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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Seeing the whole picture: Methods for getting the most from micro X-ray computed tomography of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) coated fuel particles are a nuclear fuel form under extensive study for use in advanced nuclear reactor concepts. TRISO fuels are subjected to high temperature neutron irradiations and then examined to assess their performance by determining fission product retention and studying morphological changes. Micro X-ray computed tomography is one method of nondestructively studying the effects of TRISO performance. This work addresses the need for image processing to remove X-ray tomographic reconstruction artifacts that prevent the study of TRISO features, as the TRISO particles’ high Z kernel can introduce metal artifacts that degrade the image quality in the surrounding low Z coating layers. These metal artifacts were reduced by imaging the TRISO particles with both high- and low-energy X-rays and applying a mask to the radiographs obtained with low-energy X-rays to digitally remove the dense fuel kernel region. These masked radiographs were then used to produce a tomographic reconstruction which was combined with the tomographic reconstruction of the high-energy data. This enabled the relatively-low-density TRISO buffer layer to be examined in more detail, providing information on irradiation induced dimensional changes of the coatings. This methodology, which helps see the full picture of a TRISO particle, is not limited to nuclear fuels but can be applied to systems that contain highly attenuating material surrounded by less dense materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗
DOE OSTI2023

A Robust Method to Secure Multi-Inverter Grid Tied PV and Battery Energy Storage Systems Against Cyber Intrusions

This paper details a robust method to secure a multi-inverter grid tied system that interfaces photovoltaic (PV) and battery energy storage against potential cyber-attacks. The method can be applied to any third-party inverter systems without a need to modify their internal controls. A small random private excitation signal termed "watermark" is injected into the DC input voltage terminals (via a series transformer) connected to the PV/battery inverter system. An external robust cyber intrusion detector (CID) hardware consisting of a digital signal processor (DSP) generates the "watermark" and also receives the sensor signals that control the setpoints of the PV/battery grid tied system. The CID algorithm is shown to detect all possible cyber intrusions (such as false data injection(FDI)) on external sensor signals such as P and Q measured by a smart meter that control the overall system operation. The proposed CID computes online system ID and two variance tests in real time on each sensor signal and is able pinpoint intrusion location in a multi-inverter system. Results on a hardware in the loop (HIL) of a two-inverter grid connected system demonstrate effectiveness of the proposed CID system for FDI and unobservable FDI. Test results on a laboratory prototype will be discussed in the conference presentation.

Ibrahim, Hasan↗
DOE OSTI2023

Evaluation of Digital Twin Modeling and Simulation

A digital twin has intelligent modules that continuously monitor the condition of the individual components and the whole of a system. Digital twins can provide nuclear power plants (NPP) operators an unprecedented level of monitoring, control, supervision, and security by contributing a greater volume of data for more comprehensive data analysis and increased accuracy of insights and predictions for decision making throughout the entire NPP lifecycle. NPP operators and managers have historically relied on limited, second hand or incomplete data. With proper implementation, digital twins can provide a central hub of all intel that allows for a multidisciplinary view of an NPP. This equips operators and managers with the ability to have more information, context, and intel that can be used for greater granularity during planning and decision making. Digital twins can be used in many activities as the technology has many different concepts surrounding it. From the various definitions of a digital twin within the industry, digital twins can be differentiated by levels of integration/automation. The three main models include digital model, digital shadow, and digital twin. Digital twins offer many potential advancements to the nuclear industry that could reduce costs, improve designs, provide safer operation, and improve their overall security.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

