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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mississippi's Strategic Resilience: A multi-systems approach to secure, reliable, and adaptable electric grid infrastructure

Mississippi’s electric grid resilience challenges are linked to an intersection of complex socioeconomic, ecological, technological, historical, and political challenges, exacerbated by increasing severe weather like flooding and tornado events. The state’s legacy of underinvestment in critical energy infrastructure, particularly in rural areas and vulnerable floodplains, have stressed an aging grid, creating long-lasting disruptions in electric service during weather-related outages. Effective emergency management and preparedness is further hampered by a lack of coordination across local, county, and regional scales. Using the TASTI-GRID platform and partnership with Oak Ridge National Laboratory (ORNL), Mississippi is developing a comprehensive regional resilience strategy to overcome energy security and reliability challenges, mitigating the impacts of natural hazards, and positioning Mississippi as a resilient and premier destination for residents, businesses, and economic development.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Additive Manufacturing Evaporative Casting

Traditional lost foam casting has been around for decades. The process uses foam forms blown by an injection molding like process or CNC milled into the desired shape, then pouring molten metal over the foam pattern to create a metallic object. Additive Manufacturing Evaporative Casting (AMEC) is a new process that eliminates foam forms by using additive manufacturing to 3D print the desired cast geometry. This not only saves time and money but allows for more advanced and complex designs that can’t be achieved by carving foam. Additionally, AMEC doesn’t require molds and tooling like other casting and foundry options. Because the AMEC process is new, extensive testing is needed to develop a better understanding of the process to minimize defects, quantify material properties, and start computer modeling for the process. This CRADA (collaborative research and development agreement) between ORNL and Skuld seeks to improve the process, develop a computational model, characterize material properties, and explore new applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Triggerable adhesives with infinite working life for large area application

Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF) entered a User Proposal with Perseus Materials in Knoxville, TN. By reinventing how composites are made, we unlock a new era of structural materials: faster to produce, easier to assemble, and strong enough for real-world scale. Perseus Materials was targeting the wind turbine industry to make large adhesive joints for composite turbine blades without the limitations of cure time and room temperature cure without the needs to have work times. The triggerable aspect would increase manufacturing rates for composite wind turbine blades.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Five PMI Isolates from Populus Deltoides and Populus Trichocarpa

Five bacterial isolates were isolated from the roots of poplar trees (Populus deltoides and P. trichocarpa), which are model organisms and a main focus of the Plant-Microbe Interfaces (PMI) project at ORNL. These strains belong to genera are not well represented and give a more complete view of the microbial community and bacterial interactions with poplar trees. These strains will support future studies and contribute to the broader PMI goal of understanding microbe-microbe and plant-microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ramen

Many computationally unintensive models for material process-structure-property connections exist in the literature (e.g. journal articles and textbooks). However, to our knowledge there is no existing compilation of these models in one package. Many industrially and research relevant workflows involve combinations of many of these models and if the models have inconsistent interfaces, building these workflows can be challenging. Ramen provides a collection of computationally unintensive models for calculating material process-structure-property relationships from the literature, as well as related tools (e.g. for plotting results). These models use a consistent interface structure and all leverage material data files and data structures from the Mist library (also developed at ORNL). Ramen provides a structured package for combining many standard models in one place, with a standardized interface. This approach makes combining models in long/complex chains easy and straightforward.

DeWitt, Stephen [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Assess The Water Resistance And Thermal Performance Of Pre-flashing Methods When Adding Continuous Insulation During Re-siding (AIRS)

Retrofitting existing buildings by adding continuous insulation during re-siding projects has become a popular method for enhancing energy efficiency, especially given the aging building stock and stricter energy codes. When properly integrated with existing window systems, continuous insulation can significantly improve thermal performance, but it also presents challenges related to water resistance and building envelope integrity. Research indicates that the interface between windows and wall assemblies is critical as improper installation or sealing can lead to water intrusion, materials deterioration, and energy loss. Moreover, studies show that fully integrating the windows with the insulation layer can reduce window heat loss by up to 40%, emphasizing the importance of optimizing window placement and sealing during retrofitting to ensure both energy efficiency and structural performance. In this study, we conducted experimental tests to evaluate the water penetration and thermal performance of two window types: an aluminum window with a 2-inch installation fin and a wood window, representing typical mid-20th century designs. Using the Heat, Air, and Humidity (HAM) chamber at Oak Ridge National Laboratory (ORNL), we assessed bulk water penetration and performed COMSOL analysis for thermal flux and examined the effectiveness of different pre-flashing methods, i.e., standard self-adhered flashing tape and high-performance flashing tape with low-expansion foam, to enhance water resistance. The results indicate that applying proper flashing techniques and moisture management strategies can mitigate these risks, improving the overall performance and durability of retrofitted buildings. Additionally, proper sealing during retrofitting is essential, as improper installation can lead to moisture issues that compromise both energy efficiency and structural integrity.

