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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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The Center for Nonlinear Studies (CNLS) seminar focus is on quantum sensing for room-temperature NMR, magnetic material, living system (e.g., C. elegance), gravitational wave detection, and dark matter.
Significant cost savings can be made if electrical cables can be replaced by wireless technology in current Nuclear Power Plants (NPP) and in advance reactor designs. Wireless technology can also provide in-core opportunities by significantly reducing the number of penetrations in the pressure vessel, cost and complexity of sensor installation and by increasing the efficiency of current and advanced reactors. Unlike other deployment scenarios for an industrial environment, operators need to have centralized control over all the networks. Centralized control will reduce implementation costs, provide single point control and enable monitoring of network devices, improve security, and enhance connectivity. Micro-sensors that can simultaneously monitor temperature and pressure within a fuel rod inside nuclear reactors will enable preventative actions during abnormal operating conditions. This ability could avert accidents and enable the expedient development of accident tolerant fuels. A novel micro-sensor suite (~ mm) to simultaneously measure multiple parameters such as temperature, strain, pressure, and neutron/gamma flux inside a fuel rod is being developed for use in reactors. The necessary communication architecture is also being developed to transmit measurement signals from the core to the plant's data cloud or control room. A three three-tier strategy has been developed to support wireless transmission of in-core measurements to the control room or to a secure cloud platform for control, analytics, and decision-making purposes. 1. In-core: data signal from in-core to outside of the pressure vessel within the containment building 2. Containment building: data signal from inside to the outside of the containment building and into the balance of the plant network 3. Balance of the plant network: information transmitted to the data cloud and control room This plan presents a wireless sensing and communication system for use within a reactor core and elsewhere. The communication technology is advantageous to compensate for network equipment failures and adverse data transmission conditions. Wireless technology will significantly increase the resiliency of the plants network system. The wireless system naturally provides multiple transmission path capability and data redundancy.
The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. MRCSP conducted analysis of CO 2 flow patterns using distributed temperature sensing (DTS) data acquired from the Chester 16 pinnacle reef located in Otsego County, Michigan. The reef has one injection well Chester 6-16 and one monitoring well Chester 8-16 where DTS is installed. DTS provides a means to track temperature within the wellbores in real time and characterize the migration patterns of CO 2 in the subsurface when DTS data is combined with other operational data such as the injection rates, bottomhole pressures and temperature. The primary objective of this study was to determine the distribution of CO 2 inflow zones in the A1 Carbonate and Brown Niagaran formations.
Final Report for the DOE EXPRESS 2022 project "Zero-noise Extrapolation for Quantum Sensing." Contains a description of the motivation, accomplishments, and implications of the technical work performed.
The development of an economically competitive domestic supply of rare earth elements and yttrium (REY) is necessary for our nation’s economic growth and national security. The achievement of a secure domestic supply of REY requires not only the development of effective processes for recovery of REY from naturally occurring materials and/or recycled products, but also the development of downstream processes for the ultimate production of high-REY content solids. An impediment to the development of such processes is the scarcity of analytical methods that provide rapid determination of the process stream compositions. In this work, a membrane-based extraction process was used to selectively recover REYs from a dilute solution in the presence of much higher concentrations of Ca and Al. In tandem, the use of a portable spectrometer equipped with an immobilized zinc adeninate benzene tricarboxylate metal-organic framework sensing material makes possible the rapid detection of the presence of ppm concentrations of Tb and Eu in both weakly acidic and strongly acidic process streams within minutes. A solvent extraction processing time of 15-60 min maximized REY selectivity over gangue ions while achieving up to 80% REY and minimal gangue ion recovery. Taken together, these experiments highlight not only an innovative method for REY purification but also the importance of inexpensive, portable characterization methods for near real-time analysis of REY content.
Develop and validate a displacement discontinuity model and inversion framework using distributed strain sensing (DSS) for real-time fracture monitoring and improved localization in subsurface applications.
