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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

“Innervated” Pipelines: A New Technology Platform for In-Situ Repair and Embedded Intelligence

The overall vision pursued under the project would ultimately enable capability for real-time operational monitoring of natural gas (and other) pipeline infrastructures through the combination of in-situ repair and rehabilitation methods with embedded fiber optic sensing and associated data analytics methods and platform tools. Benefits to the public include more resilient and robust natural gas pipeline infrastructures with the potential to expand the applications of developed technologies under the program to other important areas of critical energy infrastructure in the future such as H2 pipelines and infrastructure, municipal and other civil infrastructure, as well as subsurface oil, gas, and geothermal infrastructure applications. The program has successfully demonstrated feasibility for all of the critical enabling elements and serves as a foundation for future technology maturation, deployment, and customization for other potential applications and high-priority needs into the future. Several underlying innovations have shown potential for commercial deployment and are the subject of continued development and technology transfer based upon patents submitted during the project duration.

02 PETROLEUM↗
DOE OSTI2023

Abstract for CRADA among NETL, Mälardalen University, University College Cork (acting through its Tyndall National Institute), and Micro Electricity Generation Association

GeoCoHorT aims to accelerate the transition to 4 th generation district heating and cooling (4GDHC) in Europe and globally, by assessing, optimizing, and demonstrating the integration of shallow geothermal heat extraction with other renewable sources and smart buildings. The target geo-source is ground water from River Shannon (Limerick, Ireland), which will provide both an efficiency and noise-reduction benefit to the micro-district and a method of combatting climate-change-warming of the Shannon Estuary. The heat from this low-temperature source will be recycled and supplied to a smart district located in Limerick, Ireland, through heat pumps powered by renewable electricity and by means of a suitable DH network design. The mandate is to find solutions that work for entire communities in a fair and sustainable manner, with the involvement of the communities themselves to highlight their needs. The project brings together four partners from Ireland, Sweden, and the U.S.A. with strong multi-disciplinary competencies. The Tyndall Institute at University of Cork (UCC) and Mälardalen University (MDU) will design and optimize the 4GDH micro-district system, and the Micro Electricity Generation Association (MEGA) will work with the community to build a sense of ownership of the Climate Challenge and ensure close community involvement. The National Energy Technology Laboratory of the U.S. Department of Energy (NETL) will design the prospective geo-fluid loop to optimize heat-extraction effectiveness with ecological safety and assess the environmental benefits of the proposed solutions. Industrial advisors (Eskilstuna Strängnäs Energi och Miljö (ESEM) and Mimer in Sweden, and Smart MPOWER in Ireland) will steer the technology development to meet the needs of public utilities and consumers. Current infrastructure in the smart district in Limerick (demonstration site of an EU Lighthouse project) will be exploited and modified with support of MEGA, UCC, and MPOWER to integrate micro-DH from low-temperature heat sources, such as the river heat, and to allow increasing penetration of prosumers in the grid. Additional case studies will be developed with the help of industrial advisors to extend the results to other regions (e.g., Sweden and USA) and provide valuable insights for the development of 4GDH worldwide.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗
DOE OSTI2021

Sub-second and ppm-level optical sensing of hydrogen using templated control of nano-hydride geometry and composition

Abstract The use of hydrogen as a clean and renewable alternative to fossil fuels requires a suite of flammability mitigating technologies, particularly robust sensors for hydrogen leak detection and concentration monitoring. To this end, we have developed a class of lightweight optical hydrogen sensors based on a metasurface of Pd nano-patchy particle arrays, which fulfills the increasing requirements of a safe hydrogen fuel sensing system with no risk of sparking. The structure of the optical sensor is readily nano-engineered to yield extraordinarily rapid response to hydrogen gas (<3 s at 1 mbar H 2 ) with a high degree of accuracy (<5%). By incorporating 20% Ag, Au or Co, the sensing performances of the Pd-alloy sensor are significantly enhanced, especially for the Pd 80 Co 20 sensor whose optical response time at 1 mbar of H 2 is just ~0.85 s, while preserving the excellent accuracy (<2.5%), limit of detection (2.5 ppm), and robustness against aging, temperature, and interfering gases. The superior performance of our sensor places it among the fastest and most sensitive optical hydrogen sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Survey of the Worldwide Supply Chain of Commodities Needed for a Quantum Technology Program

