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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

LIRA: Probing the Lunar Surface for Resources.

The thermal, compositional and electrical properties of the lunar regolith, such as the triboelectric properties of lunar dust, the volatile content and the thermophysical properties of the polar regolith are essential to understand from a scientific perspective as well as for resource extraction and hazard mitigation. Understanding these properties specifically requires in situ measurement, as these properties are inherent in the ambient environment. With the Lunar In situ Regolith Analyzer (LIRA) we will combine mature surface sensor technologies from previous spacecraft missions (TECP) with new sensors (ClO4- ) and battery capability to achieve a compact in situ analysis package. The surface instrument suite is adapted for lunar conditions from a sensor developed as part of the Microscopy, Electrochemistry, and Conductivity Analyzer (MECA) instrument on the 2007 Mars Phoenix mission, as well as supplemental sensors. • The Thermal and Electrical Conductivity Probe (TECP), flown on MECA/Phoenix, which measures heat capacity, thermal conductivity, electrical resistance, and electrical permittivity between needles inserted into the soil. The TECP also measured atmospheric humidity, a capability that will be retained to search for subsurface H2O ice. The Mars TECP weighed 100-gram and by definition was at TRL 9. The lunar version (LIRA) is TRL 4. • The Perchlorate (ClO4) sensor is based on the fluorescence property of perchlorate (Mg, Fe, Na). We added a light source to excite the perchlorate (ClO4) and a photo diode to collect the emission wavelength. A sun-shade over the photo diode blocks out the ambient light during day light hours. The unifying goal of LIRA is to deliver a complete, integrated experiment suite that investigates properties that must be measured in situ, and provides the capability to power the suite at night or in PSRs, as well as a means to simulate aspects of the diurnal transition by delivering an optical pulse to the surface with intensity comparable to sunlight. We describe the scientific context of LIRA, touching on the electrical state of the near-surface environment, the thermal properties of the undisturbed regolith, and the distribution, geochemistry, and mobility of water. We also address the exploration implications of the work, examining hazards and resources in the context of strategic knowledge gaps (SKGs).

Heather Smith↗
DOE OSTI2020

Real-Time Defect Correction in Large-Scale Polymer Additive Manufacturing via Thermal Imaging and Laser Profilometer

Defects can result in a failed part and are costly in terms of time and material. This cost is even greater in the context of large-scale additive manufacturing where the objects can be very large. As a result, a great deal of research has focused on defect identification and mitigation. To address defects during object construction, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated an in-situ control system comprised of two sensors: a thermal camera and laser profilometer. This control system adjusted material flow and build speed to mitigate three types of defects: low layer times, underfill, and overfill. Several test objects were constructed. The control system was found to adjust build parameters to handle low layer times of approximately 15 seconds and height deviations from -100% underfill (the absence of a layer) to 50% overfill. Within two layers, height deviations could be returned to within 10% of the expected layer height. Further, preliminary results suggest the system can compensate for uneven build surfaces.

Borish, Michael↗
NASA NTRS2012

Satellite Observation Systems for Polar Climate Change Studies

The key observational tools for detecting large scale changes of various parameters in the polar regions have been satellite sensors. The sensors include passive and active satellite systems in the visible, infrared and microwave frequencies. The monitoring started with Tiros and Nimbus research satellites series in the 1970s but during the period, not much data was stored digitally because of limitations and cost of the needed storage systems. Continuous global data came about starting with the launch of ocean color, passive microwave, and thermal infrared sensors on board Nimbus-7 and Synthetic Aperture Radar, Radar Altimeter and Scatterometer on board SeaSat satellite both launched in 1978. The Nimbus-7 lasted longer than expected and provided about 9 years of useful data while SeaSat quit working after 3 months but provided very useful data that became the baseline for follow-up systems with similar capabilities. Over the years, many new sensors were launched, some from Japan Aeronautics and Space Agency (JAXA), some from the European Space Agency (ESA) and more recently, from RuSSia, China, Korea, Canada and India. For polar studies, among the most useful sensors has been the passive microwave sensor which provides day/night and almost all weather observation of the surface. The sensor provide sea surface temperature, precipitation, wind, water vapor and sea ice concentration data that have been very useful in monitoring the climate of the region. More than 30 years of such data are now available, starting with the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7, the Special Scanning Microwave/Imager (SSM/I) on board a Defense Meteorological Satellite Program (DMSP) and the Advanced Microwave Scanning Radiometer on board the EOS/ Aqua satellite. The techniques that have been developed to derive geophysical parameters from data provided by these and other sensors and associated instrumental and algorithm errors and validation techniques will be discussed. An important issue is the organization and storage of hundreds of terabytes of data collected by even just a few of these satellite sensors. Advances in mass storage and computer technology have made it possible to overcome many of the collection and archival problems and the availability of comprehensive satellite data sets put together by NASA's Earth Observing System project will be discussed.

