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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 ongoing rapid transformation of our energy supply challenges the operation and stability of electric power grids and other supply networks. This focus issue comprises new ideas and concepts in the monitoring and control of complex networks to address these challenges.
A 17 GHz single cell, standing wave, copper accelerator structure with an axisymmetric elliptical central cell sidewall was tested for internal and downstream dark current as a function of gradient up to 93 MV/m. The elliptical sidewall was predicted to suppress the internal dark current and the lower order multipactor modes as compared with a previously tested structure having a straight sidewall. During the conditioning phase of the elliptical sidewall structure, strong internal dark current generated by an N = 1 multipactor mode was observed at gradients in the 10 to 20 MV/m range. After conditioning with 2.2x10^5 pulses to 93 MV/m, the N = 1 mode was completely suppressed and no multipactor resonances were observed. The internal dark current was reduced to a comparatively low level, much smaller than in the previously studied, straight sidewall structure, in good agreement with simulations. The energy spectrum of the electrons colliding with the sidewall was measured using an isolated side dark current monitor and a bias voltage. As the conditioning progressed, the electron energy spectrum showed an increase in the concentration of lower energy electrons, also in good agreement with simulations. Studies of internal dark current may help to understand the rf conditioning and ultimate performance of high gradient accelerator structures.
Whether for scientific inquiry, weather forecasting, or public safety, the world relies upon the data gathered by satellite remote sensing. Some of NASA s most valuable work is in its remote sensing capabilities - the ability to retrieve data acquired at great distances - affording a height and scope not available from the ground. NASA satellites in low Earth orbit (LEO) monitor ocean health by taking large-scale pictures of phytoplankton blooms and measuring surface temperatures; snap photographs of full hurricanes from above, teaching researchers about how these giant storms form; and capture images of cloud formation and air pollution, all allowing researchers to further develop understanding of the planet s health. NASA remote sensing satellites also monitor shifts in the Earth s crust, analyze wind patterns around the world to develop efficient wind energy, help people around the world recover from natural disasters, and monitor diminishing sea ice levels. Just as researchers are more heavily relying on this data from space to conduct their work, the instruments carried on satellites are getting more sophisticated and capable of capturing increasingly complex and accurate measurements. The satellites are covering larger areas, from farther away, and generating more and more valuable data. The ground-based receivers for this wealth of satellite data have grown increasingly capable of handling greater bandwidth and higher power levels. They have also become less expensive, through a NASA research partnership, with the creation of a high-rate X-band data receiver system that is now in widespread use around the globe.
Carbon capture and storage (CCS) has emerged as a key solution in the fight against climate change. However, for CCS to succeed, it is crucial to ensure that the sequestered CO2 stays safely trapped underground. The U.S. Department of Energy (DOE) has emphasized the need for advancements in subsurface monitoring, measurement, reporting, and verification. Aside from caprock integrity failure, the other primary failure points usually involve defective cement in the casing annulus of wellbores or plugged and abandoned wells. In addition, many energy producers (e.g., oil and gas, geothermal) and storage and disposal operators (e.g., H2 and water) must deal with the same issue. Poorly placed or degraded cement can create pathways for gas or fluid to escape from casing annuli and in plugged and abandoned or orphan wells, posing environmental risks. Yet, a reliable and cost-effective way to monitor cement and well integrity over multiple decades is still unavailable. Traditional geophysical methods like 4D seismic imaging and surface-based electromagnetic monitoring lack the resolution and accuracy for detecting these types of failures (Vasco et al., 2022; Fawad and Mondol, 2021). Wireline logging is expensive to run continuously and is obtrusive to the operation. While fiber optics can potentially be a solution, its bulkiness can significantly compromise the cement's integrity. To address these challenges, the Advanced Energy Consortium (AEC) at The University of Texas at Austin’s Bureau of Economic Geology (the Bureau) has been pioneering research in subsurface monitoring using its portfolio of distributed autonomous microfabricated sensors for harsh subsurface environments since 2008. A class of these microsensors [System on a Chip (SoC)] can be mixed in cement and permanently placed without compromising the cement column; the sensors would then communicate with each other or a data acquisition (DAQ) master node. Another class of the AEC microsensors can be fully autonomous, with rechargeable micro-batteries capable of exceeding 100°C, flash memory, and, currently, a pressure and temperature sensor. They are designed to circulate in mud, geothermal fluids, U-loops, or pipelines. They can log data into memory and are unobtrusive to operations. Our team has been working on a multi-year DOE-funded project (DE-FE0031856)—supported by $2.95M in federal funding and $0.75M in cost-matching from the AEC—to demonstrate SoC sensor utility for CO2 leakage monitoring in CCS applications. This multi-institutional collaboration developed a novel sensing architecture utilizing radiofrequency (RF) microsensors embedded within the cement sheath. These sensors detect CO2 migration and are interrogated via a Smart Casing Collar (SCC).
