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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nb$_3$Sn Superconducting Radiofrequency Cavities: a Maturing Technology for Particle Accelerators and Detectors

Nb3Sn superconducting radiofrequency (SRF) cavities have substantial potential for enabling new performance capabilities for particle accelerators for high energy physics (HEP), as well as for RF cavity-based detectors for dark matter, gravitational waves, and other quantum sensing applications. Outside of HEP, Nb3Sn SRF cavities can also benefit accelerators for nuclear physics, basic energy sciences, and the industry. In this contribution to Snowmass 2021, we overview the potential, status, and outlook of Nb3Sn SRF cavities.

43 PARTICLE ACCELERATORS↗
NASA NTRS1979

TMS communications hardware. Volume 2: Bus interface unit

A prototype coaxial cable bus communication system used in the Trend Monitoring System to interconnect intelligent graphics terminals to a host minicomputer is described. The terminals and host are connected to the bus through a microprocessor-based RF modem termed a Bus Interface Unit (BIU). The BIU hardware and the Carrier Sense Multiple Access Listen-While-Talk protocol used on the network are described.

Brown, J. S.↗
NASA NTRS2015

Hyperspectral Microwave Atmospheric Sounder (HyMas) - New Capability in the CoSMIR-CoSSIR Scanhead

Lincoln Laboratory and NASA's Goddard Space Flight Center have teamed to re-use an existing instrument platform, the CoSMIRCoSSIR system for atmospheric sounding, to develop a new capability in hyperspectral filtering, data collection, and display. The volume of the scanhead accomodated an intermediate frequency processor(IFP), that provides the filtering and digitization of the raw data and the interoperable remote component (IRC) adapted to CoSMIR, CoSSIR, and HyMAS that stores and archives the data with time tagged calibration and navigation data.The first element of the work is the demonstration of a hyperspectral microwave receiver subsystem that was recently shown using a comprehensive simulation study to yield performance that substantially exceeds current state-of-the-art. Hyperspectral microwave sounders with 100 channels offer temperature and humidity sounding improvements similar to those obtained when infrared sensors became hyperspectral, but with the relative insensitivity to clouds that characterizes microwave sensors. Hyperspectral microwave operation is achieved using independent RF antennareceiver arrays that sample the same areavolume of the Earths surfaceatmosphere at slightly different frequencies and therefore synthesize a set of dense, finely spaced vertical weighting functions. The second, enabling element of the proposal is the development of a compact 52-channel Intermediate Frequency processor module. A principal challenge in the development of a hyperspectral microwave system is the size of the IF filter bank required for channelization. Large bandwidths are simultaneously processed, thus complicating the use of digital back-ends with associated high complexities, costs, and power requirements. Our approach involves passive filters implemented using low-temperature co-fired ceramic (LTCC) technology to achieve an ultra-compact module that can be easily integrated with existing RF front-end technology. This IF processor is universally applicable to other microwave sensing missions requiring compact IF spectrometry.The data include 52 operational channels with low IF module volume (100cm3) and mass (300g) and linearity better than 0.3 over a 330K dynamic range.

sounding↗
NASA NTRS2015

Wireless Capacitive Pressure Sensor With Directional RF Chip Antenna for High Temperature Environments

This paper presents the design, fabrication and characterization of a wireless capacitive pressure sensor with directional RF chip antenna that is envisioned for the health monitoring of aircraft engines operating in harsh environments. The sensing system is characterized from room temperature (25 C) to 300 C for a pressure range from 0 to 100 psi. The wireless pressure system consists of a Clapp-type oscillator design with a capacitive MEMS pressure sensor located in the LC-tank circuit of the oscillator. Therefore, as the pressure of the aircraft engine changes, so does the output resonant frequency of the sensing system. A chip antenna is integrated to transmit the system output to a receive antenna 10 m away.The design frequency of the wireless pressure sensor is 127 MHz and a 2 increase in resonant frequency over the temperature range of 25 to 300 C from 0 to 100 psi is observed. The phase noise is less than minus 30 dBcHz at the 1 kHz offset and decreases to less than minus 80 dBcHz at 10 kHz over the entire temperature range. The RF radiation patterns for two cuts of the wireless system have been measured and show that the system is highly directional and the MEMS pressure sensor is extremely linear from 0 to 100 psi.

Pressure sensor↗
DOE OSTI2023

Data, scripts, and figures associated with a manuscript studying impact of climate and topography on post-fire vegetation recovery.

