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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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DOE OSTI2025

Postearthquake Damage Mapping via Remote Sensing: Lessons From the 2023 Türkiye Disaster

This review addresses the urgent need for scalable, accurate, and reproducible remote sensing solutions following the February 2023 Türkiye earthquakes. It synthesizes the contributions of five peer-reviewed studies published in the IEEE JSTARS Special Issue on postearthquake damage and risk assessment. These studies cover areas such as damage classification with deep learning, fusion of multisource remote sensing data, creation of benchmark datasets, detailed damage mapping, and analysis of geophysical signals using outgoing longwave radiation. The article summarizes the methodological approaches and the practical relevance of the reviewed studies for detecting, evaluating, and quantifying damage, and outlines key challenges, including model generalization, class ambiguity, and data integration. It also discusses emerging trends, including explainable artificial intelligence, multimodal data fusion, and open-data platforms. This synthesis provides a foundation for building robust, interpretable, and real-time disaster response systems and aims to guide future research in earthquake-related Earth observation and rapid damage assessment.

Taskin, Gulsen [Istanbul Technical University] (OR↗

DOE OSTI2022

Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks (Final Project Report)

The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied various advanced machine learning and big data analysis technology to the power system event detection and classification as the first step toward the power system control and protection pertaining to grid security enhancement.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Generalized Bayesian Monte Carlo Evaluation [Slides]

Bayesian Monte Carlo is a tool to address imperfect data & models, non-linear models, non-normal PDFs, and integration of differential and integral data. Simple propagation to criticality shows effects of new $\mathcal{L}$($β|z,γ$).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Core Model Proposal #410: Updates to Socioeconomic and Macroeconomic Data, Processing Structure, and Visualization

This Core Model Proposal (CMP) comprehensively restructures and updates the macroeconomic and socioeconomic modules in gcamdata. It includes visualizations of key data inputs, accounting identities, and data flows in the context of GCAM-Macro-KLEM. Major improvements include: (1) updating the Penn World Table (PWT) to version 10 and incorporating a new source, the Global Macro Database (GMD); (2) updating the SSP socioeconomics database from version 3.0.1 to 3.2; (3) introducing SSP-specific differentiation of employment and labor force data; (4) improving data integration between national accounts (from PWT, GMD, and GTAP) and GDP/population data from external sources; and (5) general data cleaning and structural refinements. We document the data sources and key assumptions used throughout the processing. These updates establish the foundation for the forthcoming KLEAM version of GCAM-macro.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Module-OT: A Turnkey Solution for Securing Energy Systems

The Modular Security Apparatus for Managing Distributed Cryptography for Command-and-Control Messages on Operational Technology Networks (Module-OT) is a flexible and lightweight solution for grid-edge devices focusing on end-to-end security. It is a bump- in- the-wire solution acting as a secure conduit for data between devices or systems across a network. It improves the cybersecurity posture of DER systems by providing authentication, authorization, and data integrity to secure DER communications. Additionally, it performs key management, provides data security through whitelisting Internet Protocol addresses and ports, blocks unauthorized connections, controls user access, and allows serial or Ethernet connections for added flexibility. The core software is portable to various Linux-based operating systems and is developed to be customized by the developer and researcher communities. Module-OT has been validated in the lab, has been demonstrated at a 500-KW PV-plus-storage site, and has been proven ready to secure operational technology devices. Its core functionality meets current standards, including validation procedures of the NIST Cryptographic Algorithm Validation Program (CAVP) and the Federal Information Processing Standard (FIPS 140-2). Because of its capability to provide an accessible and affordable option for stepping up security across modern energy systems, Module-OT can serve as an effective technological option to standardize cybersecurity moving forward.