IACMI Project 4.2: Thermoplastic Composite Development for Wind Turbine Blades

(Section 5.1) Composites made from Arkema’s Elium® thermoplastic resin and Johns Manville fiberglass were researched during this project for applications in wind blade manufacturing. A techno-economic model was developed to model this wind blade manufacturing process using these materials in place of traditional composites made with thermoset resin. This model was based on manufacturing a 61.5-meter wind blade, which showed a 4.7% reduction in wind blade cost as compared traditional thermoset materials. These cost savings were not from the thermoplastic material costing less than traditional thermoset materials, but rather from decreased capital costs, faster cycle times and reduced energy requirements and labor costs. (Section 5.2) An infusion and curing model was developed for thermoplastic composite wind blades using PAM-RTM. The primary goal was to demonstrate the infusion simulation for the Elium® resin system on a 13-meter wind blade. Additionally, the exotherm temperature was predicted and compared to measurements, which showed model results within 10% of actual measurements. (Section 5.3) Composite laminate panels and composite sandwich panels with a balsa core were produced; specimens were cut and characterized. Similar composite specimens were made with Elium® thermoplastic resin and Hexion thermoset epoxy (RIMR135/RIMH1366) to enable comparisons between these resin systems. The static test methods included: tensile, compression, in-plane shear, interlaminar shear, flexural, sandwich core shear flexure, and single cantilever beam tests for sandwich beams. Fatigue testing at room temperature was completed to composite laminate panels at a stress ratio of R=0.1 and R=10. In addition, fatigue testing to laminate panels was completed at -30°C, and at room temperature after conditioning specimens at 70°C and 90% relative humidity. Overall, mechanical test results from Elium® composites are similar to epoxy composites. (Section 5.4) Elium composite panels were produced with intentional defects such as voids and nonwetting of fibers to begin to understand performance sensitivity to defects. A thermal digital image correlation (TDIC) method provides high spatial resolution strain field at elevated temperatures and can be used to identify defective regions within composite panels. Flexural modulus differences of 21% were seen between defect and non-defect panels. Other Elium® composite panels were forced to be defective by boiling the resin after infusion, which created voids throughout the composite laminate. X-ray computed tomography scanning was used to view the internal structure of the defect panels. Defect panels had a significant reduction in fatigue life as compared to baseline panels produced without intentional defects. (Section 5.5) Lap shear specimens were fabricated to compare the lap shear strength of an off-the-shelf adhesive (Plexus MA590) and two new adhesives developed by Arkema (Bostik SAF30 90 and Bostik SAF30 120). ISO standard 4587:2003 was used to standardize the testing method and sample fabrication. Lap shear specimens were made at 1mm, 3mm, and 10mm thicknesses. The Bostik adhesive lap shear test results were similar to Plexus for all thicknesses. (Section 5.6) Fiber-reinforced polymer (FRP) composites are typically used in high-performance applications (e.g., aerospace), and their expansion into high-volume industries (e.g. consumer automotive and wind turbine blade manufacturer or similar) is hindered by their cost and a lack of efficient manufacturing techniques. Monitoring the curing process of these composites during manufacturing can improve the efficiency of the process, and therefore reduce the manufacturing cost. Cure monitoring techniques were developed that use probabilistic estimation methods and surface temperature measurements made using infrared cameras. These techniques enable real-time monitoring of the infusion process to locate manufacturing flaws, and they can, potentially, estimate residual stresses in the part. Their commercialization will help facilitate expansion of FRP composites in high-volume industries. (Section 5.7) A 13-meter composite wind blade was produced with Elium® resin and Johns Manville fiberglass; this blade was made with VARTM processing similar to how megawatt-scale wind blades are currently manufactured, but no post-mold heating was used for this thermoplastic composite blade. The wind blade underwent full-scale validation for static loading (4-different load orientations) and flapwise fatigue loading to simulate 20-years of operational loads. The thermoplastic composite wind blade withstood the loading without any noted issues and performed similar to results from a previous full-scale validation to an equivalent epoxy composite wind blade produced with the same blade molds. (Section 5.8) A study was conducted to determine the feasibility of recycling composite wind turbine blade components fabricated with glass fiber reinforced Elium® thermoplastic resin. Dissolution, which is a process unique to thermoplastic matrices, allows recovery of both the polymer matrix and full-length glass fibers, while maintaining their stiffness and strength throughout the recovery process. The economics of recycling is favorable if 50% of the glass fiber is recovered and resold for a process of $\$$ 0.28/kg, and 90% of the resin is recovered and resold at a price of $\$$ 2.50/kg.(Section 10) Recommendations are outlined for commercializing thermoplastic resin for composite wind blade production, in addition to recommended areas for future research.

