts from HP Printers due to Sintering with Modeling and Simulation (CRADA NFE-18-07363)

This work proposes to leverage Oak Ridge National Laboratory’s (ORNL) sintering simulation software developed at the Manufacturing Demonstration Facility to 1) investigate the deformation of sintered parts and 2) to develop fast surrogate-based optimization methods for HP Inc. This includes the development of ORNL’s software on HP systems, integration and testing with HP part geometries and material properties, the development of surrogate models based on the results of the simulation, and modifications to the software based on feedback from HP. The expected impacts of this work are a drastic reduction in print costs, an increase in printer throughput, and the identification of novel, possibly exotic materials for 3D printing. This work will also decrease the time to market for HP’s print technology by decreasing the amount of resources and type dedicated to physical testing.

42 ENGINEERING↗