cryptography↗

DOE OSTI2021

Argonne Leadership Computing Facility: 2021 Operational Assessment Report

This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for calendar year (CY) 2021 as an advanced scientific computing center. In CY 2021, the ALCF operated its production resource, Theta, an Intel-based Cray XC40 system (11.7-petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9-petaflops) that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform. In 2021, we began deploying Polaris, our newest 40- petaflops system, and augmented this powerful testbed system with an additional 28 nodes to support the integration of real-time experiments and HPC resources. We also deployed our two largest storage systems yet, named Grand and Eagle, that will bring new services to our users and will power data-driven research for years to come. Last year, Theta delivered a total of 20.8 million node-hours to 16 Innovative and Novel Computational Impact on Theory and Experiment (INCITE) projects and 7.2 million node-hours to ASCR Leadership Computing Challenge (ALCC) projects (32 awarded during the 2020–2021 ALCC year and 17 awarded during the 2021–2022 ALCC year), as well as substantial support to Director’s Discretionary (DD) projects (5.5 million node-hours). As Table ES.1 shows, Theta performed exceptionally well in terms of overall availability (95.1 percent), scheduled availability (99.4 percent), and utilization (98.1 percent; Table 2.1). As of the submission date of this document, ALCF’s user community has published 249 papers in high-quality, peer-reviewed journals and technical proceedings. At the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’21), Argonne researchers won two HPCwire Readers’ Choice Awards and were part of a Gordon Bell Prize finalist team recognized for developing an AI-enabled, multi-resolution simulation framework for studying complex biomolecular machines. Their framework was used to observe the SARS-CoV-2 replication-transcription machinery in action, by directly integrating experimental data. ALCF also provided a comprehensive program of high-performance computing (HPC) support services to help our community make productive use of the facility’s diverse and growing collection of resources. We are now entering the exascale era, with exascale machines being planned for national laboratories across the country, including Aurora at Argonne National Laboratory (Argonne) in 2023. ALCF researchers have been leading and guiding numerous strategic activities that will push the boundaries of what’s possible in computational science and engineering and allow us to deliver science on day one.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Descriptor: High Temporal Resolution Meteorological Data at Oak Ridge Reservation (ORR-HiResMet)

Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.

Steckler, Morgan R. [Oak Ridge National Laboratory↗

DOE OSTI2024

OSCAR-Mike: Why not ATAK?

The Office of Nuclear Smuggling Detection and Deterrence (NSDD) is evaluating methods to increase the probability that international partners will detect radioactive materials. One proposed method to accomplish this goal is to improve communications within teams operating in challenging environments. The goal of these improvements would be to enable remote monitoring of detection equipment, share data among end users in the field and subject matter experts, and integrate data from radiation detectors with other types of sensors and camera systems. Accomplishing this goal has the potential to improve the capabilities of currently deployed NSDD equipment. During FY 2021, Oak Ridge National Laboratory demonstrated some core and expanded capabilities of the Android Team Awareness Kit (ATAK), a situational awareness application developed by the US Department of Defense, to enable precision targeting, navigation, and data sharing. During FY 2022, Oak Ridge National Laboratory developed software requirements for an NSDD team awareness kit–based system. The requirements were developed by liaising with NSDD management and subject matter experts to determine NSDD’s needs and by reviewing available software and hardware solutions with US Department of Defense team awareness kit program managers and developers and radiation detection equipment vendors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

EspressoDB: A scientific database for managing high-performance computing workflows

EspressoDB is a programmatic object-relational mapping (ORM) data management framework implemented in Python and based on the Django web framework. EspressoDB was developed to streamline data management, centralize and promote data integrity, while providing domain flexibility and ease of use. It is designed to directly integrate in utilized software to allow dynamical access to vast amount of relational data at runtime.

Chang, Chia↗

DOE OSTI2020

A Community Energy Operations and Planning System: Concept, Use cases, Metrics, and Benefits

Community and city leaders are interested in achieving sustainability goals, providing resilient energy infrastructure, and improving economic competitiveness. Community-level data acquisition and analysis can provide energy and associated benefits that are not possible at the single building level. However, there is a lack of organizational structure, common semantic data models, interoperable systems, and methods to support data-driven decision-making for community-scale energy supply and demand systems. We explored the need and opportunity for a Community Energy Operations and Planning System (Community EOPS), a potential data exchange platform. We conducted “customer discovery” interviews, and reviewed literature, public tools, and technology platforms to identify key energy data “users” and use cases in communities. The key users of the Community EOPS could be developers of mixed-use districts, corporate, defense and university campus energy managers, and city managers of cities that own their energy utility. The value could be for community planning and reporting (for energy data-integrated land use planning and community infrastructure investments in microgrids, storage, district heating and cooling), energy efficiency (leveraging optimizations for community scale energy supply and demand), flexible load management (grid-edge load management to offset, shift, and flatten loads for multiple buildings and EV fleets), cost savings and revenue generation (participating in grid services), and social benefits such as energy resilience, equity, and awareness. We developed a conceptual Community EOPS architecture with recommendations for streamlined and prioritized data acquisition, sharing, and integration driven by prioritized use cases, common metrics, and actionable visualizations that can provide value to a community’s users.