Comiso, Josefino C.↗
NASA NTRS2014

VAB Temperature and Humidity Study

In 2012, 17 data loggers were placed in the VAB to measure temperature and humidity at 10-minute intervals over a one-year period. In 2013, the data loggers were replaced with an upgraded model and slight adjustments to their locations were made to reduce direct solar heating effects. The data acquired by the data loggers was compared to temperature data provided by three wind towers located around the building. It was found that the VAB acts as a large thermal filter, delaying and reducing the thermal oscillations occurring outside of the building. This filtering is typically more pronounced at higher locations in the building, probably because these locations have less thermal connection with the outside. We surmise that the lower elevations respond more to outside temperature variations because of air flow through the doors. Temperatures inside the VAB rarely exceed outdoor temperatures, only doing so when measurements are made directly on a surface with connection to the outside (such as a door or wall) or when solar radiation falls directly on the sensor. A thermal model is presented to yield approximate filter response times for various locations in the building. Appendix A contains historical thermal and humidity data from 1994 to 2009.

Temperature↗
NASA NTRS1990

The test facility requirements for the thermal vacuum thermal balance test of the Cosmic Background Explorer Observatory

The Cosmic Background Explorer Observatory (COBE) underwent a thermal vacuum thermal balance test in the Space Environment Simulator (SES). This was the largest and most complex test ever conducted at this facility. The 4 x 4 m (13 x 13 ft) spacecraft weighed approx. 2223 kg (4900 lbs) for the test. The test set up included simulator panels for the inboard solar array panels, simulator panels for the flight cowlings, Sun and Earth Sensor stimuli, Thermal Radio Frequency Shield heater stimuli and a cryopanel for thermal control in the Attitude Control System Shunt Dissipator area. The fixturing also included a unique 4.3 m (14 ft) diameter Gaseous Helium Cryopanel which provided a 20 K environment for the calibration of one of the spacecraft's instruments, the Differential Microwave Radiometer. This cryogenic panel caused extra contamination concerns and a special method was developed and written into the test procedure to prevent the high buildup of condensibles on the panel which could have led to backstreaming of the thermal vacuum chamber. The test was completed with a high quality simulated space environment provided to the spacecraft. The test requirements, test set up, and special fixturing are described.

Milam, Laura J.↗
NASA NTRS1991

Test facility requirements for the thermal vacuum thermal balance test of the Cosmic Background Explorer Observatory

The Cosmic Background Explorer Observatory (COBE) underwant a thermal vacuum thermal balance test in the Space Environment Simulator (SES). This was the largest and most complex test ever conducted at this facility. The 4 x 4 m (13 x 13 ft) spacecraft weighed approx. 2223 kg (4900 lbs) for the test. The test set up included simulator panels for the inboard solar array panels, simulator panels for the flight cowlings, Sun and Earth Sensor stimuli, Thermal Radio Frequency Shield heater stimuli and a cryopanel for thermal control in the Attitude Control System Shunt Dissipator area. The fixturing also included a unique 4.3 m (14 ft) diameter Gaseous Helium Cryopanel which provided a 20 K environment for the calibration of one of the spacecraft's instruments, the Differential Microwave Radiometer. This cryogenic panel caused extra contamination concerns and a special method was developed and written into the test procedure to prevent the high buildup of condensibles on the panel which could have led to backstreaming of the thermal vacuum chamber. The test was completed with a high quality simulated space environment provided to the spacecraft. The test requirements, test set up, and special fixturing are described.

Milam, Laura J.↗
DOE OSTI2022

Boron nitride neutron detector with the ability for detecting both thermal and fast neutrons

The detection of fast neutrons is regarded technically challenging because the interaction probability of fast neutron with matter is extremely low. Based on our recent development of hexagonal boron nitride (BN) semiconductor thermal neutron detectors with a record high efficiency of 59%, we report here the feasibility studies of BN detectors for detecting fast neutrons. A BN detector with a detection area of 2.1 cm 2 was fabricated from a 90 $μ$m thick BN epilayer. In the presence of a bare Cf-252 source emitting fast neutrons ranging from 1 to 9 MeV, the detection efficiency was estimated to be about 0.1%. Furthermore, the measured mean free path of fast neutron in BN is about 7.6 cm. Together with the capability of BN for thermal neutron detection, the present results indicate that by incorporating BN with a large thickness, BN neutron detectors are expected to possess the unique capability of directly detecting thermal to fast neutrons as well as outstanding features resulting from the ultrawide bandgap of BN. The identification of a single material that is sensitive to both thermal and fast neutrons is valuable for the development of novel neutron detection technologies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1977

