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NASA NTRS2020

Design and Performance of the PALM-3000 3.5 kHz Upgrade

PALM-3000 (P3K), the second-generation adaptive optics (AO) instrument for the 5.1 meter Hale telescope atPalomar Observatory, was released as a facility class instrument in October 2011 and has since been used on-skyfor over 600 nights as a workhorse science instrument and testbed for coronagraph and detector development.In late 2019 P3K underwent a significant upgrade to its wavefront sensor (WFS) arm and real-time control(RTC) system to reinforce its position as a state-of-the-art AO facility and extend its faint-end capability forhigh-resolution imaging and precision radial velocity follow-up of Kepler and TESS targets. The main featuresof this upgrade include an EM-CCD WFS camera capable of 3.5 kHz framerates, and an advanced Digital SignalProcessor (DSP) based RTC system to replace the aging GPU based system. Similar to the pre-upgrade system,the Shack-Hartmann wavefront sensor supports multiple pupil sampling modes using a motorized lenslet stage.The default sampling mode with 64×64 subapertures has been re-commissioned on-sky in late 2019, witha successful return to science observations in November 2019. In 64×mode the upgraded system is alreadyachieving K-band Strehl ratios up to 85% on-sky and can lock on natural guide stars as faint as mV=16. A16×16 subaperture mode is scheduled for on-sky commissioning in Fall 2020 and will extend the system’s faintlimit even further. Here we present the design and on-sky re-commissioning results of the upgraded system,dubbed P3K-II.

Vasisht, Gautam↗

NASA NTRS2021

Tracking aerosols and SO2 clouds from the Raikoke eruption: 3D view from satellite observations

The 21 June 2019 eruption of the Raikoke volcano (Kuril Islands, Russia; 48° N, 153° E) produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. The dispersed SO2 and sulfate aerosols in the stratosphere were still detectable by multiple satellite sensors for many months after the eruption. For this study of SO2 and aerosol clouds we use data obtained from two of the Ozone Mapping and Profiler Suite sensors on the Suomi National Polar-orbiting Partnership satellite: total column SO2 from the Nadir Mapper and aerosol extinction profiles from the Limb Profiler as well as other satellite data sets. We evaluated the limb viewing geometry effect (the “arch effect”) in the retrieval of the LP standard aerosol extinction product at 674 nm. It was shown that the amount of SO2 decreases with a characteristic period of 8–18 d and the peak of stratospheric aerosol optical depth recorded at a wavelength of 674 nm lags the initial peak of SO2 mass by 1.5 months. Using satellite observations and a trajectory model, we examined the dynamics of an unusual atmospheric feature that was observed, a stratospheric coherent circular cloud of SO2 and aerosol from 18 July to 22 September 2019.

Eruption of the Raikoke volcano↗

NASA NTRSDate not supplied

All-sky Retrieval of Atmospheric Temperature, Water Vapor, Clouds, Trace Gases, and Surface Properties from Operational Hyperspectral IR Sounders

Operational IR sounders such AIRS, CrIS, and IASI provide high quality hyperspectral measurements for weather and climate applications. We will describe a new all-sky Single Field-of-view Sounder Atmospheric Product (SiFSAP). The uniqueness of this product is that it uses all available channels from hyperspectral sounders and the optimal estimation retrieval is done at a single FOV spatial resolution. The SiFSAP includes atmospheric temperature, water vapor, clouds, trace gases, surface skin, and surface emissivity and will be produced operationally at NASA GES DISC. We will describe the core component of the SiFSAP algorithm, which is the Principal Component-based Radiative Transfer Model (PCRTM), and will show example applications of the SiFSAP product for various atmospheric weather and dynamics studies. We also describe a new Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides fast and accurate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

pcrtm↗

NASA NTRSDate not supplied

Remote Sensing of Atmospheric Temperature, Water Vapor, Trace Gases, Cloud, and Surface Properties on Daily and Decadal Time Scales

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗

NASA NTRSDate not supplied

Machine Learning for Dynamic Test Sensor Placement

There are multiple different algorithms to perform modal test sensor placement optimization: effective independence, residual kinetic energy, iterative Guyan reduction, genetic algorithms, or a brute-force methodology. However, any of these methods may be computationally expensive, especially for structural models with a large number of degrees of freedom. Given the high-cost and the need to optimize the solution, modal sensor placement is a great application for machine learning (ML) algorithms. In this paper, we will apply ML algorithms to determine the optimal sensor locations for simple and complex structures. We will also discuss the benefits and drawbacks of using machine learning over other sensor placement algorithms.