Accessible performance and operational data have been identified as a key enabler for distributed wind energy industry advancement. While utility-scale wind turbines benefit from reliable and continuous supervisory control and data acquisition (SCADA)-based monitoring platforms, monitoring of the U.S. fleet of distributed wind (DW) turbines has been more inconsistent, unreliable, and sometime difficult to access. Without fleet monitoring data, the industry will never understand and thus work to improve turbine under-performance and reliability issues. For the DW industry to scale up, attract investors, and boost credibility, fleetwide monitoring must be robust and reliable, select data must be made accessible to stakeholders, and the data must be in a format useful to users. To help move the industry toward a more standardized, accessible stream of monitoring data, this distributed wind monitoring best practices report attempts to cover topics including key monitoring channels, hardware, communication strategies, and accessibility. Strategic engagement with DW original equipment manufacturers (OEMs), service providers, lab and university researchers, testing organization, certification bodies, end users and solar photovoltaic (PV) monitoring experts has enabled a better understanding of the current state-of-the-art of monitoring and aided in articulating this set of best practices that will guide OEMs toward harmonized monitoring strategies, aimed at a future goal of achieving accessible performance and operational data for the entire fleet of U.S. distributed wind turbines.
DESI, otherwise known as the Dark Energy Spectroscopic Instrument, is an astronomical project measuring millions of optical spectra to characterize Dark Energy. The survey has been running for 3 years. My work delves into the data taking/analysis side of the DESI CCDs (Charge-Coupled Devices) which will help in swapping out broken CCDs on the instrument. Additionally, I worked on making new metrics for ICARUS, Imaging Cosmic and Rare Underground Signals for their monitoring of run data. This paper will talk about the types of characterizations done for DESI and additional metrics for data quality and tools used for ICARUS monitoring.
Marine energy (ME) has the potential to power businesses in the blue economy. Kelp farms are an emerging maritime market of the blue economy and are predicted to grow, but they are not currently using ME for their power needs. As the number and size of kelp farms increase, more offshore power will be needed onsite for operations, monitoring, and harvesting. ME devices such as tidal current energy converters and wave energy converters (WECs) may be used to supply power for these needs. This article assesses the status of kelp farming in the continental United States, investigates the electricity needs of kelp farms, and examinesthe feasibility of generating the required electricity from wave and tidal current energy. The United States currently has 165 kelp farms that have either active or pending permits. The farms use electricity for boat operations, kelp drying, environmental monitoring, offshore lighting, and the raising and lowering of lines. Most kelp farms are in protected, nearshore waters that do not have significant wave energy resources. The limited available wave energy could be used to power small devices, but WECs have not yet been developed for that application. Some kelp farms are in locations that feature significant tidal energy resources, but small tidal current energy converters that are compatible with existing farm operations are not yet commercially available. As low-power WECs and tidal current energy converters are developed, kelp farms could be research partners and early adopters of the new technologies, which would encourage their broader use by other blue economy businesses.
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The Gamma-ray Large Area Space Telescope (GLAST) is a next-generation high-energy gamma-ray telescope for studying high energy gamma-ray emission from astrophysical sources. The main instrument is the Large Area Telescope (LAT) which operated in the energy band from 20 MeV to greater than 300 GeV. A second instrument, the Glast Burst Monitor to provide supportive observations of gamma-ray bursts at lower energies. The LAT is a solid state pair-conversion telescope which will have capabilities well beyond those achieved by the highly successful EGRET instrument on the Compton Gamma-ray Observatory. The sensitivity achieved on the entire sky after a single day's observation is similar to the point source sensitivity of EGRET for its entire mission. The large effective area will allow flares from AGN to be detected at much lower flux levels and on far shorter time intervals that has previously been possible from space. The very large field of view will make it possible to monitor approx. 20% of the sky at any instant, and the entire sky on timescale of a few hours. In this talk I will describe the design of the GLAST instruments and discuss their science capabilities.