A simple thermal model of the earth's surface for geologic mapping by remote sensing

Thermal inertia of the earth's surface can be used in geologic mapping as a complement to surface reflectance data as provided by Landsat. Thermal inertia cannot be determined directly but must be inferred from radiation temperature measurements (by thermal IR sensors) made at various times in the diurnal cycle, combined with a model of the surface heating processes. A model is developed which differs from those created previously for this purpose, because it includes sensible and latent heating. Tests of this model using field data indicate that it accurately determines the surface heating. When the model is used with field measurements of meteorological variables and is combined with remotely sensed temperature data, a thermal inertia image can be produced.

Kahle, A. B.↗
NASA NTRS1991

Piezoresistive sensors for measurement of thermally-induced stresses in microelectronics

The general theory of conduction in a piezoresistive material subjected to temperature change is derived and the basic equations needed to design and apply test chips for thermal stress measurement are established. A general expression is obtained for the resistance changes which occur in an arbitrarily oriented 1D filamentary conductor subjected to general 3D stress states and arbitrary temperature changes. The derived formula is valid for crystals such as silicon which have cubic symmetry and diamond lattice structures. Basic equations are presented for the resistance changes experienced by stressed in-plane resistors fabricated on (100) silicon wafers. The established theory is applied to a simple two-element sensor rosette. A test chip containing this rosette configuration is designed and fabricated, and a suitable calibration system is constructed.

Suhling, J. C.↗
DOE OSTI2024

Additive manufacturing enabled hot-wire apparatus with suppressed edge effect error

This invention describes a method of structuring a metal or metal coated wire in a manner such that it provides enhanced accuracy when used in a hot-wire apparatus for transient thermal conductivity measurements for low-density materials. This is accomplished by using a diameter varying structure with two ends of the wire of sufficiently small diameter that the edge-effect induced error can be reduced by generating a more uniform temperature profile along the wire, whereas in conventional apparatus the temperatures always drop at the ends of a finite wire due to its ends that are being cooled by the wire-supports.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Quantum-Well Infrared Photodetector (QWIP) Focal Plane Assembly

A paper describes the Thermal Infrared Sensor (TIRS), a QWIP-based instrument intended to supplement the Operational Land Imager (OLI) for the Landsat Data Continuity Mission (LDCM). The TIRS instrument is a far-infrared imager operating in the pushbroom mode with two IR channels: 10.8 and 12 microns. The focal plane will contain three 640x512 QWIP arrays mounted on a silicon substrate. The silicon substrate is a custom-fabricated carrier board with a single layer of aluminum interconnects. The general fabrication process starts with a 4-in. (approx.10-cm) diameter silicon wafer. The wafer is oxidized, a single substrate contact is etched, and aluminum is deposited, patterned, and alloyed. This technology development is aimed at incorporating three large-format infrared detecting arrays based on GaAs QWIP technology onto a common focal plane with precision alignment of all three arrays. This focal plane must survive the rigors of flight qualification and operate at a temperature of 43 K (-230 C) for five years while orbiting the Earth. The challenges presented include ensuring thermal compatibility among all the components, designing and building a compact, somewhat modular system and ensuring alignment to very tight levels. The multi-array focal plane integrated onto a single silicon substrate is a new application of both QWIP array development and silicon wafer scale integration. The Invar-based assembly has been tested to ensure thermal reliability.

Jhabvala, Murzy↗
NASA NTRS2008

Climate Quality Broadband and Narrowband Solar Reflected Radiance Calibration Between Sensors in Orbit