Kelsey Buckles↗

NASA NTRSDate not supplied

A Spectral Fingerprinting Method for Deriving Consistent Climate Data Records from Multiple Satellite IR Sounders

Deriving Climate Data Records (CDRs) from multiple IR sounders such AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C requires from current operational products are challenging due to different radiative transfer models and retireavl algorithms used for processing level 2 data. We developed a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm, which uses a single set of radiative kernels a robust spectral fingerprinting method to performs retrievals using spatiotemporally averaged L1 hyperspectral radiances directly. The ClimFiSP algorithm provides accurate data fusion CDR products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. We plan to add IASI to the CDR data set in the future. The ClimFiSP is being transitioned to NASA GES DISC data center for public access.

Xu Liu↗

NASA NTRSDate not supplied

Metabolic Vessel for Impedance Spectroscopy and Electrochemistry (MVISE): The Ground Mapping Unit for the Lunar Explorer Instrument for Space Biology Applications (LEIA)

The BioSensor payload on the upcoming LEIA platform aboard a CLPS lander will carry yeast to the moon to study response to radiation and lunar gravity. The LEIA BioSensor is designed to monitor metabolic activity using absorbance in conjunction with alamarBlue for measuring colorimetric changes as proxy measurement for redox potential. The science data returned from small spacecraft mission modules like LEIA is limited as it relies solely on optical measurements, necessitating a corresponding ground mapping unit that is equipped with multiple electrochemical sensors for accurate mapping of the optical data and operates fully automatically. This technology development work discusses the extensive design and optimization efforts put into the ground mapping unit, MVISE. MVISE is a custom designed, 3D-printed vessel with an agitation system, with six different electrochemical sensor probes, and ports for sample collection and a pressure release valve. The sensors provide real-time data, with dry absorbance measurements aligning with LEIA flight hardware and wet measurements demonstrating invasive sensor design enabling a comparison between the two setups. The 3D printer resin was tested for mechanical robustness, biocompatibility, and resistance to autoclave sterilization. The inner walls were coated with food-grade epoxy, ensuring a smooth finish to prevent microbial lodging and dye staining. The MVISE has successfully passed a week-long leak test and is now undergoing active biology tests. The MVISE prototype will be prepared for radiation tests, with three identical units being tested for varying radiation levels and culture compositions at the NASA Space Radiation Laboratory in November 2024. The integrated sensor approach proposed in this work will enable the accurate mapping of the BioSentinel/LEIA optical flight data to six sensor parameters on the ground unit for better science data return and will enable the first effort to evaluate classical biochemical sensor measurements by comparing and contrasting their responses.

Chinmayee Govinda Raj↗

DOE OSTI2021

Multielectrode sensor for concentration and depth measurements in molten salt

A multi electrode sensor that provides in-situ, real time measurements for molten salts and other process fluids such as real-time concentration and salt level measurements for nuclear systems such as molten salt reactors, nuclear reprocessing facilities utilizing molten salts and concentrated solar power systems. The sensor has multiple electrodes with unique lengths which are connected to a potentiostat. Measurements are taken when the electrodes are immersed in the process fluid.

Hoyt, Nathaniel C.↗

DOE OSTI2021

Enhanced heavy oil recovery by low salinity polymer flood combined with microgel treatment

“Heavy oil resources account for a large portion of the total oil reserves around the world. The target heavy oil reservoir is located on Alaska’s North Slope (ANS). Advantages of low-salinity HPAM polymer (LSP) over high-salinity polymer (HSP) were demonstrated. LSP could recover more oil with 40% less polymer consumption. No additional oil was recovered by HSP after LSP flood. The first-ever polymer flood pilot on ANS showed remarkable success regarding water cut reduction, oil production increase, delayed breakthrough, and projected oil recovery improvement. Polymer alone was insufficient to achieve satisfactory recovery as the reservoirs were highly heterogeneous. Microgels could improve the effectiveness of polymer flood by reducing water cut and increasing oil recovery. Favorable working conditions were identified. Microgel transport behavior was studied using superpermeable sandpacks (27-221 darcies) with multiple pressure sensors. The particle-to-pore matching size ratio significantly impacted the effectiveness of the gels. A threshold differential pressure (ΔPth) and critical pressure gradient (∇Pcr) were required to push the gels to penetrate and propagate through the channels. The ΔPth and ∇Pcr revealed the underlying mechanisms of selective penetration/placement behavior of microgels in heterogeneous reservoirs. Diagrams were developed to estimate the maximum propagation distance of the gels in channels in conceptual field applications. Sandwich-like channel models and methodologies were developed to comprehensively evaluate the effectiveness of gel materials. Gel retention in the channels was quantified. Results also indicated that the retained gels were dehydrated. Fluid diversion and sweep improvement after gel treatments were evaluated by tracer tests”--Abstract, page iv.

