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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Combined analysis of neutron and photon flux measurements for the Jules Horowitz reactor core mapping

We study the combined analysis of nuclear measurements to improve the knowledge of the irradiation conditions in the experimental locations of the future Jules Horowitz Reactor (JHR). The goal of the present work is to measure more accurately neutron flux, photon flux and nuclear heating in the reactor. In a Material Testing Reactor (MTR), nuclear heating is a crucial parameter to design the experimental devices to be irradiated in harsh nuclear conditions. This parameter drives the temperature of the devices and of the samples. The numerical codes can predict this parameter but in-situ measurements are necessary to reach the expected accuracy. For this reason, one objective of the IN-CORE program [1] is to study the combined measurements of neutron and photon flux and their cross advanced interpretation. It should be reminded that both neutron and photon sensors are not totally selective as their signals are due to neutron and photon interactions. We intend to measure the neutron flux by three different kinds of sensors (Uranium Fission chamber, Plutonium Fission chamber and Self Powered Neutron Detector), the photon flux by two different sensors (Ionization chamber and Self Powered Gamma Detector) and the nuclear heating by two different ones (Differential calorimeter and Gamma Thermometer). For the same parameter, we expect that the use of different kinds of sensors will allow a better estimation of the aimed parameter by mixing different spectrum responses and different neutron and gamma contributions. An experimental test called CARMEN-1 is scheduled in OSIRIS reactor (CEA Saclay - France) at the end of 2011, with the goal to map irradiation locations in the reactor reflector to get a first validation of the analysis model. This article focuses on the sensor selection for CARMEN-1 experiment and to the way to link neutron and photon flux measurements in view to reduce their uncertainties but also to better assess the neutron and photon contributions to nuclear heating. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

A portable time-domain LED fluorimeter for nanosecond fluorescence lifetime measurements

Fluorescence lifetime measurements are becoming increasingly important in chemical and biological research. Time-domain lifetime measurements offer fluorescence multiplexing and improved handling of interferers compared with the frequency-domain technique. In this paper, an all solid-state, filterless, and highly portable light-emitting-diode based time-domain fluorimeter (LED TDF) is reported for the measurement of nanosecond fluorescence lifetimes. LED based excitation provides more wavelengths options compared to laser diode based excitation, but the excitation is less effective due to the uncollimated beam, less optical power, and longer latency in state transition. Pulse triggering and pre-bias techniques were implemented in our LED TDF to improve the peak optical power to over 100 mW. The proposed pulsing circuit achieved an excitation light fall time of less than 2 ns. Electrical resetting technique realized a time-gated photo-detector to remove the interference of the excitation light with fluorescence. These techniques allow the LED fluorimeter to accurately measure the fluorescence lifetime of fluorescein down to concentration of 0.5 μM. In addition, all filters required in traditional instruments are eliminated for the non-attenuated excitation/emission light power. These achievements make the reported device attractive to biochemical laboratories seeking for highly portable lifetime detection devices for developing sensors based on fluorescence lifetime changes. The device was initially validated by measuring the lifetimes of three commercial fluorophores and comparing them with reported lifetime data. It was subsequently used to characterize a ZnSe quantum dot based DNA sensor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

High-resolution wall temperature measurements with distributed fiber optic sensors

Conventional approaches of surface temperature measurement are often unreliable, and other methods may not be practical in closed, high-temperature spaces. In this study, surface temperature measurements of the cooling panel of a natural circulation facility were performed with fiber optic distributed temperature sensors (DTS). High resolution surface temperature profiles of the cooling panel were acquired under two steady experimental conditions, and compared with measurements performed with nearby thermocouples. The total measurement error of the DTS results presented has been estimated to be ±4.4 °C. The findings reflected herein show that these DTS can reasonably measure surface temperature when installed with the methods developed in this study. Through the analysis of the DTS data collected, the interesting behavior of the cooling panel was revealed, providing new insights on the system's behavior that may be beneficial to the design and optimization of such heat transfer devices. These sensors can also provide the measurement density necessary for validation efforts with highly-resolved prediction methods such as finite element analysis (FEA) or computational fluid dynamics (CFD).