This data package is associated with the publication “Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning” submitted to Remote Sensing of Environment (Zahura et al. 2023). In this research, a machine learning algorithm, random forest (RF), was utilized to examine the impact of climate and topography on post-fire vegetation recovery. We used enhanced vegetation index (EVI) to examine varying burn severity and land cover types. The data package includes the input files for RF model training, outputs from model predictions and analysis, and python scripts to run the model, analyze the results to understand model performance and interpretability, and plot manuscript figures. This data package contains three folders (Data, Scripts, and Figures), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Postfire_recovery_flmd.csv for a list of all files contained in this data package and descriptions for each. The data dictionary (Postfire_recovery_dd.csv) describes the csv column headers. The “Data” folder provides all the inputs and outputs to train the RF model, evaluate performance, and interpret predictions. The “Scripts” folder contains python scripts and jupyter notebooks for model training and result analysis. The “Figures” folder includes the figures used in the manuscript in “.png” and “.jpg” format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Radio Frequencies Emitted by Mobile Granular Materials: A Basis for Remote Sensing of Sand and Dust Activity on Mars and Earth

In recent laboratory experiments, measurements were made of microsecond radio-wave (RF) bursts emitted by grains of sand as they energetically circulated in a closed, electrically ungrounded chamber. The bursts appeared to result from nanoscale electrical discharging from grain surfaces. Both the magnitude and wave form of the RF pulses varied with the type of material undergoing motion. The release of RF from electrical discharging is a well-known phenomenon, but it is generally measured on much larger energy scales (e.g., in association with lightning or electrical motors). This phenomenon might be used to detect, on planetary surfaces, the motion and composition of sand moving over dunes, the turbulent motion of fine particles in dust storms, highly-energetic grain and rock collisions in volcanic eruptions, and frictional grinding of granular materials in dry debris flows, landslides, and avalanches. The occurrence of these discharges has been predicted from theoretical considerations Additional information is contained in the original.

Marshall, J.↗
NASA NTRS1973

Study of blood flow sensing with microwave radiometry

A study and experimental investigation has been performed to determine the feasibility of measuring regional blood flow and volume in man by means of microwave radiometry. An indication was expected of regional blood flow from measurement of surface and subsurface temperatures with a sensitive radiometer. Following theoretical modeling of biological tissue, to determine the optimum operating frequency for adequate sensing depth, a sensitive microwave radiometer was designed for operation at 793 MHz. A temperature sensitivity of of 0.06 K rms was realized in this equipment. Measurements performed on phantom tissue models, consisting of beef fat and lean beefsteak showed that the radiometer was capable of sensing temperatures from a depth between 3.8 and 5.1 cm. Radiometric and thermodynamic temperature measurements were also performed on the hind thighs of large dogs. These showed that the radiometer could sense subsurface temperatures from a depth of, at least, 1.3 cm. Delays caused by externally-generated RF interference, coupled with the lack of reliable blood flow measurement equipment, prevented correlation of radiometer readings with reginal blood flow. For the same reasons, it was not possible to extend the radiometric observations to human subjects.

Porter, R. A.↗
NASA NTRS2010

Thermal Deformation and RF Performance Analyses for the SWOT Large Deployable Ka-Band Reflectarray

A large deployable antenna technology for the NASA Surface Water and Ocean Topography (SWOT) Mission is currently being developed by JPL in response to NRC Earth Science Tier 2 Decadal Survey recommendations. This technology is required to enable the SWOT mission due to the fact that no currently available antenna is capable of meeting SWOT's demanding Ka-Band remote sensing requirements. One of the key aspects of this antenna development is to minimize the effect of the on-orbit thermal distortion to the antenna RF performance. An analysis process which includes: 1) the on-orbit thermal analysis to obtain the temperature distribution; 2) structural deformation analysis to get the geometry of the antenna surface; and 3) the RF performance with the given deformed antenna surface has been developed to accommodate the development of this antenna technology. The detailed analysis process and some analysis results will be presented and discussed by this paper.

thermal distorsion↗
NASA NTRS2011

Higher Order Parametric Excitation Modes for Spaceborne Quadrupole Mass Spectrometers

This paper describes a technique to significantly improve upon the mass peak shape and mass resolution of spaceborne quadrupole mass spectrometers (QMSs) through higher order auxiliary excitation of the quadrupole field. Using a novel multiresonant tank circuit, additional frequency components can be used to drive modulating voltages on the quadrupole rods in a practical manner, suitable for both improved commercial applications and spaceflight instruments. Auxiliary excitation at frequencies near twice that of the fundamental quadrupole RF frequency provides the advantages of previously studied parametric excitation techniques, but with the added benefit of increased sensed excitation amplitude dynamic range and the ability to operate voltage scan lines through the center of upper stability islands. Using a field programmable gate array, the amplitudes and frequencies of all QMS signals are digitally generated and managed, providing a robust and stable voltage control system. These techniques are experimentally verified through an interface with a commercial Pfeiffer QMG422 quadrupole rod system.When operating through the center of a stability island formed from higher order auxiliary excitation, approximately 50% and 400% improvements in 1% mass resolution and peak stability were measured, respectively, when compared with traditional QMS operation. Although tested with a circular rod system, the presented techniques have the potential to improve the performance of both circular and hyperbolic rod geometry QMS sensors.