Zhao, Yang↗

DOE OSTI2022

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

DOE OSTI2022

28 NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning: Preprint

AI↗

DOE OSTI2026

Alpha-Imaging Detector System Development for Large Area Monitoring

Effective management and disposal of legacy nuclear waste are essential for ensuring safe work environments and minimizing environmental impacts. Monitoring airborne actinide contamination is particularly critical due to the high internal dose potential of alpha-emitting radionuclides. Traditional continuous air monitoring systems (CAMs) used in the industry are limited in the volume of air they can sample, potentially leading to inaccurate radiation detection over large areas. For example, in 2018, elevated levels of airborne Plutonium-239 were detected beyond the controlled areas of the Hanford Plutonium Finishing Plant, highlighting the potential risks to both plant workers and nearby residents. To address these challenges, high-efficiency particulate absorbing (HEPA) air purifiers can enhance air flow by up to 1.5 orders of magnitude, thereby increasing monitoring efficiency and providing a cost-effective solution for large-area surveillance. To quantify the activities of alpha-emitting radionuclides on HEPA filters, the Savannah River National Laboratory is developing an advanced alpha-imaging detection system. This system includes scintillating materials combined with a digital scientific camera. A significant concern in operating such a large-area airborne monitoring system is the handling of HEPA filters, which may be contaminated with radioactive particles. To mitigate these hazards, it is crucial to ensure that any contamination is securely fixed onto the filters. Efforts have been made to optimize the sensitivity of scintillator-epoxy composites and apply them to HEPA filters. These materials were characterized using fluoroscence spectroscopy. These techniques confirmed the purity of the raw materials, the dispersion of scintillators in the epoxy matrix, and the stability of their optical and structural properties post-modification. The optimal scintillator-epoxy composite was selected for use on alpha-spiked HEPA filters to evaluate the efficiency of the sprayer. HEPA filters, embedded with alpha particles collected by an air purifier deployed in an airborne radiation area, have been tested to assess detection efficiency. Future work will focus on employing multiple imaging sensors simultaneously to enhance sensitivity across different regions of the HEPA filter.

Pham, Phuong [Savannah River National Laboratory (↗

NASA NTRS1990

Layered Internal-Photoemission Sensor

High quantum efficiency achieved without multiple layer contacts. Proposed infrared sensor based on photoemission from multiple layers of metal silicide sandwiched between layers of silicon. Suitable for use in focal-plane arrays. Layered internal-photoemission sensor has positive/intrinsic/negative structure modified by inclusion of layers or islands of metal silicide. Holes photo-excited from silicide regions and swept to detection by reverse-bias electric field in intrinsic silicon. Easier to make because internal layers allowed to "float" electrically, contact being made with only two outer semiconductor layers.

Fathauer, Robert W.↗

DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are at the forefront in advancing the way we interact and perceive with the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as Radar, LiDAR and Video Imaging which are the most popular modalities for ITS. Real-time perception data from these sensors allows intelligent infrastructure side decision making to improve the energy, efficiency and safety at traffic intersections. As traffic departments across the United States are transitioning from traditional loop detectors / emulators and embracing newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception which is reliable, inexpensive, easy to setup and has robust performance in varying weather conditions. However, choosing a sensor which checks all boxes is not straightforward as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long range vehicles and weather resistance but lacks high resolution. LiDAR is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines Radar, LiDAR and camera sensors capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. Through this evaluation, we hope to draw attention to the necessity of National Renewable Energy Laboratory's (NREL) Infrastructure Perception and Control (IPC) framework which presents a multi-sensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like Radar, LiDAR and cameras, offers the most robust solution for enhancing the safety and efficiency in intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Thin-Film Embedded Sensors for Battery Health Monitoring

