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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Synchrophasors-based Master State Awareness Estimator for Cybersecurity in Power Grid: Testbed Implementation & Field Demonstration

The integration of distributed energy resources(DERs) and expansion of complex network in the distribution grid requires an advanced distributed state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper proposes a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the proposed MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs),deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement the proposed methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the “valley of death” and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex(CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time

42 ENGINEERING↗
NASA NTRS1996

An Open Computing Infrastructure that Facilitates Integrated Product and Process Development from a Decision-Based Perspective

Computer applications for design have evolved rapidly over the past several decades, and significant payoffs are being achieved by organizations through reductions in design cycle times. These applications are overwhelmed by the requirements imposed during complex, open engineering systems design. Organizations are faced with a number of different methodologies, numerous legacy disciplinary tools, and a very large amount of data. Yet they are also faced with few interdisciplinary tools for design collaboration or methods for achieving the revolutionary product designs required to maintain a competitive advantage in the future. These organizations are looking for a software infrastructure that integrates current corporate design practices with newer simulation and solution techniques. Such an infrastructure must be robust to changes in both corporate needs and enabling technologies. In addition, this infrastructure must be user-friendly, modular and scalable. This need is the motivation for the research described in this dissertation. The research is focused on the development of an open computing infrastructure that facilitates product and process design. In addition, this research explicitly deals with human interactions during design through a model that focuses on the role of a designer as that of decision-maker. The research perspective here is taken from that of design as a discipline with a focus on Decision-Based Design, Theory of Languages, Information Science, and Integration Technology. Given this background, a Model of IPPD is developed and implemented along the lines of a traditional experimental procedure: with the steps of establishing context, formalizing a theory, building an apparatus, conducting an experiment, reviewing results, and providing recommendations. Based on this Model, Design Processes and Specification can be explored in a structured and implementable architecture. An architecture for exploring design called DREAMS (Developing Robust Engineering Analysis Models and Specifications) has been developed which supports the activities of both meta-design and actual design execution. This is accomplished through a systematic process which is comprised of the stages of Formulation, Translation, and Evaluation. During this process, elements from a Design Specification are integrated into Design Processes. In addition, a software infrastructure was developed and is called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment). This represents a virtual apparatus in the Design Experiment conducted in this research. IMAGE is an innovative architecture because it explicitly supports design-related activities. This is accomplished through a GUI driven and Agent-based implementation of DREAMS. A HSCT design has been adopted from the Framework for Interdisciplinary Design Optimization (FIDO) and is implemented in IMAGE. This problem shows how Design Processes and Specification interact in a design system. In addition, the problem utilizes two different solution models concurrently: optimal and satisfying. The satisfying model allows for more design flexibility and allows a designer to maintain design freedom. As a result of following this experimental procedure, this infrastructure is an open system that it is robust to changes in both corporate needs and computer technologies. The development of this infrastructure leads to a number of significant intellectual contributions: 1) A new approach to implementing IPPD with the aid of a computer; 2) A formal Design Experiment; 3) A combined Process and Specification architecture that is language-based; 4) An infrastructure for exploring design; 5) An integration strategy for implementing computer resources; and 6) A seamless modeling language. The need for these contributions is emphasized by the demand by industry and government agencies for the development of these technologies.

Hale, Mark A.↗
DOE OSTI2022

FY2022 Progress Report for Advanced Re-fabrication/Re-instrumentation Capability Development

In support of performing follow-on irradiation experiments with previously irradiated materials, the Halden Reactor Project developed unique and state of the art capabilities to refabricate and re-instrument previously irradiated materials. Such materials were used in in-pile tests at the Halden reactor, and out-of-pile tests for example using furnaces as a heat source. The decision to close the Halden Reactor Project results in the loss of this refabrication and re-instrumentation capability. As a result, the United States (U.S.) Department of Energy (DOE) has determined to develop refabrication and re-instrumentation capability at the unique shielded facilities at Idaho National Laboratory (INL). The development of refabrication capability has been completed and demonstrated. This report focuses on the complementary aspects of reinstrumentation and the progress to-date. Halden spent nearly 30 years developing both refabrication and re-instrumentation. Collaboration with Halden is allowing Idaho National Laboratory (INL) to develop this capability much more rapidly. In FY-21 the results include development of the capability to drill annular center holes in ceramic UO2 fuel pellets, development of fuel rod end caps with feedthroughs for centerline instrumentation inside the rodlet, The procurement of both fuel drilling and welding demonstration equipment from Halden, evaluation of surface thermocouple attachments to support better understanding of temperature measurement uncertainties, and finally, the conceptual design of a new shielded enclosure where advanced refabrication and re-instrumentation equipment can be housed. In FY-22, the results include; Completing set up of the drilling and welding modules procured from Halden, and early experimental trials using that equipment.; Completing set up of an out-of-cell circumferential weld system to allow for further weld development to take place and support fabrication of fuel for fresh fuel experiments.; Evaluation of the Hot Fuel Examination Facility (HFEF) infrastructure to support future installation of advanced re-fabrication/re-instrumentation equipment, specifically related to necessary infrastructure for cryo-drilling.; Developments in dry-drilling alternative. Including experimental studies showing cordierite is the most suitable surrogate for UO 2 for performing drilling studies. That drilling performance is enhanced when a fuel-clad bonding condition is simulated.; Conceptual design completed for attaching surface thermocouples to irradiated fuel in support of TWIST capsule experiments. The authors would like to thank the numerous colleagues at INL and Halden who provided support in accomplishing this work. Their support both material and intellectual is invaluable in advancing the state of the art and establishing the capabilities for refabrication and re-instrumentation at INL.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