Shen, Zhenglai [ORNL]↗
DOE OSTI2025

Compressed Air Scoping Tool Teaches Energy Efficiency Best Practices

Standards and practices around compressed air systems are evolving as previous golden rules become outdated and unhelpful. One of the most referenced guidelines gives us the perfect example: The previous rule of thumb for compressed air storage – 1-3 gal/cfm1,2 – is now accepted to be 3-5 gal/cfm. This change in recommended storage is only one of many in the industry, resulting in industrial users lost in a sea of contradictory best practices. To help combat confusion and poor information sharing, a collaborative effort between Oak Ridge National Laboratory (ORNL) and the Department of Energy’s (DOE’s) Better Plants program created the Compressed Air (CA) Scoping Tool with the most up-to-date best practices in the industry (as of 2023). It can be used as a nonbiased, one-stop shop for best practice recommendations. The Excelbased tool is designed to be an initial step in understanding the operation of a compressed air system, a baselining tool enabling users to comprehend various aspects of a facility’s system from the production of compressed air to its application by end users.

42 ENGINEERING↗
DOE OSTI2024

Assessment of the CTF subchannel code for modeling a large-break loss-of-coolant accident reflood transient

With increased industry interest in extending reactor operating cycles, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has been investigating the behavior of high-burnup fuel during design basis accidents such as the large-break loss-of-coolant accident (LBLOCA) with consideration for risk of fuel fragmentation, relocation, and dispersal (FFRD). As part of that activity, the NEAMS subchannel thermal/ hydraulics (T/H) code, CTF, is being used for modeling of LBLOCA and to determine the impact of subchannel resolution on results. Although CTF includes a wide range of models for LBLOCA conditions, the code has not been used for this application while maintained at Oak Ridge National Laboratory (ORNL) until now. Therefore, here, in this work, a preliminary assessment of several of these models was performed using openly available reflood experimental data from the Flooding Experiments in Blocked Arrays (FEBA) tests. One coarse mesh and one fine mesh model were set up in CTF for high and low flooding rate tests performed in the unblocked FEBA facility. A coarse TRACE model was set up to be as consistent as possible with the coarse CTF model to allow for code-to-code benchmarking. The assessment shows a tendency of the codes to over-predict peak cladding temperature (PCT) near the top of the bundle and to quench early. Advanced spacer grid models were shown to improve upper bundle predictions in CTF. The resolved CTF model over-predicted PCT by a larger degree in the center channels in the low-flooding rate test, and it is believed that the radiative heat transfer model, which was not used in this study, may be needed to correct this over-prediction. Finally, this work demonstrates the importance of the droplet model in determining quench time and vapor temperature and PCT prediction, which necessitates a more in-depth validation of these models in the future.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

A Ce-Doped LiI Scintillator Film-Based High Rate, High Spatial Resolution Neutron Anger Camera for Neutron Scattering Facilities

The 2-D position sensitive neutron detectors featuring a significantly improved count rate capacity and a sub-mm spatial resolution are in high demand by neutron scattering facilities, especially the next generation high-flux spallation neutron sources (SNSs). They are anticipated to play a crucial role in fully utilizing the unprecedentedly high neutron brightness offered by these sources. In this work, a high rate and high spatial resolution neutron Anger camera has been successfully developed based on a Ce-doped microcolumnar LiI scintillator film and the PETsys TOFPET2 ASIC readout electronics. The performance evaluation was conducted in a standard laboratory environment as well as the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) with regard to light yield, γ-ray sensitivity, neutron detection efficiency, count rate capability, spatial resolution, and position linearity. The LiI:Ce scintillator was found to have a higher light yield and a lower γ-ray sensitivity than GS20 (i.e., 11729 versus 6000 photons/n and 1.67 × 10 −5 versus on the order of 10 −4 ). The neutron detection efficiency was measured to be 63% at 4.2 Å. This camera demonstrated an exceptional count rate capability, up to at least 481 kHz without rate loss. The spatial resolution was as good as 0.47 mm. Here, the position linearity was also enhanced.

High rate↗
DOE OSTI2026

Automated, reliable, and efficient continental-scale replication of 7.3 petabytes of computational simulation data: A case study

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) computational simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee—a task motivated by a need for increased reliability, capacity, and performance. This task presented significant challenges: the need to move 29 million files twice under time pressure from aging storage hardware; a source file system bottleneck limiting throughput to 1.5 GB/s; frequent site maintenance windows; and the need for complete reliability at scale. We addressed these challenges using a simple replication tool that invoked Globus to transfer large bundles of files while tracking progress in a database, dynamically rerouting transfers to work around maintenance periods and file system limitations. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure. The replication tool is available at https://github.com/esgf2-us/data-replication-tools.

Globus↗
DOE OSTI2022

Frontier (HPE Cray EX) Exascale Supercomputer at the Oak Ridge Leadership Computing Facility

Frontier is the HPE Cray EX exascale supercomputer deployed and operated by the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL). Frontier is designed for large-scale modeling, simulation, and AI workloads and is built from HPE Cray EX system architecture with AMD CPUs and AMD Instinct GPU accelerators connected by the HPE Slingshot interconnect. System composition (representative production configuration): Frontier is composed of approximately 74 cabinets with 128 compute nodes per cabinet (~9,400 compute nodes total). Each compute node contains one 64-core AMD EPYC CPU and four AMD Instinct MI250X GPUs. Nodes are connected using HPE Slingshot (Slingshot-200 class) networking with multiple NIC ports per node providing high injection bandwidth. Frontier is connected to the Orion parallel file system (multi-tier Lustre) providing a large, center-wide high-performance storage namespace. Operational context: Frontier entered public prominence as the first system to reach No. 1 on the TOP500 list in May 2022 (HPL benchmark), establishing the first widely recognized exascale-era performance milestone. The system supports DOE Office of Science mission workloads and enables leadership-class computational science and AI for open science users.