Hybrid or all-electric aircraft are being developed as the next generation of aircraft to both allow new forms of aviation and decrease environmental impact. Since these types of aircraft are based on high-capacity battery technology, safe operation of these batteries becomes increasingly important. In particular, the potential for battery failure due to uncontrolled chemical reactions resulting in thermal runaway, catastrophic failure, and battery fires must be addressed in order for such battery technology to have the level of safety needed for standard aviation implementation. Efforts to ensure battery safety often involve engineering solutions that seek to contain rather than prevent such events by early detection. Such approaches increase the system weight and decrease the power per unit mass provided by the battery system. Existing methods for measuring battery parameters to determine the battery state-of-health are limited. These methods include electrical measurements of the cell current and/or voltage output as well as temperature measurements taken externally on the cell surface. Such external temperature measurements are limited in their ability to provide early warning of impending battery failure. In response, an effort to develop sensors operating internal to battery for health monitoring has been ongoing in the NASA Sensor-based Prognostics to Avoid Runaway Reactions & Catastrophic Ignition (SPARRCI) project. The basic approach associated with this sensor work is the deposition of thin film sensors on the battery separator located between the anode and cathode of the battery. These thin film sensors are then monitored to determine changes in battery parameters and health. Microfabrication techniques are employed to minimize the overall impact of the sensors on battery operation through the implementation of sensors with minimal size, weight, and power consumption. The thickness of the films, which are fabricated through physical vapor deposition (sputtering), are on the order of thousands of angstroms and can have minimal surface area. Thin film sensors for system health management have been implemented for a many decades on complex components for aerospace applications. However, the application of thin films of this type on a battery separator for internal battery monitoring applications has not previously been demonstrated to our knowledge. This paper describes the development of sensors for the internal battery monitoring through the use of thin film sensor technology. Thin metal films were successfully deposited on a battery separator polymer material with good adherence and electrical continuity. Multiple types of sensors have been deposited, as well as lead connections from the sensor to the edge of the separator material. The ability of these thin film sensors immersed in electrolyte to perform multiple types of battery parameter measurements has been demonstrated. For example, a multiparameter sensor system measured multiple properties simultaneously inside of a pouch cell over a wide temperature range. Further, real time measurement of interior temperature changes in a battery pouch cell with an integrated interior temperature sensor was demonstrated. These changes include detecting a fault in the battery (shorting) in situ with rapid response time (less than a minute) corresponding to a more limited response by a temperature sensor mounted externally. Other aspects of monitoring battery health were also explored, such as real-time measurement of simulated dendrite growth/metal deposition by sensor on separator material demonstrated. Future efforts will include improvements in the durability of the sensor structure to allow introduction of the approach into standard battery fabrication techniques. Overall, this work is a step forward in providing a method to prevent catastrophic battery failures and provide a foundation for safer, lighter, and higher energy batteries for the electric aircraft industry.

thin film battery health↗

NASA NTRS2007

In Situ Multi-Species (O2, N2, Fuel, Other) Fiber Optic Sensor for Fuel Tank Ullage

A rugged and compact fiber optic sensor system for in situ real-time measurement of nitrogen (N2), oxygen (O2), hydrocarbon (HC) fuel vapors, and other gases has been developed over the past several years at Glenn Research Center. The intrinsically-safe, solid-state fiber optic sensor system provides a 1% precision measurement (by volume) of multiple gases in a 5-sec time window. The sensor has no consumable parts to wear out and requires less than 25 W of electrical power to operate. The sensor head is rugged and compact and is ideal for use in harsh environments such as inside an aircraft fuel tank, or as a feedback sensor in the vent-box of an on-board inert gas generation system (OBIGGS). Multiple sensor heads can be monitored with a single optical detection unit for a cost-effective multi-point sensor system. The present sensor technology is unique in its ability to measure N2 concentration directly, and in its ability to differentiate different types of HC fuels. The present sensor system provides value-added aircraft safety information by simultaneously and directly measuring the nitrogen-oxygen-fuel triplet, which provides the following advantages: (1) information regarding the extent of inerting by N2, (2) information regarding the chemical equivalence ratio, (3) information regarding the composition of the aircraft fuel, and (4) by providing a self-consistent calibration by utilizing a singular sensor for all species. Using the extra information made available by this sensor permits the ignitability of a fuel-oxidizer mixture to be more accurately characterized, which may permit a reduction in the amount of inerting required on a real-time basis, and yet still maintain a fire-safe fuel tank. This translates to an increase in fuel tank fire-safety through a better understanding of the physics of fuel ignition, and at the same time, a reduction in compressed bleed air usage and concomitant aircraft operational costs over the long-run. The present fiber optic sensor can also be used as a false-alarm-free engine/hidden/cargo space fire detector (by measuring increased CO2 and CO, and decreased O2), a multi-point in situ measurement and certification system for halogenated-compound fire protection systems, and for the testing and certification of other aircraft safety sensor systems. The technology (LEW-17826-1) developed in the present sensor system is patent pending.

Nguyen, Quang-Viet↗

NASA NTRS1994

Improved Fiber-Optic-Coupled Pressure And Vibration Sensors

Improved fiber-optic coupler enables use of single optical fiber to carry light to and from sensor head. Eliminates problem of alignment of multiple fibers in sensor head and simplifies calibration by making performance both more predictable and more stable. Sensitivities increased, sizes reduced. Provides increased margin for design of compact sensor heads not required to contain amplifier circuits and withstand high operating temperatures.

Zuckerwar, Allan J.↗

NASA NTRS2008

Measuring Ultrasonic Acoustic Velocity in a Thin Sheet of Graphite Epoxy Composite

A method for measuring the acoustic velocity in a thin sheet of a graphite epoxy composite (GEC) material was investigated. This method uses two identical acoustic-emission (AE) sensors, one to transmit and one to receive. The delay time as a function of distance between sensors determines a bulk velocity. A lightweight fixture (balsa wood in the current implementation) provides a consistent method of positioning the sensors, thus providing multiple measurements of the time delay between sensors at different known distances. A linear fit to separation, x, versus delay time, t, will yield an estimate of the velocity from the slope of the line.

Source record↗