DSI Python API Demo May 2023 [Slides]

Data Science Infrastructure (DSI) is developing searchable databases and workflows for simulation and experimental data derived from ASC clients. Short term goal: Make data easily accessible through metadata indexing and querying while respecting data permissions. Longer term goal: Use this data for data science activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Secure API-Driven Research Automation to Accelerate Scientific Discovery

The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M enables secure and flexible programmatic access to high performance computing (HPC) resources. This framework allows intelligent agents and experimental facilities to dynamically provision resources and execute complex workflows, accelerating experimental lifecycles, and enabling AI-augmented autonomous science. S3M establishes a modern foundation for scientific computing infrastructure that significantly reduces traditional barriers between researchers, computational resources, and experimental facilities.

Skluzacek, Tyler [ORNL] (ORCID:0000000322424931)↗
DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRSDate not supplied

Database Design Strategies for Coordinated Simulation and Testing in Additive Manufacturing

The qualification and certification (Q&C) process presents a significant challenge for widespread adoption of additive manufacturing (AM) materials and processes for aerospace applications. A relational database framework will be presented as a tool for data curation of coordinated experimental and computational materials modeling research activities. A comparison of relational and hierarchical data structures in this domain will be emphasized through the evolution of a database design strategy. This framework’s mission is to support the advancement of computational materials-informed Q&C by providing the necessary data infrastructure to trace reliability and reproducibility measures through unified AM materials simulation and experimental testing. FAIR (findable, accessible, interoperable, and reusable) data will be highlighted as a necessary precursor for automation of specific actions, which ultimately reduces the time and expense burden for Q&C. The discussion will be mostly limited to back-end design elements, though a few front-end user experience examples will also be shared.

Qualification↗
DOE OSTI2021

Intrinsic self-stressing and low carbon Engineered Cementitious Composites (ECC) for improved sustainability

Highlights: • A self-stressing criterion for ECC was established and experimentally verified. • An intrinsic self-stressing ECC was developed by utilizing LC3 and CSA cement. • Initial maximum expansion and later expansion loss were successfully tailored. • Expansion mechanism was identified by XRD patterns. • Self-stressing ECC has 64% of the carbon footprint of conventional concrete. Engineered Cementitious Composites (ECC) is an emerging cementitious composite material with ultra-high ductility. However, its higher cement dosage limits its material greenness and leads to concerns with drying shrinkage. In this research, an ECC utilizing limestone calcined clay cement (LC3) and calcium sulphoaluminate cement (CSA) is investigated, focusing on minimizing the material's embodied carbon while enhancing its durability with intrinsic self-stressing functionality. A self-stressing criterion is theoretically established and experimentally verified. X-ray diffraction patterns reveal an ettringite quantity that modulates the initial expansion and later expansion-reversal of LC3-CSA-ECC to support a persistent self-stressing mechanism. LC3-CSA-ECC has a lower (64%) carbon footprint and similar embodied energy compared to conventional concrete. When combined with the durability advantage (tiny crack, high ductility of 5.5%, and self-stressing function), this low carbon self-stressing ECC holds promise as a sustainable repair material that lowers the embodied and operational carbon in civil infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Provenance infrastructure for multimodal x-ray experiments and reproducible analysis

