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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 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 OSTI2026

datasight [SWR-26-045]

This software is an AI-powered data exploration with natural language. datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations. Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV and Parquet files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.

Thom, Daniel [National Laboratory of the Rockies (↗
DOE OSTI2021

Evaluation of Data Catalog Software for Hanford Site Environmental Datasets

Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Vision and Development of a Design, Implementation, and Verification Automation (DIVA) Software Platform for DNA Construction

Abstract DNA construction, while a prerequisite to many biological endeavors, is often a time-consuming distraction from an individual’s primary research objectives. We envisioned that with the right software infrastructure and cultural mindset, a single person could execute in parallel the batched DNA construction tasks of an entire research institute, at scales realizing efficiency gains through process and laboratory automation. In pursuit of this vision, we developed the Design, Implementation, and Verification Automation (DIVA) software platform. DIVA’s web interface enables researchers to design DNA constructs (using visual biological computer-aided design tools and biological parts repositories), submit designs for construction to dedicated staff, and track DNA construction as it progresses. DIVA supports the dedicated staff through the DNA construction process and records both successful and unsuccessful attempts toward improving the overall process. The platform is publicly available at public-diva.jbei.org and its open-source code through github.com/JBEI/DIVA.

Plahar, Hector [DOE Agile BioFoundry , , ,; DOE Jo↗
DOE OSTI2025

A universal language for finding mass spectrometry data patterns

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here, in this study, we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

Damiani, Tito [Czech Academy of Sciences (CAS), Pr↗
DOE OSTI2019

Verification of Floating Offshore Wind Linearization Functionality in OpenFAST

The wind engineering community relies on multiphysics engineering software to run nonlinear time-domain simulations, e.g., for design standards-based loads analysis. Although most physics involved in wind energy are nonlinear, linearization of the underlying nonlinear system equations is often advantageous to understand the system properties and exploit well-established methods and tools for analyzing linear systems. Previous work in this area has focused on the development of the new linearization functionality of the open-source engineering tool OpenFAST for floating offshore wind turbines, as well as the concepts and mathematical background needed to understand and apply it correctly. This paper focuses on the verification of this new linearization functionality, which is carried out by comparing results to previous stable versions of FAST. A nonlinear time-domain simulation for a floating offshore platform is also compared to the time-domain response of the linearized state-space model. The linearized results show good alignment between OpenFAST and previous versions of FAST, as well as with the time-domain simulations, thereby showing the accuracy of the new features in OpenFAST.

17 WIND ENERGY↗
DOE OSTI2026

Optimal Co-Design of Integrated Thermal-Electrical Networks and Control Systems for Grid-interactive Efficient District (GED) Energy Systems

This project advances a unified, open-source framework for the optimal co-design of thermal, electrical, and control systems in grid-interactive efficient districts (GEDs). As communities integrate growing levels of distributed energy resources, traditional approaches that model thermal and electrical networks independently lead to reduced efficiency, limited flexibility, and missed opportunities for coordinated operation. To address these challenges, the research team developed a comprehensive suite of physics-based models, control algorithms, and software tools that enable holistic simulation, optimization, and demonstration of district-scale energy systems.

14 SOLAR ENERGY↗
DOE OSTI2026

Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture

Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.

Cao, Xuan [Lawrence Berkeley National Laboratory (↗
DOE OSTI2018

AESD (Advanced Energy System Design) Records API [SWRs-17-50, 17-51, 17-52]