Gershman, D. J.↗
DOE OSTI2023

Self-Sustainable IoT-Based Remote Sensing Powered by Energy Harvesting Using Stacked Piezoelectric Transducer and Thermoelectric Generator

We propose a self-powered remote multi-sensing system for traffic sensing which is powered by the collective energy harvested from the mechanical vibration of the road caused by the passing vehicles and from the temperature gradient between the asphalt of the road and the soil underneath. A stacked piezoelectric transducer converts mechanical vibrations into electrical energy and a thermoelectric generator harvests the thermal energy from the thermal gradient. Electrical energy signals from the stacked piezoelectric transducer and the thermoelectric generators are converted into usable DC power to recharge the battery using AC-DC and DC-DC converters working simultaneously. The multi-sensing system comprises an embedded system with a microcontroller that acquires data from the sensors and sends the sensory data to an IoT transceiver which transmits the data as RF packets to an ethernet gateway. The gateway converts the RF packets into Internet Protocol (IP) packets and sends them to a remote server. Laboratory and road-testing results showed over 98% sensory data accuracy with the system functioning solely powered by the energy harvested from the alternative energy sources. The successful maximum transmission distance obtained between the IoT, and the gateway was approximately 1 mile, which is a considerable transmission distance achieved in an urban environment. Successful operation of the self-powered multi-sensing system under both laboratory and road conditions contributes considerably to the fields of energy harvesting and self-powered remote sensing systems. The energy flow chart and efficiency for the steps in the system were found to be mechanical power from vehicles to the energy harvester of 0.25%, stacked PZT transducer efficiency was found to be 37%, and for the TEGs the efficiency is 11%. AC-to-DC and DC-to-DC converters’ efficiencies were found to be 90% and 11%. The wireless communication RF transceiver efficiency was found to be 62.5%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Fiber optic sensing system

A fiber optic interferometer utilizes a low coherence light emitting diode (LED) laser as a light source which is filtered and driven at two RF frequencies, high and low, that are specific to the initial length of the resonator chamber. A displacement of a reflecting mirror changes the length traveled by the nonreferencing signal. The low frequency light undergoes destructive interference which reduces the average intensity of the wave while the high frequency light undergoes constructive interference which increases the average intensity of the wave. The ratio of these two intensity measurements is proportional to the displacement incurred.

Adamovsky, Grigory↗
DOE OSTI2025

Contributions of vegetation heterogeneity within tower footprint to CO 2 flux estimations through graph neural network modeling

Net ecosystem exchange of CO 2 (Fc) measured directly by eddy covariance towers is based on various assumptions, including large, flat and homogenous land cover type. In reality, often a tower site is not large enough for flux measurements, and landscapes consist of patches of different land cover types within the flux footprint. In addition, some portions of fluxes are contributed by different cover types when a footprint exceeds the size of the target ecosystem. The contributions of non-dominant patches to Fc are often ignored. Here, in this study, we propose a novel integrated modeling framework that combines random forest (RF) and XGBoost with a residual correction module based on a deep graph convolutional network (DeeperGCN) to simulate Fc for seven flux measurement sites in southwest Michigan. High-resolution remote sensing vegetation indices, soil properties, meteorological variables, and footprint-weighted spatial features were used as model inputs at three spatial resolutions (10 m, 20 m, 30 m), and their importance in predicting Fc with DeeperGCN was assessed. We found that residual correction using DeeperGCN significantly improved prediction accuracy, with the R 2 increasing from 0.9098 to 0.9479 for RF and from 0.9235 to 0.9433 for XGBoost. At site level, the maximum improvement in R 2 reached 0.1617. Paired t-tests confirmed that these improvements were statistically significant (p < 0.05). Among all predictors, leaf area index and incoming shortwave radiation emerged as the dominant drivers of spatial residual variation, followed by precipitation, relative humidity, and selected vegetation indices. The 20 m resolution yielded the best balance between model performance and computational efficiency. In conclusion, our modeling framework effectively captures both spatial heterogeneity and nonlinear interactions, offering a robust solution for spatially explicit flux modeling in structurally diverse ecosystems beyond the study sites.