Exploring relationships between material structures and desired properties for material discovery and synthesis requires mining machine-readable databases and linking experimental data with sufficiently rich annotations. There are vast gaps both in these data sources and in the connections between them. We present a provenance-based data management and analysis framework that enables capturing, persisting, and reanalyzing experimental data from the BNL National Synchrotron Light Source II (NSLS-II). Our system leverages NSLS-II Bluesky for experimental data acquisition, captures analysis parameters, and enriches the search space with results from the scientific literature. We describe in detail the challenges and benefits of data-driven approaches when comparing computational and experimental data, such as the multiple possible scenarios of interest in often ill-posed inverse problems and missing data. Our infrastructure utilizes the NSLS-II event model and includes discovery mechanisms across multiple beamlines. A text and data mining module using natural language processing classification techniques extracts relevant information from the literature that is added to data sources. We describe a provenance acquisition and analysis rerun workflow based on a directed acyclic graph that keeps track of process and data provenance and allows to replay analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

NRIC FY 2025 Collaboration Initiatives Annual Report

The National Reactor Innovation Center (NRIC) is a national program established by the U.S. Department of Energy (DOE) in 2019 and led by Idaho National Laboratory (INL). Its mission is to work with industry and national laboratories to bridge the gap between concept, demonstration, and commercialization of advanced nuclear technology. NRIC works to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient collaboration and coordination with partners. NRIC is partnered with industry to accelerate the demonstration and deployment of advanced nuclear technology using DOE’s national laboratory infrastructure and expertise. NRIC’s vision is to establish four new experimental facilitates and two large reactor test beds for integrated technology demonstrators and experimentation by 2028 and complete two advanced nuclear technology tests by 2030.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2003

Comparison of Experimental Data and Computations Fluid Dynamics Analysis for a Three Dimensional Linear Plug Nozzle

A three dimensional linear plug nozzle of area ratio 12.79 was designed by EADS Space Transportation (former Astrium Space Infrastructure). The nozzle was tested within the German National Technology Program 'LION' in a cold air wind tunnel by TU Dresden. The experimental hardware and test conditions are described. Experimental data was obtained for the nozzle without plug side wall fences at a nozzle pressure ratio of 116 and then with plug side wall fences at NPR 110. Schlieren images were recorded and axial profiles of plug wall static pressures were measured at several spanwise locations and on the plug base. Detailed CFD analysis was performed for these nozzle configurations at NPR 116 by NASA MSFC. The CFD exhibits good agreement with the experimental data. A detailed comparison of the CFD results and the experimental plug wall pressure data are given. Comparisons are made for both the without and with plug side wall fence configurations. Numerical results for density gradient are compared to experimental Schlieren images. Experimental nozzle thrust efficiencies are calculated based on the CFD results. The CFD results are used to illustrate the plug nozzle fluid dynamics. The effect of the plug side wall is emphasized.

Ruf, J. H.↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2026

Towards a Public Event Display for DUNE

The Deep Underground Neutrino Experiment (DUNE) is a next generation long baseline neutrino experiment based at Fermilab, with a near detector near the beam target and a Far Detector (FD) in South Dakota. As the experiment prepares for its first data runs, creating pathways for public engagement and data transparency is essential. We present the first-ever DUNE event display designed for public outreach and education. Developed using data from the ProtoDUNE detectors at the CERN Neutrino Platform, this tool provides an intuitive and interactive interface that allows non-experts to visualise and explore particle interactions in a Liquid Argon Time Projection Chamber (LArTPC). By translating raw experimental data into a browser-accessible format, we establish the essential infrastructure for DUNE’s pathway to open data. This talk will detail the technical development of the display, its current implementation with ProtoDUNE data, and the strategic roadmap for integrating it into DUNE’s long-term open-access framework.

Sabater, Eva [U. Sussex (main)] (ORCID:00090001748↗
NASA NTRSDate not supplied

Experimental Setup for Mechanically Testing Subscale Triangular, Rollable, and Collapsible Deployable Composite Booms

High-strain composite deployable space structures are used for space infrastructure and science applications such as solar array supports, antennae, camera masts, and lightweight structures supporting solar sailing propulsion elements. Triangular, Rollable, and Collapsible (TRAC) deployable composite booms are one example of a high-strain composite deployable structure and were studied using novel experimental test and characterization methods developed under the Gravity Offloading and Analysis of Long Imperfection-sensitive Elements (GOALIE) project. In the present work, a subscale 7-m-long TRAC boom was suspended vertically to orient gravity along the length of the boom. By orienting vertically, highly nonlinear and unstable behavior often encountered during horizontally oriented gravity offload testing of similar structures was reduced. Pretest analytical predictions of TRAC booms indicated three unique failure modes, loads, and locations for three unique loading cases of in-plane bending, out-of-plane bending, and axial compression. To investigate the predicted behavior, an experimental test was set up to impart mechanical loads to a subscale TRAC boom. The experimental setup, loading cases, and instrumentation used to characterize the mechanical response of a subscale TRAC boom are described in this paper.

Experimental testing↗