Quantum Information Science (QIS) is an emerging technology being pursued by fundamental science research groups worldwide, as well as commercial companies and government programs. There are a variety of QIS disciplines, including quantum computing, quantum sensing and quantum encryption. Some of the commodities needed for a robust quantum laboratory are particular to quantum phenomenon, but in general the equipment needed is similar to that needed for a typical high - technology lab (e.g. oscilloscopes, lasers, vacuum chambers, etc.). This study focuses on identifying commodities manufactured worldwide that would be needed for a robust quantum lab. The authors' own knowledge of needed equipment and primary vendors was used as a starting point, follow ed by extensive internet searching and utilization of buyer's guides to create a large spreadsheet of most of the components needed, the company offering the components, and country of manufacture. With this extensive spreadsheet, stakeholders can identify commodities that would be needed for a quantum lab oratory and potentially identify market choke points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing Temperature Monitoring of the Bottom Anode in a Direct Current Electric Arc Furnace Operations with Distributed Optical Fiber Sensors.

The bottom anode in the Direct Current Electric Arc Furnace (DC EAF) is critical for completing the electrical circuit necessary for sustaining the arc within the furnace. For pin-type bottom anodes, monitoring of the temperature of select pins instrumented with thermocouples is performed to track bottom wear in the EAF and inform the operator when the furnace should be removed from service. Furthermore, this work presents the results from a plant trial using distributed temperature monitoring of bottom anode pins in a 165-ton DC EAF over a two-month service period utilizing two optical fiber sensing techniques: fiber Bragg grating (FBG) and Rayleigh backscattering (RBS). The early detection of temperature anomalies along the length of the anode pin through distributed sensing enhances operational safety, providing a robust alternative to traditional thermocouples.

42 ENGINEERING↗
DOE OSTI2026

Redefining precision interferometry and spectroscopy with high-performance optical interference coatings

High-performance optical interference coatings have transformed precision interferometry and spectroscopy by enabling unparalleled control over light–matter interactions. This review explores recent innovations in ion-beam sputtered amorphous dielectric, as well as substrate-transferred crystalline coatings, and their impact on systems at the forefront of precision metrology. These state-of-the-art coating techniques generate multilayers with ultralow optical losses, yielding mirrors with exceptional reflectivity. Refinements in their noise performance push the ultimate limits of sensitivity, resolution, and stability in demanding laser-based metrology applications. These technologies underpin the most advanced timekeeping and spatial measurement tools, enabling high-finesse reference cavities for the world’s most precise optical atomic clocks and low-noise reflective test masses for km-baseline gravitational-wave detectors. Emerging hybrid designs combining these techniques expand access to the mid-infrared spectral region, enabling the first ultralow-optical-loss coatings in the 3000–5000 nm wavelength range for enhanced spectroscopy and trace-gas detection. We highlight how these technologies redefine coating performance metrics and set new benchmarks in quantum science, fundamental physics, and precision optical sensing.

Cole, Garrett D. [University of Arizona, Tucson, A↗
DOE OSTI2020

Atmospheric Convection and Air–Sea Interactions over the Tropical Oceans: Scientific Progress, Challenges, and Opportunities

Over the past thirty years, the scientific community has made considerable progress in understanding and predicting tropical convection and air-sea interactions, thanks to sustained investments in extensive in-situ and remote sensing observations, targeted field experiments, advances in numerical modeling, and vastly improved computational resources and observing technologies. Those investments would not have been fruitful as isolated advancements without the collaborative effort of the atmospheric convection and air-sea interaction research communities. In this spirit, a U.S. and International CLIVAR-sponsored workshop on “Atmospheric convection and air-sea interactions over the tropical oceans” was held May 7-9, 2019 in Boulder, CO. The 90 participants were observational and modeling experts from the atmospheric convection and air-sea interactions communities with varying degrees of experience, from early-career researchers and students to senior scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comparison of excitation and kinetic temperatures in a laser-produced plasma using absorption spectroscopy