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments

Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Application of a Triple Bubbler Sensor for Determining the Density, Surface Tension, and Depth in Molten Salts

Electrochemical (i.e. pyroprocessing) technology is being widely studied throughout the world as a potential alternative to aqueous reprocessing of spent nuclear fuel (SNF). Central to this process is the electrorefiner (ER), in which the useful uranium in the SNF is electrochemically transported through a molten salt electrolyte to a cathode for later recovery. As part of this process, uranium, plutonium, and other actinides accumulate in the ER salt over time. Material accountancy and safeguards of these special nuclear material buildups in near real-time is a significant challenge due to the elevated processing temperatures, remote operation within a hotcell, and high radiation fields. Analytical techniques outside of the hotcell can be used to provide the SNM concentrations of salt samples from the ER. However, determining the actual mass of these materials in the ER presents a significant challenge because the density and volume within the vessel is largely unknown. The Idaho National Laboratory (INL) has developed a triple bubbler sensor capable of determining the density, surface tension, and depth of a fluid in near real-time. Previous experiments in aqueous media has shown that the sensor is capable of determining the density, surface tension and depth with accuracies on the order of 0.2%. The goal of this current work is to test the sensor in several molten salt media to calibrate and validate the triple bubbler. Experiments have been performed in LiCl-KCl eutectic salt. Calibration factors were 1.0015 and 1.26 for density and surface tension, respectively. With the above calibration factors applied, the depth of the salt in the test vessel was determined to within 0.4%. These results indicate that the triple bubbler sensor has the potential to accurately (less than 1% uncertainty) determine the density, surface, tension, and depth of the ER salt in near real-time. The triple bubbler sensor designed at INL can significantly enhance the material accountancy, safeguards, and process monitoring of the electrochemical processing of SNF.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Results of South-East Flux Trap Dosimetry Measurements for the Advanced Test Reactor Critical Facility in support of Advanced Sensors and Instrumentation Development

Reactor dosimetry measurements are commonly used to validate simulation and modeling in nuclear reactor tests. Numerous standard dosimeter materials exist which are commonly utilized with sensitivities to different energy ranges of neutrons. At the Advanced Test Reactor (ATR), cobalt alloy and pure nickel wires are installed every cycle to monitor thermal- and fast-neutron fluence rates. However, there is growing interest in exploring less commonly used materials which are either more sensitive to different parts of the neutron energy spectrum or which can incorporate multiple activation paths in a single material. Epithermal and fast-neutron energies beyond the typical 1-MeV threshold are of particular interest. Two ATR-C Flux Runs took place during 2024; each flux run included supplemental dosimetry packages in the South-East Flux Trap (SEFT) Filler. The focus of the dosimetry package for flux run 23-4 was to test two novel dosimetry methods that can provide simultaneous thermal and threshold (fast) sensitivity in a single dosimeter wire. A selection of 3% Au in Cu alloyed wires were available that provided sensitivity to fast and thermal neutrons through 5 different reactions. Likewise, Fe offers multiple interaction pathways with sensitivity to both thermal and fast neutrons. The main question to be answered by these irradiations was if sufficient radioactivation would take place in the ATR-C SEFT during a nominal 20-minute irradiation at typical power levels (near 600Wth) to observe the threshold reactions that have smaller activation cross-sections than the thermal reactions without being saturated by interfering interactions and Compton continuum during the High-Purity Germanium (HPGe) measurements. The results from comparing the measurement results to anticipated activity levels provide confidence in our ability to activate both traditional and novel dosimetry materials in ATR-C, however not all the measured values matched with the predicted activities. This leaves further room for investigation both on the experimental and computational approaches for future irradiation experiments.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2017

On-Line Monitoring to Detect Sensor and Process Degradation under Normal Operational Transients - 327