The Records API (application program interface) for Advanced Energy System Design (AESD) enables software that serves multidimensional record-oriented data to interoperate with software than uses such data. In the context of the Records API, multidimensional data records are simply tuples of real numbers, integers, and character strings, where each data value is tagged by a variable name, according to a pre-defined schema, and each record is assigned a unique integer identifier. Conceptually, these records are isomorphic to rows in a relational database, JSON objects, or key-value maps. Records servers might supply static datasets, sensor measurements that periodically update as new telemetry become available, or the results of simulations as the simulations generate new output. Records client software might display or analyze the data, but in the case of simulations the client request the creation of new ensembles for specified input parameters. It is also possible to chain records clients and servers together so that a client consuming data from a server might transform that data and serve it to additional clients. This minimalist API avoids imposing burdensome metadata, structural, or implementation requirements on developers by relying on open-source technologies that are readily available for common programming languages. In particular, the API has been designed to place the least possible burden on services that provide data. This document defines the message format for the Records API, a transport mechanism for communicating the data, and the semantics for interpreting it. The message format is specified as Google Protocol Buffers and the transport mechanism uses WebSockets. We discuss three major use cases for serving and consuming records data: (i) static data, (ii) dynamically augmented data, (iii) on-demand simulations, (iv) with filters, and (v) with bookmarks. Separate implementations of the API exist in C++, Haskell, JavaScript, Python, and R.

Bush, Brian↗
DOE OSTI2018

Advanced Energy System Design (AESD): Technical Manual for the Records API

The Records API (application program interface) for Advanced Energy System Design (AESD) enables software that serves multidimensional record-oriented data to interoperate with software than uses such data. In the context of the Records API, multidimensional data records are simply tuples of real numbers, integers, and character strings, where each data value is tagged by a variable name, according to a pre-defined schema, and each record is assigned a unique integer identifier. Conceptually, these records are isomorphic to rows in a relational database, JSON objects, or key-value maps. Records servers might supply static datasets, sensor measurements that periodically update as new telemetry become available, or the results of simulations as the simulations generate new output. Records client software might display or analyze the data, but in the case of simulations the client request the creation of new ensembles for specified input parameters. It is also possible to chain records clients and servers together so that a client consuming data from a server might transform that data and serve it to further clients. This minimalist API avoids imposing burdensome metadata, structural, or implementation requirements on developers by relying on open-source technologies that is readily available for common programming languages. In particular, the API has been designed to place the smallest possible burden on services that provide data. This document defines the message format for the Records API, a transport mechanism for communicating the data, and semantics for interpreting it. The message format is specified as Google Protocol Buffers and the transport mechanism uses WebSockets. We discuss three major use cases for serving and consuming records data: (i) static data, (ii) dynamically augmented data, (iii) on-demand simulations, (iv) with filters, and (v) with bookmarks. Separate implementations of the API exist in C++, Haskell, JavaScript, Python, and R.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adapting the Electron Beam from SEM as a Quantitative Heating Source for Nanoscale Thermal Metrology

The electron beam (e-beam) in the scanning electron microscopy (SEM) provides an appealing mobile heating source for thermal metrology with spatial resolution of ~1 nm, but the lack of systematic quantification of the e-beam heating power limits such application development. Here, we systemically study e-beam heating in LPCVD silicon nitride (SiN x ) thin-films with thickness ranging from 200 to 500 nm from both experiments and complementary Monte Carlo simulations using the CASINO software package. There is good agreement about the thickness-dependent e-beam energy absorption of thin-film between modeling predictions and experiments. Using the absorption results, we then demonstrate adapting the e-beam as a quantitative heating source by measuring the thickness-dependent thermal conductivity of SiN x thin-films, with the results validated to within 7% by a separate Joule heating experiment. The results described here will open a new avenue for using SEM e-beams as a mobile heating source for advanced nanoscale thermal metrology development.