17 WIND ENERGY↗
DOE OSTI2023

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Verification Testing of OLI Systems Mixed Solvent Electrolyte Model for the Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 ) System to High Ionic Strength at 25°C

This technical report summarizes model verification results and summary statistics for 41 evaporite mineral solubility cases evaluated by Savannah River National Laboratory using OLI Systems’ aqueous electrolyte thermodynamic modeling software. The 41 verification cases containing a total of 60 solubility curves comprise mineral solubility data from low to high ionic strength at 25°C for the eight-component system Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 O as reported by Harvie et al. (1984). Thermodynamic calculations were executed using OLI Systems’ Stream Analyzer computation module within the OLI Studio software platform (Ver. 11.0, Rev. 11.0.1.9). The Mixed Solvent Electrolyte (MSE) thermodynamic framework was chosen for this investigation because of its superiority in modeling high ionic-strength inorganic salt solutions and actinide redox chemistry and solubility, both of which are relevant to the geological repository conditions at the Waste Isolation Pilot Plant in Carlsbad, New Mexico. Mineral solubility data in various inorganic salt solutions were digitized and extracted from figures generated by Harvie et al. (1984). For each of the 60 solubility curves, a case-specific chemistry model and input file were generated in OLI Studio using OLI Stream Analyzer and the MSE (H 3 O + ion) public databank provided by OLI Systems. Model simulation results were exported to Microsoft Excel to calculate summary statistics and to generate graphs comparing the OLI model predictions to the solubility data. Summary statistics include residuals (model – data) and concordance (accuracy × precision, where precision is indicated by the Pearson correlation coefficient and accuracy accounts for bias and scale differential). Private databanks were not developed, and activity coefficient model regressions were not performed to improve OLI model fits to the data. Of the 41 model verification plots, 83% have a mean of the percent residuals less than or equal to 25%. Similarly, 75% display a concordance greater than or equal to 0.75. Only seven of the 41 verification plots fail to show good agreement between the model and data. Of these seven, three are relevant to the WIPP repository because they involve the Mg-OH-Cl-SO 4 -CO 3 aqueous system. The remaining four address salt solubilities at the pH extremes (strong acid and strong base). It should be noted that in two of the three Mg-OH-Cl-SO 4 -CO 3 system cases, the regressed Harvie et al. (1984) solubility curve also deviated from the data. Lack of agreement between the OLI model-predicted solubility curves and the data is attributable to one or more of the following: specific solid species are not included in the OLI MSE databank; there is significant variation among the different solubility datasets chosen by Harvie et al. (1984); the OLI MSE model’s thermodynamic parameters were determined using different solubility datasets; and the activity coefficient parameters for certain relevant ion-ion and ion-molecule pairs have not been optimized via data regression. Two recommendations for future work are to (1) evaluate solubility data for the Mg-OH-Cl-SO 4 -CO 3 system at high ionic strength and, if necessary, develop a private OLI MSE database that includes missing species and, where necessary, regressed standard state properties and interaction parameters; (2) perform similar verification testing of the OLI model for actinide solubility data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Solving independent set problems with photonic quantum circuits

An independent set (IS) is a set of vertices in a graph such that no edge connects any two vertices. In adiabatic quantum computation [E. Farhi, et al ., Science 292, 472–475 (2001); A. Das, B. K. Chakrabarti, Rev. Mod. Phys. 80, 1061–1081 (2008)], a given graph G ( V , E ) can be naturally mapped onto a many-body Hamiltonian H IS G ( V , E ) , with edges E being the two-body interactions between adjacent vertices V . Thus, solving the IS problem is equivalent to finding all the computational basis ground states of H IS G ( V , E ) . Very recently, non-Abelian adiabatic mixing (NAAM) has been proposed to address this task, exploiting an emergent non-Abelian gauge symmetry of H IS G ( V , E ) [B. Wu, H. Yu, F. Wilczek, Phys. Rev. A 101, 012318 (2020)]. Here, we solve a representative IS problem G ( 8 , 7 ) by simulating the NAAM digitally using a linear optical quantum network, consisting of three C-Phase gates, four deterministic two-qubit gate arrays (DGA), and ten single rotation gates. The maximum IS has been successfully identified with sufficient Trotterization steps and a carefully chosen evolution path. Remarkably, we find IS with a total probability of 0.875(16), among which the nontrivial ones have a considerable weight of about 31.4%. Our experiment demonstrates the potential advantage of NAAM for solving IS-equivalent problems.