Calibration assessment of nuclear plant sensors and transmitters is currently performed through periodic maintenance actions during plant refueling outages. On-line monitoring (OLM) can be used to reduce the time and cost of this maintenance process by using empirical models to evaluate sensor calibration. The implementation of OLM can also mitigate human error associated with unnecessary maintenance on sensors operating within calibration tolerances. OLM has been studied and validated for plants under steady-state baseload operation, but the extension to load-following operation is not well-documented in the literature. Auto-associative kernel regression (AAKR) provides an error-corrected prediction of process sensor measurements for OLM through a weighted average of historic data. The current study evaluates the performance of AAKR for prediction under normal operational transients (such as expected under load-following operation). Performance of the AAKR model with two anomaly detection routines, error uncertainty limit monitoring (EULM) and sequential probability ratio test (SPRT), was evaluated for normal operation, sensor faults, and process degradation. The data used to develop and evaluate the OLM system were collected from a forced flow loop with temperature and flow sensors. Three models of well-correlated groups of sensors are trained to predict normal system behavior under planned transients. The anomaly detection routines are tested for sensor faults and process faults. The results of this experimental study suggest AAKR and an appropriate anomaly detection routine can be applied for sensor calibration and process fault detection under transient operation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

A novel imaging technique for measuring kinematics of light-weight flexible structures

A new imaging algorithm is proposed to capture the kinematics of flexible, thin, light structures including frequencies and motion amplitudes for real time analysis. The studied case is a thin flexible beam that is preset at different angles of attack in a wind tunnel. As the angle of attack is increased beyond a critical value, the beam was observed to undergo a static deflection that is ensued by limit cycle oscillations. Imaging analysis of the beam vibrations shows that the motion consists of a superposition of the bending and torsion modes. The proposed algorithm was able to capture the oscillation amplitudes as well as the frequencies of both bending and torsion modes. The analysis results are validated through comparison with measurements from a piezoelectric sensor that is attached to the beam at its root.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Detecting outliers in streaming time series data from ARM distributed sensors

The Atmospheric Radiation Measurement (ARM) Data Center at ORNL collects data from a number of permanent and mobile facilities around the globe. The data is then ingested to create high level scientific products. High frequency streaming measurements from sensors and radar instruments at ARM sites require high degree of accuracy to enable rigorous study of atmospheric processes. Outliers in collected data are common due to instrument failure or extreme weather events. Thus, it is critical to identify and flag them. We employed multiple univariate, multivariate and time series techniques for outlier detection methods and studied their effectiveness. First, we examined Pearson correlation coefficient which is used to measure the pairwise correlations between variables. Singular Spectrum Analysis (SSA) was applied to detect outliers by removing the anticipated annual and seasonal cycles from the signal to accentuate anomalies. K-means was applied for multivariate examination of data from collection of sensors to identify any deviation from expected and known patterns and identify abnormal observation. The Pearson correlation coefficient, SSA and K-means methods were later combined together in a framework to detect outliers through a range of checks. We applied the developed method to data from meteorological sensors at ARM Southern Great Plains site and validated against existing database of known data quality issues.

Lu, Yuping↗
DOE OSTI2024

DOE Cold Climate Heat Pump Challenge: Development, Metrics, and Early Field Observations

Space heating in residential buildings is a major contributor of Greenhouse Gas (GHG) emissions in the United States. New, advanced electric heat pumps are poised to provide a low carbon alternative to traditional fossil-based heating, especially in colder climates. Widespread deployment of cold climate heat pumps could help address the significant portion of building emissions and primary energy used in American households, but these gains will require broader acceptance from consumers and decision makers. As part of the “Energy, Emissions, and Equity” (E3) initiative, the U.S. Department of Energy launched the Cold Climate Heat Pump Challenge (CCHP) in 2021 to accelerate the deployment of the next generation of air source heat pumps. The Challenge is currently focused on residential, centrally ducted, electric heat pumps, with a nominal cooling capacity greater than or equal to 24,000 Btu/h (2 tons) and less than or equal to 65,000 Btu/h (5 tons). The Challenge specifications represent a best-in-class heat pump product that provides high-efficiency heating performance in cold climates, employs environmentally friendly low-Global Warming Potential (GWP) refrigerants, and is designed to be grid interactive. Spearheaded by DOE in partnership with the US Environmental Protection Agency (EPA) and Natural Resources Canada (NRCan), the CCHP Challenge brings together numerous major heat pump manufacturers and key stakeholders including utilities and state agencies across the country. In addition to performance testing in a laboratory environment, a key component of this research is evaluating the in-field performance of the prototype heat pumps developed as part of the Challenge. This paper will discuss the development of the CCHP Challenge, key performance specifications, and the energy and non-energy metrics that will be evaluated through the field study. The types of data that are being collected from the units installed in homes across North America as part of the field validation will be described, including details about the sensors and data acquisition system. Data cleaning and analysis methodologies will be discussed, along with early observations from the winter 2022-2023, spring and summer performance periods, and an assessment of non-energy metrics through pre- and post-installation homeowner surveys. Challenges uncovered and lessons learned throughout the process will also be discussed. Finally, the paper will discuss next steps for the second winter assessment period, areas needing additional research, and potential applications of the data collected and analyzed through this work.