Singh, Reshma↗

DOE OSTI2023

Nuclear Data: What is the big deal? [Slides]

This presentation begins with a look at the practical application: differential and integral data. It also includes a nuclear data evaluation: cross section formalisms, data processing and cross section library generation, computer tools, and criticality safety assessment: nuclear data. The presentation ends with some concluding remarks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Advances in Nuclear Data Evaluation Theory for NCSP [Slides]

The generalized form of the Bayes’ Theorem improves UQ for NCSP of differential and/or integral data evaluations and of applications. It is being implemented in an API for use by SCALE, SAMMY, etc. Extended R-matrix formalism improves evaluations of RRR including direct reactions, direct capture, and the Reich-Moore approx., doorway state reactions with applications to RPI data on lead isotopes. Extended R-Matrix formalism will be implemented into SAMMY post modernization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Data Centers Gap Analysis [Slides]

Data centers and other large loads are a significant driver of unprecedented, near-term demand growth in the United States. Power system planners, utilities, regulators, and other stakeholders are grappling with how to integrate data centers on the system without comprising reliability, resiliency, and energy affordability. NLR is pursuing work to develop a siting and decision-making tool that would draw on power systems modeling expertise to achieve granular representation of trade-offs involved in data center sitting and development. This slide deck supports the same workstream by reviewing the literature to identify mitigation options to facilitate near-term integration of large loads and by presenting options for pursuing data development and/or modeling projects to improve representation of siting options.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Designing a User Interface for Real-Time Magnetometer Data Acquisition

The Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) is a next-generation quantum sensor designed to search for ultralight dark matter and explore new frontiers in quantum mechanics. Due to the experiment s sensitivity to magnetic interference, a magnetometer trolley system was developed to scan magnetic fields along a vacuum tube. Interacting with the system required command-line inputs, creating usability challenges. To improve accessibility and streamline data acquisition, I developed a graphical user interface (GUI) using Python and the customtkinter library. The GUI supports real-time data display, state/mode switching, command execution, and CSV file management. I collaborated with another intern to integrate data visualization features into the GUI, allowing users to generate 3D plots of post-acquisition magnetic field data. In the future, I aim to fix the real-time plotting feature as it results in an unresponsive GUI.

Mendez, Milagros [DuPage Coll.]↗

DOE OSTI2025

Designing a User Interface for Real-Time Magnetometer Data Acquisition

The Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) is a next-generation quantum sensor designed to search for ultralight dark matter and explore new frontiers in quantum mechanics. Due to the experiment’s sensitivity to magnetic interference, a magnetometer trolley system was developed to scan magnetic fields along a vacuum tube. Interacting with the system required command-line inputs, creating usability challenges. To improve accessibility and streamline data acquisition, I developed a graphical user interface (GUI) using Python and the customtkinter library. The GUI supports real-time data display, state/mode switching, command execution, and CSV file management. I collaborated with another intern to integrate data visualization features into the GUI, allowing users to generate 3D plots of post-acquisition magnetic field data. In the future, I aim to fix the real-time plotting feature as it results in an unresponsive GUI.

Mendez, Milagros [DuPage Coll.]↗

DOE OSTI2024

Survey of Use Cases and Scenarios on the Open Energy Data Initiative Solar Systems Integration (OEDI SI) Platform

The Open Energy Data Initiative Solar Systems Integration (OEDI SI) Data and Modeling Platform offers a comprehensive set of use cases tailored for power systems analysis. Each use case is centered around a specific power system analysis problem, supported by composite input data and reference algorithms. These composite input datasets are meticulously assembled using OEDI SI's data preprocessing tools, which integrate raw data from various sources. The primary objectives of the OEDI SI Platform include facilitating access to composite input data through widely accepted input/output formats and verified results. This accessibility enables power system network researchers and developers to validate their algorithms and showcase their applications' capabilities to the broader community. Moreover, the platform strives to promote reproducible, robust, replicable, and generalizable solar systems integration research.

14 SOLAR ENERGY↗