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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SAMPA Based Streaming Readout Data Acquisition Prototype

We have assembled a small-scale streaming data acquisition system based on the SAMPA front-end ASIC. The 32-channel SAMPA chip was designed for the high-luminosity upgrade of the ALICE Time Projection Chamber (TPC) detector at the CERN Large Hadron Collider. The goals of the prototype system are to determine if the SAMPA chip is appropriate for use in detector systems at Jefferson Lab, and to gain experience with the hardware and software required to deploy streaming data acquisition systems in nuclear physics experiments. The 800 channel system is composed of components used in the ALICE TPC data acquisition upgrade. Five front-end cards (FEC) support five SAMPA chips each. SAMPA data streams on an FEC are concentrated into two high-speed (4.48 Gb/s) serial data streams by a pair Gigabit Transceiver ASICs (GBTx). These ten streams (44.8 Gb/s) are transmitted from the FECs over fibers to a PCIe based readout unit. The FPGA engine on the readout unit compresses data for transmission to a server via 100 Gb ethernet. Components on the FECs are radiation tolerant. High data rates can be handled. The system is by design scalable and thus provides a functional prototype for high-rate streaming readout at Jefferson Lab and the future Electron Ion Collider. We have made fundamental measurements (noise, linearity, time resolution) on the SAMPA ASIC. We have also coupled the readout system to a small Gas Electron Multiplier (GEM) detector and have studied its response to cosmic rays. A beam test of the system is planned.

ABBOTT, David↗
DOE OSTI2024

Large-scale Hydrogen Storage Risk Assessment

This project investigated risks involved in deploying a large-scale hydrogen storage system at the Port of Seattle (hereafter, the Port) for its on-terminal and maritime applications in an urban industrial setting. Alongside, the project attempted to address some of the barriers to risk assessment such as need for an exact system design for a systematic investigation, direct access to surrounding communities to gauge their perceptions, and an integrated software required to undertake a full-fledged risk assessment. These barriers were overcome using illustrative reference station designs, engaging with community-facing agencies through Port support, and pooling national laboratory capabilities available for risk assessments. The project identified relevant public safety risk metrics, compared various hydrogen carriers, engaged with community-facing agencies, and explored potential gaps in existing safety codes and standards. The primary impacts of this project include the development of risk assessment guidance for ports and utilities, informing them of the trade-offs in the choice of hydrogen carriers, and the ability to increase public capacity for dialog and engagement. This paves the way toward decarbonization of the Port activities, bringing about awareness around jobs in the market for risk assessments, and the need to ramp up community engagement long before any hydrogen system deployment is undertaken.

08 HYDROGEN↗
DOE OSTI2025

OpenCSP Step-by-Step: Getting Started Guide for Windows (V.1.0)

This document provides a step-by-step tutorial on how to set up OpenCSP for both users and developers. It is meant to support novice users and does not require software engineering experience. It is a detailed extension of the OpenCSP getting started on-line documentation, found here: https://opencsp.readthedocs.io/en/main/contributing.html#getting-started. For an overview of OpenCSP overall, see the OpenCSP website: https://opencsp.sandia.gov. For details on OpenCSP components and algorithm, see the references.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
DOE OSTI2025

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗
DOE OSTI2020

Requirements Description of the REBUS Software for the Versatile Test Reactor

This report presents the modeling and simulation capabilities of Argonne National Laboratory’s fuel cycle analysis code, the REactor BUrnup System (REBUS), that will be relied on for the VTR project. These capabilities will then be used to establish the set of REBUS verification tasks necessary to verify REBUS for usage on the VTR project. A similar path was followed for the DIF3D software where its’ requirements and verification tasks were established. REBUS has been maintained by Argonne since the early 1960s to support its reactor design mission. That software transitioned from the original REBUS to REBUS-2 in the mid 1970s and to REBUS-3 in the mid 1980s. Since then REBUS has gone through many revisions to the current REBUS-11. Note that this version numbering is consistent with the progression of DIF3D, the base flux solver that REBUS is built upon. The name REBUS refers to a pictorial based puzzle as the original developers were inspired by having to track thousands of unique fuel assemblies as they are inserted into the reactor, depleted, shuffled, discharged, and reprocessed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Energy Northwest — Advanced Grid Interactive Load Efficiency (AGILE) : A Techno-economic Assessment

The Advanced Grid Interactive Load Efficiency project team, led by Energy Northwest, was awarded funding as part of the fourth round of the Clean Energy Fund program to investigate, co-create, and complete a preliminary design for grid-interactive efficient buildings (GEBs) for a number of schools served by Grays Harbor PUD. The project team has evaluated different combinations of systems at selected school buildings and the potential to respond to grid and customer needs, and develop a preliminary system technical design, including specifying desired GEB technology options, determining communication and electrical interface requirements, assessing control software sensor requirements, and interfacing with building management systems. PNNL led the techno-economic assessment efforts, leveraging its advanced modeling and analytical methods and tools developed for building-to-grid integration. This report documents the economic assessment for different GEB design options, encompassing an in-depth examination of use cases and value propositions, assumptions and inputs, models, methods, case studies, as well as key findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Next-Generation Marine Energy Software Needs Assessment