Science & Technology - Other Topics↗
DOE OSTI2021

A Cooperative Game Theory-based Approach to Under-frequency Load Shedding Control

This paper proposes a cooperative game theory-based under-frequency load shedding (UFLS) approach for frequency stability and control in power systems. UFLS is a crucial factor for frequency stability and control especially in power grids with high penetration of renewable energy sources and restructured power systems. Conventional UFLS methods, most of which are off-line, usually shed fixed amounts of predetermined loads based on a predetermined schedule which can lead to over or under curtailment of load. This paper presents a co-operative game theory-based two-stage strategy to effectively and precisely determine locations and amounts of loads to be shed for UFLS control. In the first stage, the total amount of loads to be shed, also referred to as deficit in generation or the disturbance power, is computed using the initial rate of change of frequency (ROCOF) referred to the equivalent inertial center. In the second stage, the Shapley value, one of the solution concepts of cooperative game theory, is used to determine load shedding amounts and locations. The proposed method is implemented on the reduced 9-bus 3-machine Western Electricity Coordinating Council (WECC) system and simulated on Real-time Digital Simulators (RTDS). The results show that the proposed UFLS approach can effectively return the system to normal state after disturbances.

42 ENGINEERING↗
DOE OSTI2023

Telestream CML File

This is a chronicle file from the Telestream video digitization software that the NSRC Digital Collections Group has installed on the Red. We have not gotten this software to totally function and want to be able to do a workalong with a Telestream representative. One of their technician’s said this file might be helpful to replicate the problem we see in our digitization lab. (The main pointer to the red network that is in this file are file paths. They have been scrubbed so that “valid path to mxf” replaces the real file path.) (CML stands for controlled markup language.)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Human-Building Collaboration: Toward lighting enabled collaborative system design

The profession of lighting design is evolving as contemporary lighting systems increasingly rely on cutting-edge computational technologies, sensors, and IOT systems. This trend requires designers to incorporate ideas of automation, dynamic controls, and user-system interaction into their design logic. However, real-time lighting simulations are constrained by inherent limitations arising from the reductive assumptions inevitably introduced in simulated lighting environments. This raises the question of how can designers account for the discrepancies between simulated and real lighting environments, and how can collaboration between humans and autonomous lighting systems bridge this gap. To address this question, we propose a protocol for designing collaborative interactions between humans and systems. Furthermore, this protocol builds on the Human-Lighting System Interaction Framework and demonstrates how human and system intelligence can be combined to fine-tune lighting qualities in a given space. Our paper shows how interactive lighting systems can customize lighting based on user preferences in real-time and how global lighting configurations can be adjusted over time. Specifically, we demonstrate: a) Human-system collaboration assumptions and goals, as well as how the protocol can be integrated into digitally programmable lighting systems. b) Implementations of collaboration that reveal how system autonomy, performance, and user experience are improved over short and long-term timeframes. c) How lighting design can be enhanced beyond simulation-driven design optimization capacities. The associated affordances and limitations are discussed with respect to existing lighting simulation design frameworks.