Mendon, Vrushali V.↗
DOE OSTI2013

Leak Detection and H2 Sensor Development for Hydrogen Applications

Recent developments in the search for sustainable and renewable energy coupled with the advancements in fuel cell powered vehicles (FCVs) have augmented the demand for hydrogen safety sensors. There are several sensor technologies that have been developed to detect hydrogen, including deployed systems to detect leaks in manned space systems and hydrogen safety sensors for laboratory and industrial usage. Among the several sensing methods electrochemical devices that utilize high temperature-based ceramic electrolytes are largely unaffected by changes in humidity and are more resilient to electrode or electrolyte poisoning. The desired sensing technique should meet a detection threshold of 1% (10,000 ppm) H 2 and response time of ≤1 min, which is a target for infrastructure and vehicular uses. Further, a review of electrochemical hydrogen sensors by Korotcenkov et.al and the report by Glass et.al suggest the need for inexpensive, low power, and compact sensors with long-term stability, minimal cross-sensitivity, and fast response. This view has been largely validated and supported by the fuel cell and hydrogen infrastructure industries by the NREL/DOE Hydrogen Sensor Workshop held on June 8, 2011. Many of the issues preventing widespread adoption of best-available hydrogen sensing technologies available today outside of cost, derive from excessive false positives and false negatives arising from signal drift and unstable sensor baseline; both of these problems necessitate the need for unacceptable frequent calibration.

08 HYDROGEN↗
DOE OSTI2015

Optimization of Thermal Neutron Converter in SiC Sensors for Spectral Radiation Measurements

Optimization of the neutron converter in SiC sensors is presented. The sensors are used for spectral radiation measurements of thermal and fast neutrons and optionally gamma ray at elevated temperature in harsh radiation environment. The neutron converter, which is based on 10B, allows to detect thermal neutrons by means of neutron capture reaction. Two construction of the sensors were used to measure radiation in experiments. Sensor responses collected in experiments have been reproduced by the computer tool created by authors, it allows to validate the tool. The tool creates the response matrix function describing the characteristic of the sensors and it was used for detailed analyses of the sensor responses. Obtained results help to optimize the neutron converter in order to increase thermal neutron detection. Several enhanced construction of the sensors, which includes the neutron converter based on {sup 10}B or {sup 6}Li, were proposed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Advanced WEC Dynamics and Controls, Test 1

Numerous studies have shown that advanced control of a wave energy converter's (WEC's) power take off (PTO) can provide significant increases (on the order of 200-300%) in WEC energy absorption. Transitioning these control approaches from simplified paper studies to application in full-scale devices remains an open and extremely challenging problem will be central to creating economically competitive WECs and delivering clean renewable energy to the US electrical grid. The Advanced WEC Dynamics and Controls project is targeted on assisting WEC developers to apply novel control systems for their devices and thus achieving major increases in performance and economic viability. The success of any control strategy is based directly upon the availability of a reduced-order model with the ability to accurately capture the dynamics of the system with sufficient accuracy. A model-scale WEC was designed and fabricated for use in studies to advance the state-of-the-art in WEC controls. This test, which is the first in a series of planned tests, focused on system identification (system ID) and model validation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Force Sensing in Nanoscale: Integration of Virtual Spring for Sensing the Interactions of β-Amyloid Grabbed by Prefoldin

