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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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Motivation and recent work to develop Surface Acoustic Wave devices and related systems for in-situ gas monitoring and corrosion by proxy.
In order to improve the long-term performance of post-combustion carbon capture technologies, improvements to the detection of solvent degradation or reduction in CO2 capture efficiency are necessary. A pair of sensors were deployed to the National Carbon Capture Center’s (NCCC) to monitor CO2 capture and solvent degradation. The CO2 sensors were located to measure before capture and after capture concentrations of CO2 to measure the capture efficiency of the solvent system. Amine degradation sensors were located at four locations along the main solvent lines corresponding to CO2 loading and temperature: hot rich, cold rich, hot lean, and cold lean. Experimental results from laboratory tests using the sensors are shown, along with prototype designs that were deployed to the field site.
In order to test and monitor the operational stability and conditions of various energy, and manufacturing systems and their components, development of accurate sensors capable of operating at temperatures in excess of 1000 °C in various environments for long durations is required. In addition, many of these harsh-environment systems do not permit sensors to be directly inserted, so these sensors need to be embedded into thermal protective materials. In this work were developed high-temperature embedded thermocouples composed of various doped lanthanum chromites-refractory based composites synthesized by a mixed-oxide route and directly embedded into a Al2O3 based refractory. The electrical properties were specifically manipulated by altering the level of percolation of the conductive phase within the refractory constituent. Chemical stability of sensors was determined by phase development analysis by X-ray diffraction and X-ray photoelectron spectroscopy; Seebeck coefficients were determined by thermoelectric voltages measurements at temperature up to 1400 oC.
In order to develop improved methods for characterization and monitoring of hyporheic exchange along stream and river corridors, this project evaluated and compared concentration (C) and discharge (Q) correlations between the dam-managed, intermittent Rio Grande River and the perennial East Fork Poplar Creek in Oak Ridge, Tennessee. Additionally, a multimethod geoelectric survey was implemented between January and March 2022 along a 220-meter long reach of the bedrock-lined East Fork Poplar Creek in Oak Ridge, Tennessee to identify locations of surface-water and groundwater exchange in the bedrock-lined streambed and characterize the subsurface flow paths that convey water between the stream and flood plain.
DOE’s Integrated Field Laboratories (IFLs) require a vast array of measurements to measure urban air quality. Nitrogen dioxide (NO2) and formaldehyde (HCHO) are ubiquitous pollutants with numerous sources, most notably vehicle exhaust. High-spatial density measurements are critical to understanding how certain neighborhoods are more adversely impacted by pollutants. This report describes the results of a Phase I SBIR project to develop and demonstrate a rapid, affordable, low-power monitor to provide high spatial resolution mobile measurements of formaldehyde and nitrogen dioxide at street-level, where people live and walk. The spatial density will be achieved via rapid measurements on a mobile platform. This sensor will quantify spatial disparities in cities of the toxics, HCHO and NO2, as well as carbon monoxide and carbon dioxide for better source identification. These species are detected in the mid infrared by laser absorption spectroscopy, and fiberoptics are used. Tasks accomplished include identification and testing of the appropriate spectroscopic regions; coupling of laser light into a fiberoptic system; development of software to control miniature electronics to scan and control two lasers simultaneously; coupling of the mid infrared fiber into a multipass cell for enhanced pathlength and sensitivity; characterization of sources of instrument noise; and design work for a Phase II prototype.
This report summarizes the activities of this project, which aims at design and testing a scalable, robust, and online framework that provides secure monitoring of photovoltaic generation in the face of potential cyber-attacks.