autonomous systems↗
DOE OSTI2026

Alchemy: A Model-Based Approach for 2D to 3D Autonomous Nuclear System Design

Engineering design of nuclear power plant (NPP) piping and equipment systems frequently bypasses crucial 2D system planning, instead moving straight to 3D modeling. This often leads to designs that exceed building envelope constraints, forcing expensive and time-consuming redesigns. When 2D modeling is employed, it typically involves labor-intensive manual workflows that convert 2D drawings into 3D models, resulting in inefficiencies and errors across design iterations. These workflows further suffer from poor software interoperability and dependence on proprietary software ecosystems, thereby contributing to schedule delays and cost overruns. This paper presents Alchemy, an autonomous framework that transforms 2D system definitions into Industry Foundation Classes (IFC)-compliant 3D building information models (BIMs) for expediting nuclear facility design at the conceptual preliminary phase. Using a model-based approach, the framework treats the 2D system diagram as the central reference model employed to automatically generate all subsequent outputs, ensuring consistency between the system definition and the resulting physical design. A web-based interface enables engineers to define hierarchical system topologies including associated equipment, geometric properties, and connectivity requirements. A two-phase equipment layout optimization algorithm automatically computes collision-free spatial configurations within predefined building envelopes. An artificial intelligence (AI)-assisted pipe routing module then generates orthogonal, collision-free routing paths, allowing the user to select either an A* search-based method or an Ant Colony Optimization (ACO)-based method. All outputs are authored natively in IFC format, relying on open-source technologies and standardized formats in order to ensure extensibility and eliminate proprietary software dependencies. The proposed framework is validated on two representative pressurized-water reactor (PWR)-based case studies, for which it autonomously generates IFC-compliant 3D models in minutes, drastically reducing workflows that typically require hours of manual effort. The generated model demonstrates topologically correct equipment placement, physically plausible spatial relationships, and collision-free pipe routing consistent with known PWR loop configurations. This work represents a foundational step toward digital engineering for nuclear facility preliminary design, with future ongoing development targeting design code compliance and expanded system complexity.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Updated Void Catalogs of the SDSS DR7 Main Sample

Abstract We produce several public void catalogs using a volume-limited subsample of the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Using new implementations of three different void-finding algorithms, VoidFinder and two ZOBOV-based algorithms (VIDE and REVOLVER), we identify 1163, 531, and 518 cosmic voids with radii >10 h −1 Mpc, respectively, out to a redshift of z = 0.114 assuming a Planck 2018 cosmology, and 1184, 535, and 519 cosmic voids assuming a WMAP5 cosmology. We compute effective radii and centers for all voids and find none with an effective radius >54 h −1 Mpc. The median void effective radius is 15–19 h −1 Mpc for all three algorithms. We extract and discuss several properties of the void populations, including radial density profiles, the volume fraction of the catalog contained within voids, and the fraction of galaxies contained within voids. Using 64 mock galaxy catalogs created from the Horizon Run 4 N -body simulation, we compare simulated and observed void properties and find good agreement between the SDSS DR7 and mock catalog results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Partial Support of the Fast-Track Consensus Study on Foundational Research Gaps and Future Directions for Digital Twins (Final Report)

This study from the National Academies of Sciences, Engineering, and Medicine was launched to explore the foundational research gaps and opportunities for digital twins. As part of the information gathering process, the committee organized three targeted workshops—in engineering, climate sciences, and biomedical sciences—to better understand domain-specific nuances and barriers to developing digital twins. These sessions enabled cross-sector experts to surface field-specific needs, challenges, and open questions related to digital twins. Thousands of participants across multiple domains engaged in the discussions, which workshops were summarized in three separate Proceedings-in-Brief.

42 ENGINEERING↗
DOE OSTI2022

Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities

Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high-throughput automated approach to univariate nonparametric density estimation based on maximum entropy and order statistics, improving accuracy over univariate KDE. This article presents an extension of the single variable case to multiple variables. The univariate estimator is used to recursively calculate a product array of one-dimensional conditional probabilities. In combination with interpolation methods, a complete joint probability density estimate is generated for multiple variables. Good accuracy and speed performance in synthetic data are demonstrated by a numerical study using known distributions over a range of sample sizes from 100 to 10 6 for two to six variables. Performance in terms of speed and accuracy is compared to KDE. The multivariate density estimate developed here tends to perform better as the number of samples and/or variables increases. As an example application, measurements are analyzed over five filters of photometric data from the Sloan Digital Sky Survey Data Release 17. The multivariate estimation is used to form the basis for a binary classifier that distinguishes quasars from galaxies and stars with up to 94% accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