AMD EPYC↗
DOE OSTI2026

Oak Ridge National Laboratory Building Envelope Library (ORNOBEL)

The Oak Ridge National Laboratory Building Envelope Library (ORNOBEL) is a collection of dense exterior building-facade point clouds acquired using a survey-grade terrestrial laser scanner. Each file represents an individual facade from a building on the Oak Ridge National Laboratory (ORNL) campus or in Knoxville, Tennessee, with an average point-cloud resolution of approximately 3 mm. The points in each facade are semantically labeled into three classes: (1) window/door, representing openings in the building envelope; (2) wall, representing planar opaque envelope surfaces; and (3) other, representing the remaining facade-adjacent elements, architectural features, and protrusions. ORNOBEL supports the development, training, and evaluation of advanced deep-learning methods for automated building-envelope segmentation, geometric reconstruction, and building information modeling (BIM).

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗
DOE OSTI2025

Development of Engine Valve Materials for Next Generation Higher Efficiency Engines

The growing demand to increase the performance and efficiency of light-, medium-, and heavy-duty engines continues to drive increases in combustion intensities and cylinder pressures, which result in higher exhaust gas temperatures. Thus, there is a critical need for new materials that can meet the performance and cost targets for components such as exhaust valves which are exposed to these higher exhaust gas temperatures. Oak Ridge National Laboratory (ORNL) has developed several lower-cost, high-strength alloys that have the potential to be adopted into intake and/or exhaust valves in the next generation, high-efficiency engines and other high temperature applications. These alloys are covered by two issued patents.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Improved Chopper System for a Direct Geometry Spectrometer at the Second Target Station

This report describes a chopper cascade suitable for a cold direct geometry spectrometer at a pulsed neutron source with a low repetition rate such as the Second Target Station (STS) currently being planned at Oak Ridge National Laboratory (ORNL). The chopper cascade offers the following capabilities: selection of a variable number of pulses per frame (flexible pulse suppression), selection of pulses with increasing timing separation within one frame, and an increasing pulse width ∆t within one frame, in order to achieve an even energy resolution over several pulses at different wavelengths across the frame.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Additively manufacturing high-temperature aluminum alloy for automotive engine pistons

The purpose of the project was to evaluate DuAlumin-3D, an ORNL designed Al alloy, for additive manufacturing of high-performance automotive components. The project consisted of additively manufacturing a cylindrical block of representative material which was then heat treated before machining into test coupons. Testing included tensile tests as a function of temperature, high-cycle fatigue testing as a function of temperature, density and elastic modulus measurements, and thermophysical property measurements. Fractography was also performed on failed high-cycle fatigue specimens. The results were compared against benchmark AM Al alloys and current high-performance conventionally manufactured alloys. Overall, the DuAlumin-3D material outperformed the incumbent materials in mechanical properties, especially at elevated temperatures, while maintaining broadly similar thermophysical properties. The results of this study support further maturation of the material for additive manufacturing of high-performance automotive components.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrating Machine-learning-assisted Computer Vision with RICH System

Developments in artificial intelligence have vastly expanded the capabilities of robots. Currently, the Spallation Neutron Source (SNS) beamlines at Oak Ridge National Lab (ORNL) have robotic sample loaders to increase the efficiency of running experiments. However, they require retraining if anything about the situation changes, e.g., where the samples are, and cannot notice if errors occur. So, the viability of using computer vision and machine learning to enhance these sample loaders’ functionality was investigated. In this project, the RICH system with a Dobot CR3 6-axis robot present at the VULCAN beamline assisted by an Intel Realsense D435i camera, a unique camera that enables convenient translation of 2D pixel coordinates to 3D world points, was programmed to load ceramic crucibles into a thermogravimetric analyzer (TGA) furnace. An algorithm was constructed in Python with three major phases planned: (1) obtaining a sample, (2) moving it to the target location, and then (3) bringing the sample back to its original location once the experiment finished. In the first phase, the algorithm would dynamically detect sample locations using ArUco markers to recognize the samples’ general location and a custom-trained yolov5 object detection model to locate the crucibles’ centers. Afterward, the robot would be directed to pick up samples based on the crucibles’ calculated positions. In the second phase, the robot would move the sample to a secondary point, reorient its grip, and place the sample at the target location. In the final phase, the robot would determine whether the sample was intact and would bring it back to its original place if it was or raise an alarm. Using this algorithm, the robot was able to pick up different types of crucibles at varying positions. These results indicate that integrating machine-learning-assisted computer vision with robotic sample loaders can result in effective autonomous detection of samples.

97 MATHEMATICS AND COMPUTING↗