Inefficient control settings in multifamily boilers often lead to substantial energy and cost penalties. To address this, a Fault Detection and Diagnostic (FDD) tool was developed to automate data analysis and identify operational faults such as suboptimal outdoor temperature sensor placement, misconfigured outdoor air reset (OAR) curves, excess boiler cycling, and domestic hot water (DHW) setpoint errors. By comparing pre- and post-implementation periods and applying engineering models, the tool quantifies energy savings and reduces manual analysis time by over 90%. Testing on over 100 monitored sites and a targeted subset of 12 buildings showed an average 11% energy savings from remote optimization; further validation across 19 OAR curve changes confirmed the tool’s accuracy, predicting actual savings within ±5% for most cases. Simple payback can be under three years for many multifamily buildings, though rising hardware, labor, and fuel costs create uncertainties, and decarbonization goals increasingly shift focus to electrification. The FDD tool remains invaluable for optimizing existing boilers, enhancing future electrification measures, and adapting to new technologies by refining building load estimates. In doing so, it supports both near-term efficiency and long-term transitions to low-carbon alternatives, ensuring buildings achieve substantial cost and energy benefits throughout their system lifecycles.
Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.
Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.
This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.
Nowadays, photovoltaic (PV) technology has reached a high level of maturity in terms of module efficiency and cost competitiveness in comparison with other energy technologies. As PV has achieved high levels of deployment, the development of devices that can help to reduce PV operation and maintenance costs has become a priority. Soiling can be cause of significant losses in certain PV plants and its detection has become essential to ensure a correct mitigation. For this reason, accurate and low-cost monitoring devices are needed. While soiling stations have been traditionally employed to measure the impact of soiling, their high cost and maintenance have led to the development of innovative low-cost optical sensors, such as the device presented in this work and named “DUSST” (Detector Unit for Soiling Spectral Transmittance). The thermal characterization of DUSST’s components and the methodology used to predict soiling transmittance losses are presented in this study. The results demonstrate that the losses can be predicted with an error lower than 1.4%. The method has been verified with an experimental campaign with naturally soiled coupons exposed outdoors in Jaén, Spain.
This report summarizes work performed under DOE Award DE-EE00010149 to support the reliable operation of a community-scale biochar reactor system in Kivalina, Alaska. The project focused on improving sanitation and waste management in a remote community by assessing the installed system, identifying spare parts, defining key performance indicators (KPIs), preparing operator and maintenance manuals, and developing mobile reporting tools for operational data and KPI tracking. The team also produced training materials and recorded videos to support operator onboarding and continuity. The project demonstrated progress in system readiness, documentation, and digital reporting, while also identifying challenges common to remote deployments, including travel constraints, upstream system failures, and local resource limitations. This work provides a practical framework for improving the operation, monitoring, and future replication of biomass reactor systems in remote communities.
Accurate subsurface characterization is crucial for all subsurface energy exploration. Accurate characterization of uncertain subsurface properties is also critical for monitoring storage of CO 2 , estimating pathways of subsurface contaminant transport, and monitoring potential nuclear explosions for treaty verification. This research will advance our world-leading subsurface sensing capabilities that are crucial for LANL missions in energy security (geothermal energy, oil/gas resource exploration, geologic carbon storage) and nuclear security (facility monitoring, detonation detection).
The closed-off structure of the Fermilab Drift Tube Linac precludes a robust array of instrumentation from directly monitoring the H- beam that is accelerated from 750 keV to 116 MeV. To improve beam tuning and operational assessment of Drift Tube Linac performance, scintillator-based loss monitors were previously installed along the exterior of the first two accelerating cavities to assess low energy beam losses. Here we present a recent upgrade to the loss monitor system, including significant improvements in analog signal processing to address baseline-interfering noise; digitization of the signals to enable regular operational use and tuning; and a new remote operation upgrade of the translating loss monitor with precise positioning of the loss monitor along its nine-foot track. Data from the fixed and translating detectors collected under varying beam conditions validate the utility of the upgrade.
The closed-off structure of the Fermilab Drift Tube Linac precludes a robust array of instrumentation from directly monitoring the H- beam that is accelerated from 750 keV to 116 MeV. To improve beam tuning and op-erational assessment of Drift Tube Linac performance, scintillator-based loss monitors were previously installed along the exterior of the first two accelerating cavities to assess low energy beam losses. Here we present a recent upgrade to the loss monitor system, including significant improvements in analog signal processing to address baseline-interfering noise; digitization of the signals to enable regular operational use and tuning; and a new remote operation upgrade of the translating loss monitor with precise positioning of the loss monitor along its nine-foot track. Data from the fixed and translating de-tectors collected under varying beam conditions validate the utility of the upgrade.