footprint model↗
NASA NTRSDate not supplied

Delay Tolerant, Radio Frequency Identification (RFID )-enabled Sensing

Radio Frequency Identification (RFID) technology offers a completely passive method to transmit fixed data sequences from an RFID tag, which typically doesn’t have its own power supply, to an interrogator. Radio frequency (RF) energy harvested from the interrogator is rectified by the tag and used to charge an integrated circuit (IC). The IC then modulates the received signal with the data stored on the tag and reflects the energy back to the interrogator. RFID has seen great proliferation in terrestrial inventory management applications, and it has recently made the jump to spaceflight applications onboard the International Space Station, augmenting an existing optical bar‐code infrastructure for tracking supplies. A number of advanced automated logistics management (ALM) concepts employing RFID are currently being developed and evaluated, including so‐called “smart” shelves, cubbies, and trash receptacles using low‐power, embeddable RFID interrogators. An infrastructure where both crew members and robotic assistants, such as autonomous free flyers, are similarly equipped with small RFID interrogators seems likely. It therefore behooves us to consider extending this infrastructure beyond ALM to applications such as low power, embedded sensing. Typically, data on an RFID tag can only be written by an RFID interrogator, using interrogator energy. In recent years, however, a few efforts have focused on using that energy to drive data acquisition from the tag IC, allowing the tag to modify its stored data sequence with sensor data before replying to an interrogator. In this way, the tag can act as a completely passive sensing device. One problem exists with this approach, however: the tag cannot gather data when an interrogator is not present. Thus, strictly passive RFID sensing tags cannot gather data at regular intervals, in the manner of a typical wireless sensor network, without careful, and impractical, planning of mobile interrogator movements. To address this shortcoming, we look to a recent advance in RFID technology which allows an external microprocessor to power the tag IC and write directly into its RFID memory using a wired serial interface. In this paradigm, data gathering is driven by a small, on‐board power supply (using batteries or harvested energy), and data transfer is provided passively through the RFID interrogation service. Since communication typically consumes the lion’s share of power in WSNs, such a technique has the potential to enable extremely long‐lived, embedded wireless sensing when used with extremely low‐current microcontrollers. Since the communication channel is only open when an interrogator is present and actively interrogating the RFID sensing tag, transport of periodically‐sampled sensor data presents itself as a delay/disruption‐tolerant networking (DTN) problem. In this paper, we present the design of a DTN‐like overlay on the common EPC Global, Class 1, Generation 2 RFID standard. This overlay allows seamless, guaranteed data transfer using the EPC Global protocol, supporting extremely low‐power, embedded sensing using an infrastructure likely to be already in place for ALM applications. We evaluate a prototype implementation of a complete end‐to‐end system using a robotic RFID interrogation agent, and we present future directions for the development of this sensing technique.

Raymond S. Wagner↗
NASA NTRS2019

A 220-GHz InP HEMT Direct Detection Polarimeter

In this article, a 220-GHz direct detection polarimetric receiver is reported. This polarimetric receiver is intended for sensing of the microphysical properties of cloud ice in the earth's upper atmosphere. In the article, we report the measured RF performance, including RF gain of approximately 35 dB per channel and integrated noise figure of the receiver. The noise figure is measured to be 5.1 dB for the horizontally polarized channel and 5.4 dB for the vertically polarized channel. The receiver measures only 1.8 cm × 3.0 cm × 6.4 cm and has a total regulated dc power consumption of 520 mW. This article represents the first 220-GHz polarimetric direct detection receiver and is enabled by an advanced 25-nm indium phosphide (InP) HEMT monolithic microwave integrated circuit (MMIC) technology.

Indium phosphide (InP)↗
DOE OSTI2022

Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Antenna Technology Shuttle Experiment (ATSE)

Numerous space applications of the future will require mesh deployable antennas of 15 m in diameter or greater for frequencies up to 20 GHz. These applications include mobile communications satellites, orbiting very long baseline interferometry (VLBI) astrophysics missions, and Earth remote sensing missions. A Lockheed wrap rip antennas was used as the test article. The experiments covered a broad range of structural, control, and RF discipline objectives, which is fulfilled in total, would greatly reduce the risk of employing these antenna systems in future space applications. It was concluded that a flight experiment of a relatively large mesh deployable reflector is achievable with no major technological or cost drivers. The test articles and the instrumentation are all within the state of the art and in most cases rely on proven flight hardware. Every effort was made to design the experiments for low cost.

Freeland, R. E.↗