Over the past decade the marine energy industry has continued to grow and evolve, with new concepts and technologies constantly being pursued. Additionally, the field of computing is vastly different today than it was five or ten years ago. By utilizing advanced software and hardware architectures, like graphics processing units as well as parallelization and high-performance computing resources, software can produce higher quality outputs and a higher volume of outputs. These software and hardware resources can enable the marine energy community to exploit computational advancements from other research fields, which can include machine learning, differentiable programming, and controls co-design. Better integration of existing software and development of potential new software is necessary to take advantage of trends in modern computing and respond to the current and future needs of the marine energy community. In order to better understand the existing marine energy software landscape and industry needs, DOE's Water Power Technologies Office (WPTO) tasked Sandia National Laboratories and the National Renewable Energy Laboratory to update the needs assessment by identifying existing software gaps and software needs, and assisting WPTO in planning the next wave of marine energy software development. The proposed effort involved cataloguing and analyzing the available data on existing software related to marine energy. The marine energy software landscape has vastly changed in the last ten years. There are now nearly 230 different software packages utilized by the marine energy sector, compared to a decade ago when the Cardinal Engineering survey identified approximately 40 software packages. In 2012, the marine energy software landscape was captured in two tables, whereas the current marine energy software landscape required development of a software database to collect and categorize software.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2023

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

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 aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗
DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) for Pasoh, Malaysia, Sep 2024

This data package contains data from terrestrial laser scanning (TLS) at the Pasoh Forest Reserve, Malaysia. The Pasoh Forest Reserve is a facility of the Forest Research Institute Malaysia, and contains evergreen lowland dipterocarp forest. The Next-Generation Ecosystem Experiments Tropics (NGEE-Tropics) study areas at Pasoh were established to study how different species respond to climatic variation and soil water availability. Two study areas were chosen representing different topography and species. The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree-level characterization of woody structure and leaf area for 12 focal trees with FloraPulse and sap flux sensors, facilitating estimation of woody biomass and leaf area to allow upscaling of water content and transpiration data to the tree-level. Scan positions were not selected to provide consistent data for non-focal trees with the study areas. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) from Urban Biogeochemistry Pilot Project sites, Knoxville, Tennessee, Jul 2024 - Jul 2025

This data package contains data from terrestrial laser scanning (TLS) at five urban park sites in Knoxville, Tennessee, USA. All parks include open-grown and/or closed-canopy trees and mixed nearby land use. These study sites were established as part of the Urban Biogeochemistry Pilot Project, which has an overall goal of better understanding how hydrobiogeochemical cycling is altered within the human environment. These five sites represent a gradient of urbanization, and were instrumented to understand hydrological and biogeochemical cycling (e.g., soil moisture, soil physical properties and biogeochemistry, tree transpiration, species type). The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree- and stand-level characterization of woody structure and leaf area. TLS scans were placed to capture the area around trees with sap flow sensors, and as much of a 50 m radius area around the meteorological station as possible given site property limits. Derived products will allow upscaling of water content and transpiration data. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Summary of Work on a Software Adapter for Project Requirements and MBSE

The National Reactor Innovation Center (NRIC) has funded the development of a digital pipeline to pull a project’s requirements from IBM’s Rational Dynamic Object Oriented Requirements System (DOORS), save them to Deep Lynx, then push them into the corresponding project in Innoslate. These utilities are accessible from a web browser and allow a project’s functional and business requirements to be tied to its tree of model-based systems engineering (MBSE) data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

omni-engineer-lbl (omni) v0.5

This is a fork of the omni-engineer software developed by Pietro Schirano. My collaborators and I are altering the software to work better with our LBNL infrastructure and requirements. The software is a coding agent wrapper around LLM model APIs, and can use any of the APIs provided by LBNL's CBorg service, or any service that uses an OpenAI endpoint(s).

Fong, Timothy [Lawrence Berkeley National Laborato↗
DOE OSTI2024

ExaWorks software development kit: a robust and scalable collection of interoperable workflows technologies

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Idaho National Laboratory Advanced Reactor Technologies Quality Assurance Program Plan

This QAPP covers INL ART R&D, licensing, and program support activities. QA implementing procedures will be identified for all activities to ensure work is documented and the results are capable of successfully completing a technical review by DOE. For work performed by other organizations under contract or other agreements with INL, applicable requirements will be flowed down through the contract or other agreements, but the organizations will be responsible to implement the flowed down requirements through their own specific implementing procedures. This QAPP also covers implementation of software QA (SQA) requirements for software used exclusively by INL ART. Software that is used to support multiple INL programs/projects is not covered by this QAPP; the responsibility for implementing SQA requirements for that software resides with the owners and respective managers of the software. This QAPP does not cover activities for installing and operating experiments in the Advanced Test Reactor (ATR) at INL, nor does it cover ATR facility modifications designed to meet INL ART testing needs (such as temperature, pressure, gas flow, and control systems). Those activities are controlled by the INL QAP and specific ATR implementing procedures. ATR facility management and INL ART management jointly approve experiment designs and functional system requirements. This QAPP does apply to INL ART R&D experiments that will be inserted into ATR and any support activities associated with those R&D activities. This QAPP does not cover activities for installing and operating general post irradiation examination (PIE) test equipment in Materials and Fuels Complex (MFC) facilities. Those activities are controlled by the INL QAP and MFC implementing procedures. However, this QAPP does apply to MFC facility modifications designed to meet specific INL ART testing needs. It also applies to INL ART experiments that will have PIE performed on them and any support activities associated with the PIE activities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2025

H420 Imager Calibration Procedure: Field of View Scan

Gamma-ray imagers with coded apertures have a finite field of view (FOV) within which an image of a source can be generated. A “FOV scan” is a facet of imager calibration procedure that involves collecting specialized data for a range of source locations filling the FOV in order to understand and correct for differences in imager performance. Systematic distortions reveal relative displacements between internal imager components, which can be accounted for in data analysis. The success of this scan requires a stable imager position during the full measurement duration (~hours). This document summarizes the hardware and software tools required to conduct a FOV calibration scan of an H3D H420 Coded Aperture Gamma-Ray Imager.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