vAs the potential impacts of global climate change become more clear [1], the need to determine the accuracy of climate prediction over decade-to-century time scales has become an urgent and critical challenge. The most critical tests of climate model predictions will occur using observations of decadal changes in climate forcing, response, and feedback variables. Many of these key climate variables are observed by remotely sensing the global distribution of reflected solar spectral and broadband radiance. These "reflected solar" variables include aerosols, clouds, radiative fluxes, snow, ice, vegetation, ocean color, and land cover. Achieving sufficient satellite instrument accuracy, stability, and overlap to rigorously observe decadal change signals has proven very difficult in most cases and has not yet been achieved in others [2]. One of the earliest efforts to make climate quality observations was for Earth Radiation Budget: Nimbus 6/7 in the late 1970s, ERBE in the 1980s/90s, and CERES in 2000s are examples of the most complete global records. The recent CERES data products have carried out the most extensive intercomparisons because if the need to merge data from up to 11 instruments (CERES, MODIS, geostationary imagers) on 7 spacecraft (Terra, Aqua, and 5 geostationary) for any given month. In order to achieve climate calibration for cloud feedbacks, the radiative effect of clear-sky, all-sky, and cloud radiative effect must all be made with very high stability and accuracy. For shortwave solar reflected flux, even the 1% CERES broadband absolute accuracy (1-sigma confidence bound) is not sufficient to allow gaps in the radiation record for decadal climate change. Typical absolute accuracy for the best narrowband sensors like SeaWiFS, MISR, and MODIS range from 2 to 4% (1-sigma). IPCC greenhouse gas radiative forcing is approx. 0.6 W/sq m per decade or 0.6% of the global mean shortwave reflected flux, so that a 50% cloud feedback would change the global reflected flux by approx. 0.3 W/sq m or 0.3% per decade in broadband SW calibration change. Recent results comparing CERES reflected flux changes with MODIS, MISR, and SeaWiFS narrowband changes concluded that only SeaWiFS and CERES were approaching sufficient stability in calibration for decadal climate change [3]. Results using deep convective clouds in the optically thick limit as a stability target may prove very effective for improving past data sets like ISCCP. Results for intercalibration of geostationary imagers to CERES using an entire month of regional nearly coincident data demonstrates new approaches to constraining the calibration of current geostationary imagers. The new Decadal Survey Mission CLARREO is examining future approaches to a "NIST-in-Orbit" approach of very high absolute accuracy reference radiometers that cover the full solar and infrared spectrum at high spectral resolution but at low spatial resolution. Sampling studies have shown that a precessing CLARREO mission could calibrate other geo and leo reflected solar radiation and thermal infrared sensors.

Wielicki, Bruce A.↗
DOE OSTI2024

Developing a robust sensor for infrared imaging bolometers

A new type of large area sensor for infrared imaging bolometers has been developed. It replaces the thin and fragile free-standing metal foils, which typically have been used, with a multi-layer coated sapphire (or diamond) substrate. Sapphire is transparent to mid-infrared wavelengths, is robust against transients, and can be thick enough to even be the vacuum window. The primary radiation absorber is still a thin deposited metal layer, but now it is partially insulated from the supporting sapphire substrate by a black (carbon-based) layer, which also acts as a blackbody remitter. Test results indicate 6× more noise equivalent power density (estimated NEPD = 23 W/m 2 at 5 ms camera exposure time, foil temperature decay time 60 ms) for a 2 μm gold-coated sapphire disk compared to estimated NEP = 4 W/m 2 at 1.8 ms exposure time, with foil decay time 420 ms, for a nominal 2.5 μm thick platinum-free-standing foil.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
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 OSTI2020

Automated, Unmanned Detection and Quantification of Fugitive Methane Emissions

Compressor stations used to move natural gas are one of the largest sources of fugitive methane emissions in the midstream sector, accounting for approximately 50% of all fugitive emissions (Zimmerle et al., 2015). This problem is most widespread at reciprocating compressors (Subramanian et al., 2015) where faulty seals are a key contributor to methane emissions (Johnson et al., 2015). As such, there is a significant need for a robust technology that could provide an early indication of an unexpected emission. Equally important, the technology needs to be able to account for biogenic versus anthropogenic sources of methane. One means of indirectly making this determination, is to leverage optical technologies that can autonomously pinpoint the source of such leaks. This presentation discusses recent work funded by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), focused on the development of an innovative remote sensing technology that can reliably and autonomously detect fugitive methane emissions in near real-time, using computer vision and deep learning. The technology called the Smart Methane Leak Detection (SLED/M) system was initially developed to monitor facilities such as compressor stations in a stationary, pan-tilt-zoom configuration. The system has recently been adapted to monitor facilities from an unmanned aerial system (UAS). The speed and maneuverability of UAS platforms are attractive to leak detection and repair program operators, but introduce several challenges. Many existing methane detection algorithms rely on mostly static backgrounds becoming unusable with motion. In addition, top-down views of fugitive methane emissions present differently in Optical Gas Imagers (OGI) compared to looking across the plume. Our work has focused on overcoming these challenges, enhancing the operators ability to detect methane emissions, and pinpoint their sources. Another recent adaptation to SLED/M is the ability to quantify methane emissions using passive sensors (OGI, thermal camera), environmental conditions, plume modeling, and deep learning. SLED/M advances the state-of-the-art for methane emission detection and quantification by focusing on three key critical criteria for effective methane emission mitigation: (1) autonomy (no need for a human to be in the loop), (2) high reliability (low false alarm rates), and (3) real-time performance. Results from this work will be presented.

03 NATURAL GAS↗