42 ENGINEERING↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2018

On efficient Hessian computation using the edge pushing algorithm in Julia

Evaluating the Hessian matrix of second-order derivatives at a sequence of points can be costly when applying second-order methods for nonlinear optimization. In this work, we discuss our experiences implementing the recently proposed Edge Pushing (EP) method in Julia as an experimental replacement for the current colouring-based methods used by JuMP, an open-source algebraic modelling language. We propose an alternative data structure for sparse Hessians to avoid the use of hash tables and analyse the space and time complexity of EP method. In our benchmarks, we find that EP is very competitive in terms of both preprocessing time and Hessian evaluation time. As a result, we identify cases where EP closes the performance gap between JuMP's previous implementation and the implementation in AMPL, a commercial software package with similar functionality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MODAQ 2.0 (Modular Ocean Data AcQuisition System v2.0) [SWR-24-138]

MODAQ 2.0 is the continuation of the Modular Ocean/Offshore Data AcQuisition system which offers new benefits of being based on generally available hardware and open-source development tools. The MODAQ 2.0 project is the amalgamation of ROS packages, tools, and guidance for developing data acquisition and control applications for marine energy devices using a ROS2 based architecture. The MODAQ 2.0 Reference Design includes several packages that are targeted for the marine energy sector and allow most developers with a basic background in programming to spin up a high-quality DAQ and control system. It is comprised of multiple software repositories which can be found at the MODAQ2 GitHub Organization: https://github.com/NREL-MODAQ2

Nichols, Casey↗
DOE OSTI2024

USPAS Digital Low-Level RF firmware and software suite (uspas_llrf) v1.0

The USPAS Digital Low-Level RF firmware and software suite was originally developed for the USPAS 2023 LLRF course, for demonstrating the close loop RF control on the BerkeleyLab Marble FPGA carrier and Zest digitizer platform in controlling the RF field for various accelerators. The suite features Register-transfer level (RTL) designs for the board-support layer, digital signal processing and verification test benches, system-on-chip architecture, and Experimental Physics and Industrial Control System (EPICS) support, utilizing a open-source tool chain.

Du, Qiang↗
DOE OSTI2023

A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty

Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. Finally, the mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Concepts and Meta-Programming Library

CAMP provides a portable optimized implementation of template meta-programming primitives. All capabilities found within are available in other open source projects, including metal and mpl among others, but no other library we know of supports all compilers required by LLNL codes. CAMP serves as an efficient facility for writing portable compile-time operations in C++ codes. It was developed as part of the RAJA project to facilitate more complex compile-time introspection and better error message delivery to users, but has since proven useful in Umpire, CHAI and other pre-release LLNL software projects and is thus being re-factored to stand alone. The CAMP library uses portable C++ template mechanisms almost exclusively, with the exception of intrinsics provided for performance by some compilers such as clang and gee. There are no private or protected interfaces and no proprietary information included in the source. Any and all future versions will limit themselves to implementing commonly available and well known primitives for compile-time manipulation of types. A new public github repository will be created to host the code independent of its current location in the raja repository, likely at github.com/llnl/camp.

Kunen, AdamJ.↗
DOE OSTI2022

Calibration and Commissioning of the LSST

Understanding the nature of dark energy and dark matter remains one of the fundamental questions in physics today; impacting our understanding of particle physics, cosmology, and possibly theories of gravity. Given the scale and complexity of the next generation of cosmology experiments (e.g., the Rubin Observatory, the Euclid satellite mission, and the Roman space telescope) we are entering an era where statistical noise no longer determines the accuracy to which we can measure cosmological parameters. Our ability to control and correct for systematics will ultimately determine the scientific impact of these experiments. This award addressed the challenge of how we determine what limits the accuracy of our cosmological measures, what techniques are appropriate for measuring and calibrating the properties of galaxies to best constrain cosmological models, how to develop statistical techniques that are insensitive to systematic errors, and how to optimize survey strategies in order to minimize systematics while maximizing the speed at which an experiment can achieve its science objectives. In this final technical report for award DE-SC0011635 we describe a set of open-source frameworks that simulate the characteristics and properties of current and planned cosmology surveys and the application of these frameworks to the development of new methodologies for estimating the properties and distances to galaxies that are robust to noisy and incomplete data.

79 ASTRONOMY AND ASTROPHYSICS↗