Analysis of atomic forces in molecular scale including the reciprocal internal force directions, quantity, and Young’s modulus, and more detailed analysis via diagrams, is one of the most current research topics. This research aims to analyze the bilateral forces applied between a nano-actuator and a cargo from a new perspective. While virtual springs have been used as a tool to convey forces in the previous steered molecular dynamics simulations, this study uses the spring as a sensor for measuring the internal forces. This study introduces atomic sensing via the steered molecular dynamics method. Following the previous studies, the protein employed in this study is the mutated Archetype Prefoldin being used to control the pathogenic cargo beta-amyloid (Alzheimer’s). The powerful Gromacs software carries out the simulations for the calculation of the total force and force for each branch. The simulation results illustrate the total force between the cargo and nano-actuator is ~ 2.8 nN, while each branch needs a force of ~ 1.2–1.5 nN to release the cargo. The results demonstrate the validity of the method and applicability of the virtual sensor for assessing the microscopic forces. This investigation is a pioneer study for the advent of the sensor as an assessment tool for the mechanical analysis and precise atomic force studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Demonstration of VOC Fenceline Sensors and Canister Grab Sampling near Chemical Facilities in Louisville, Kentucky

Experimental fenceline sensor pods (SPods) fitted with 30 s duration canister grab sampling (CGS) systems were deployed at a site near chemical facilities in Louisville, KY, from 4 June 2018 to 5 January 2020. The objective of the study was to better understand lower cost 10.6 eV photoionization detector (PID)-based volatile organic compound (VOC) sensors and investigate their utility for near-source emissions detection applications. Prototype SPods containing PID sensor elements from two different manufacturers yielded between 78% and 86% valid data over the study, producing a dataset of over 120,000 collocated pair fenceline measurements averaged into 5-min datapoints. Ten-second time-resolved SPod data from an elevated fenceline sensor signal day are presented, illustrating source emission detections from the direction of a facility 500 m west of the monitoring site. An SPod-triggered CGS acquired in the emission plume on this day contained elevated concentrations of 1,3-butadiene and cyclohexane (36 parts per billion by volume (ppbv) and 637 ppbv, respectively), compounds known to be emitted by this facility. Elevated concentrations of these compounds were observed in a subset of the 61 manual and triggered CGS grab samples acquired during the study, with winds from the west. Using novel wind-resolved visualization and normalization approaches described herein, the collocated pair SPod datasets exhibited similarity in emission source signature. With winds from the west, approximately 50% of SPod readings were above our defined theoretical detection limit indicating persistent measurable VOC signal at this site. Overall, this 19-month study demonstrated reasonable prototype SPod operational performance indicating that improved commercial forms of lower cost PID sensors could be useful for select VOC fenceline monitoring applications.

1,3-butadiene↗
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗
DOE OSTI2019

The Terra Vega Active Light Source: A First Step in a New Approach to Perform Nighttime Absolute Radiometric Calibrations and Early Results Calibrating the VIIRS DNB

A fully automated, National Institute of Standards and Technology (NIST)-traceable artificial light source called Terra Vega has been developed to radiometrically calibrate the Visible Infrared Imaging Radiometer (VIIRS) Day Night Band (DNB) working in high gain stage (HGS) mode. The Terra Vega active point source is a calibrated integrating sphere that is only a fraction in size of a VIIRS DNB pixel. As such, it can be considered analogous to a ground-based photometric reference star. Vicarious calibrations that employ active point sources are different than those that make use of traditional extended sources and can be applyed to quantify the brightness of artificial light sources. The active source is successfully fielded, and early results indicate that it can be used to augment and validate the radiometric calibration of the VIIRS DNB HGS sensor on both the Suomi National Polar-orbiting Partnership (NPP) and NOAA-20 satellites. The VIIRS DNB HGS sensor can benefit from this technology as on-board calibration is challenging and hinges on transferring low gain stage (LGS) calibration using a solar diffuser to the medium gain stage (MGS) and HGS via regions of overlap. Current vicarious calibration methods that use a lunar-illuminated extended source estimate the HGS radiometric accuracy to within 8-15%. By comparison, early results and analysis showed that Terra Vega is stable to about 1%. Under clear dark night conditions, predicted top-of-atmosphere radiance from Terra Vega ranged between 1–11% of VIIRS measured values. Terra Vega’s excellent stability opens up new opportunities to validate and develop nighttime imaging applications based on point sources.

Ryan, Robert↗