High-resolution tunable laser absorption spectroscopy is used to measure time-resolved absorption spectra for six neutral uranium transitions in a laser-produced plasma. Here analysis of the spectra shows that kinetic temperatures are similar for all six transitions, but excitation temperatures are higher than kinetic temperatures from 10–100 μs, indicating departures from local thermodynamic equilibrium.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Luminescent Materials for the Detection of Economically Critical Metals in Harsh Environments

Renewable energy technologies used for electric vehicles and wind turbines are heavily reliant upon metals, such as rare earth elements, cobalt, lithium, and nickel. Indeed, there are 50 minerals that are currently considered “economically critical” by the 2022 United States Geological Survey. With anticipated global adoption of renewable energy technologies, producing sufficient metals to meet this demand presents a significant challenge, particularly due to the current monopolistic market for many of these metals. The production of metals from unconventional sources, such as coal utilization byproducts, is one of many promising strategies to boost domestic supply. However, sensitive, rapid, and inexpensive characterization technologies are needed to minimize production costs associated with metals prospecting and processing. Photoluminescence-based sensing techniques are particularly intriguing due to their potential for low cost and portability, coupled with high sensitivity and selectivity. This presentation focuses on the development of high-performance sensing materials for a range of critical metals, including metal-organic frameworks capable of sensitizing detection of parts-per-billion concentrations of six different rare earth elements, nanoparticles that can detect down to 600 parts-per-billion levels of cobalt, and thin films that sense aluminum down to 120 parts-per-billion. These materials are highly selective, capable of withstanding low pH conditions, and provide a response within minutes. Importantly, each sensing material is integrated with a custom-built, fully portable fiber-optic spectrometer for potential field deployment, providing significant cost savings over commercial instruments, along with potential advantages such as material regeneration for use across multiple sensing cycles and solvent removal for enhanced emission signal. These results highlight the exciting potential of luminescence platforms as cost-effective alternatives for metals characterization.

Crawford, Scott↗
DOE OSTI2025

Quantum materials for nanosensing and fault-tolerant quantum computing

New concepts of symmetry related to topological order emerged from the discovery of the fractional quantum Hall effect and high-temperature superconductivity in strongly correlated electron systems. This led to the study of quantum materials-- materials exhibiting emergent quantum phenomena with no classical analogues. While these materials have engendered exciting basic materials science and physics, realizing novel devices is a key challenge in the field. The goal of this proposal is to harness the unique properties of topological materials for quantum computing and quantum sensing applications. In this project, we investigated a variety of topological superconducting platforms and identified three technologies that can benefit from their quantum properties: quantum memory, single-photon detection, and non reciprocal electronics. The platforms developed in this work will be broadly useful to National Security and Basic Science.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

17 WIND ENERGY↗
DOE OSTI2025

Estimating the energy threshold of phonon-mediated superconducting qubit detectors operated in an energy-relaxation sensing scheme

In recent years, the lack of a conclusive detection of weakly interacting massive particle dark matter at the 10 GeV/c 2 mass scale and above has encouraged development of low-threshold detector technology aimed at probing lighter dark matter candidates. Detectors based on Cooper-pair-breaking sensors have emerged as a promising avenue for this detection due to the low (meV-scale) energy required for breaking a Cooper pair in most superconductors. Among them, devices based on superconducting qubits are interesting candidates for sensing due to their observed sensitivity to broken Cooper pairs. Here, we have developed an end-to-end G 4 CMP -based simulation framework and have used it to evaluate performance metrics of qubit-based devices operating in a gate-based “energy relaxation” readout scheme, akin to those used in recent studies of qubit sensitivity to ionizing radiation. We find that for this readout scheme, the qubit acts as a phonon sensor with an energy threshold ranging down to ≃ 0.4 eV for near-term performance parameters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

54 ENVIRONMENTAL SCIENCES↗