OUTLINE • Six Fuel Cycle Topics • SRNL Process Modeling • Tritium Accountancy • Real-Time Monitoring Framework Proof-of-Concept Study • Development of the Accountancy Framework • Sensor Assessment In Relation to the DT Fuel Cycle • Summary
Our project explores a new solution to complement current safeguards toolbox by independently, and noninvasively, determining the pressure inside uranium hexafluoride (UF 6 )-carrying Gas Centrifuge Enrichment Plant (GCEP) pipes. Proof-of-principle, non-invasive pipe pressure monitoring tests were conducted through measurements of hoop and axial strain using an optical-interference dilatometry technique based on fiber Bragg gratings (FBGs) inscribed in the core of telecommunication-type 125 µm diameter optical fibers. The fibers were affixed to commercial stainless steel and aluminum tubes and pipes of the type expected to be found at GCEP’s, each measuring four inches in diameter. When the internal air pressure in the pipes changes their external diameter and length follow, and the pitch in the FBG shrinks and/or expands accordingly. In turn, these changes result in a shift of the light wavelength reflected by the FBGs. Several FBGs physically attached to the pipe, as well as control FBGs not attached, were interrogated during the process using state-of-the-art commercial interrogators that operate on swept-wavelength laser technology. Strains measured were contrasted against calculations based on the known elastic and mechanical properties of the pipes. Good agreement is found, and strategies are proposed for further improvement of the experimental resolution as well as future implementation of this novel approach for GCEP safeguards.
Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.
The report documents the design of the Integrated Data AcQuisition (IDAQ) system and observations recorded during the first in a series of underground chemical explosions conducted on the Nevada National Security Site (NNSS) in southern Nevada. Experiments are funded as part of Low Yield Nuclear Monitoring (LYNM) research and development within the United States National Nuclear Security Administration NA-22 nuclear non-proliferation program. The series is part of the broader Physical Experiment 1 (PE1) being conducted in and around the P-tunnel facility on the NNSS. Each explosive experiment utilizes several tons of comp-B to generate signals recorded by a broad suite of instrumentation. The IDAQ serves as the backbone for all subsurface instrumentation providing precise time synchronization, remote control, data exfiltration and backup, along with recording several sensing modalities throughout the underground complex that includes ground motion, environmental conditions, and electromagnetic signals.
The Hanford Tank Waste Treatment and Immobilization Plant Effluent Management Facility (EMF) stack monitor location was qualified using a combination of scale model stacks to mitigate the risk of identifying that the sampling location does not meet the qualification criteria on the full-scale stack. The LV-S1 scale model stack was used as a baseline, augmented by the LB-S1 and LV-S2 scale model stacks to address the Direct Feed Low Activity Waste Effluent Management Facility Vessel Vent Process (DVP) injection into the main Active Confinement Ventilation (ACV) system duct. As required by the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999 standard, the scale model and its sampling locations were geometrically similar to the actual stack, and the Reynolds numbers for both the actual and model stacks were >10,000. An additional criterion is that the product of the hydraulic diameter and mean velocity (DV) of the full-scale stack must be between 1/6 DV and 6 DV of the scale model stack tests. The LV-S1, LB-S1, and LV-S2 scale model stack tests have met the criteria of the ANSI/HPS N13.1-1999 standard to demonstrate the stack sampling locations are well mixed. Verification tests of the EMF stack were performed at normal operating conditions. The minimum 1/6 DV value and the maximum 6 DV value from the scale model testing determine the range of stack flow rates for which the full-scale stack may be operated while remaining in compliance with the stack verification criterion. A practical range for the full-scale stack qualification uses the average DV through 6 DV from the scale model tests to compute the corresponding flow rates. Table S1 lists the operating flow rate along with the average and maximum qualified stack flow rate based on the LV-S1 scale model DV values. The operating flow is below the maximum qualified stack flow, which means that the scale model test results are acceptable for stack qualification. The remaining criteria for the stack verification to be considered valid involve the flow angle and velocity uniformity results. First, the flow angle at the full-scale stack must be ≤20°. Second, the velocity uniformity at the full-scale stack must be ≤20% coefficient of variance (COV). Finally, the velocity uniformity results for the actual and scale model stack tests must agree within 5% COV. These criteria were met through the full-scale stack test at the EMF. Flow angle results were <5°; all flow angle results were within the ≤20° criterion. The velocity uniformity results for each test condition ranged between 2.2% COV and 4.3% COV, all of which were within the range of the target % COV values from the scale model tests on the LV-S1, LB-S1, and LV S2 scale models. Based on these stack verification test results, the EMF filtered exhaust stack sampling location meets the qualification criteria provided in the ANSI/HPS N13.1-1999 standard for all planned fan operating configurations. This includes each combination of ACV fans with DVP exhausters. Further changes to the system configuration or operating conditions that are outside the qualified flow rates described in this report may require additional tests or analyses to determine compliance with the standard.