The developed low-cost prototype sensing system successfully detects hydrogen concentrations across a wide range, demonstrating immediate feasibility for commercial deployment. Utilizing fiber-optic technology, the system delivers stable, reproducible performance from parts-per-million levels up to 100% pure H2 environments. Environmental and chemical cross-sensitivity evaluations confirm robust selectivity against methane and temperature changes, establishing its readiness for near-term commercialization.
This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.
Metal–organic frameworks (MOFs) have been broadly applied to numerous domains with a substantial surface area, tunable pore size, and multiple unsaturated metal sites. Recently, hollow MOFs have greatly attracted the scientific community due to their internal cavities and gradient pore structures. Hollow MOFs have a higher tunability, faster mass-transfer rates, and more accessible active sites when compared to traditional, solid MOFs. Hollow MOFs are also considered to be candidates for some functional material carriers. For example, composite materials such as hollow MOFs and metal nanoparticles, metal oxides, and enzymes have been prepared. These composite materials integrate the characteristics of hollow MOFs with functional materials and are broadly used in many aspects. This review describes the preparation strategies of hollow MOFs and their composites as well as their applications in organic catalysis, electrochemical sensing, and adsorption separation. Finally, we hope that this review provides meaningful knowledge about hollow-MOF composites and their derivatives and offers many valuable references to develop hollow-MOF-based applied materials.
Fluorescence-based phosphate sensing using phosphate-sensitive phosphors is a promising approach for in situ monitoring of phosphate pollution in waterways and reservoirs. To date, the most sensitive phosphor developed for this purpose is Tb(cpboda)(DMF) 2 , where cpboda = (3,3'-((5-Carboxy-1,3-phenylene)bis(oxy))dibenzoic acid). In this study, we further improve this sensitivity by replacing the Tb 3+ ions with Eu 3+ ions to make Eu(cpboda)(DMF) 2 and find concentration-independent phosphate-sensitivity of 1570 ± 120, which is ≈8× more sensitive than the Tb-version. This improvement is attributed to Eu 3+ having a hypersensitive transition, while Tb 3+ does not. Additionally, we characterize the phosphor’s optical properties, photodegradation, and water solubility. We find that the phosphor presents challenges with regards to both photodegradation and solubility, as it is found to be poorly soluble in water and is quickly photodegraded under UV radiation <360 nm. However, these obstacles can, in theory, be overcome with the use of direct excitation of the Eu 3+ ions at 394 nm and careful design of an analysis instrument to reduce concentration variations.
Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.
Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
We examine the application of guided waves on a single conductor (Goubau waves) for sensing. In particular, the use of such waves to remotely interrogate surface acoustic wave (SAW) sensors mounted on large-radius conductors (pipes) is considered. Experimental results using a small-radius (0.0032 m) conductor at 435 MHz are reported. The applicability of published theory to conductors of large radius is examined. Finite element simulations are then used to study the propagation and launching of Goubau waves on steel conductors up to 0.254 m in radius. Simulations show that waves can be launched and received, although energy loss into radiating waves is a problem with current launcher designs.
Tower Distributed Temperature-Sensing (DTS) - channel 3
Tower Distributed Temperature-Sensing (DTS) - channel 2
Tower Distributed Temperature-Sensing (DTS) - channel 1
Tower Distributed Temperature-Sensing (DTS) - channel 4
Using airborne hyperspectral remote sensing data from NASA Airborne Visible-Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) platforms in a region near NGEE-Arctic intensive watersheds at Seward peninsula of Alaska, high resolution (5m) maps of plant community distribution were developed and included in this data collected. AVIRIS-NG data collected over 2017-2019 period were used to develop deep neural networks, trained using vegetation plot observations collected at NGEE-Arctic watersheds at Kougarok, Council and Teller. A hierarchical vegetation classification scheme consisting of six classes at Level I, and 16 classes at Level II contained in two .txt files were used to developed the plant community maps for the region. Two geospatial raster data files (.tif) at both thematic levels are shared in this data collection. Data files in this collection use Alaska Albers Equal Area projection. Readme files available in three formats (*.html, *.md, *.pdf) and one *.png visualization map.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).