This report presents design plans for integrated methane monitoring platforms for the oil and gas sector, which include satellites, aircrafts, drones, mobile platforms, open-path systems, sensor networks, LDAR techniques, and other systems.
Presentation discussing career paths in chemistry as well as how chemical research is applied to address real-world problems. Here, a variety of different materials (metal-organic frameworks, carbon dots, thin films, etc.) are used as luminescent sensors to detect trace quantities of economically critical metals, which are used in applications ranging from renewable energy to national defense. In an application particularly relevant to Western Pennsylvania, the sensor technologies are deployed in coal utilization byproducts including fly ash leachates and acid mine drainage. Taken together, this research presents an exciting path towards reducing the characterization costs associated with metals prospecting and process monitoring, facilitating domestic production of these metals.
Development of robust, transparent, and precise monitoring, reporting, and verification (MRV) technologies and practices is critical for carbon dioxide removal (CDR) project developers to comply with regulatory and permitting requirements, voluntary carbon market (VCM) protocols, and to ensure safety while reducing environmental impacts. Enhanced rock weathering (ERW)-based CDR technologies focus on removing atmospheric carbon through conversion into thermodynamically stable solid or aqueous carbonate forms for permanent storage (i.e., mineralization). This highly durable form of CDR enhances naturally occurring silicate rock weathering cycles by optimizing application of finely-ground silicate rock particles (i.e., from basalt) on terrestrial agricultural lands to accelerate natural silicate rock weathering and mineralization. Enhanced rock weathering may also provide improved crop yields and enhance soil health. A critical aspect for commercialization of these technologies is the development of MRV to quantify the net removal and durable storage of atmospheric CO 2 . For ERW systems, it is essential to accurately characterize the mineral feedstock selected for application to establish the baseline geochemical composition, mineral dissolution rates, and carbon removal potential of the feedstocks to estimate overall net removal. Given the difficulty with conducting MRV for ERW in diverse soil/environment types, over large application areas, and due to complex chemical reaction networks, this project will accelerate understanding towards consensus on best practices for MRV. The overall objectives of the proposed voucher project are to: 1) Characterize and analyze feedstock(s) intended for ERW field application by Lithos Carbon (“Voucher Recipient”/ “CRADA Participant”) to determine overall mineralization potential; 2) Facilitate knowledge transfer and documentation of experimental protocols, instrumentation, and other relevant best practices; and 3) Support the Voucher Recipient’s broader technology commercialization and ERW Research Facility development plans. This work will align with the Voucher Recipient’s MRV plans for field sites and build upon complementary efforts conducted by PNNL on mineralization MRV.
Effective border security is essential for maintaining national safety and managing immigration. This helps prevent illegal activities such as smuggling, trafficking, and unauthorized entry. Traditional methods of border monitoring are heavily reliant on human patrols which can be inadequate given the cost and labor-intensity given the challenging terrain involved. This report presents an advanced unmanned solution designed to significantly enhance border security through currently used drone technology using integrated autonomy by the adoption of ORNL’s sophisticated software platform known as Mapster-Nomad.
Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.
A/SMRs will likely be deployed in remote locations that are difficult to access, thereby requiring limited on-site staff. • Vendors are considering remote monitoring as a solution to enhance nuclear security. RFTIDs are a candidate technology for maintaining nuclear security of A/SMRs but need to be evaluated for feasibility and implementation into the wider physical protection system.
This report describes our progress in developing a scalable wireless metering system that integrates plug-load energy meters that measure real, reactive, and apparent power along with load monitoring based on extracting high-fidelity electrical waveform features collected at every load to capture power profiles and automatically identify and categorize MELs, and make this technology available to the research, commercial, and government sectors.