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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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NASA NTRS1988

NASA Aircraft-Satellite Instrument Calibration Project

Ground based prelaunch calibration of satellite instruments usually is not adequate to provide an accurate characterization of the in-orbit performance of a satellite instrument. This is because the ground calibrations may not simulate the in-orbit environment observations of the satellite sensor, or because the sensor characteristics have changed during launch and in-orbit operations. One technique to obtain a meaningful in-orbit calibration of satellite sensors is to acquire simultaneous observations of an Earth scene with the satellite and a well calibrated aircraft or shuttle sensor which has similar characteristics to the satellite sensor. This is a direct in-orbit calibration technique and is usually called vicarious calibration. The experiment with the control instrument must occur above the sensible atmosphere as measured by the satellite sensor to provide a useful improvement to the calibration of the satellite sensor. Some observations on the experiments are made.

Guenther, B. W.↗

NASA NTRS1977

A study of selected environmental quality remote sensors for free flyer missions launched from the space shuttle

The sensors were examined for adaptability to shuttle by reviewing pertinent information regarding sensor characteristics as they related to the shuttle and Multimission Modular Spacecraft environments. This included physical and electrical characteristics, data output and command requirements, attitude and orientation requirements, thermal and safety requirements, and adaptability and modification for space. The sensor requirements and characteristics were compared with the corresponding shuttle and Multimission Modular Spacecraft characteristics and capabilities. On this basis the adaptability and necessary modifications for each sensor were determined. A number of the sensors were examined in more detail and estimated cost for the modifications was provided.

Goldstein, H. W.↗

NASA NTRS2021

Novel Fiber Optic Sensing Arrays with Enhanced Sensitivity in Cryogenic Temperatures

State-of-the-art instrumentation techniques have provided an opportunity to obtain greater insight into the characteristics of cryogenic liquid storage. Optical fiber sensors that utilize fiber Bragg gratings are a viable option for temperature sensing in volatile liquid such as liquid oxygen because there is no risk of electrical sparking and no electromagnetic interference. The National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center (Edwards, California) has developed a novel, simple-to-fabricate fiber array sensor that has been tested and found to be three times more sensitive than typical fiber sensors under cryogenic condition (traditional fiber sensors can suffer from reduced sensitivity below 100 K). Cryogenic temperature monitoring using the NASA fiber optic sensing system is demonstrated in a simulated cryostatic condition, as well as in an elevated pressure environment under liquid nitrogen. Measurement accuracy is compared with traditional silicon diodes. Instrument installation, sensor characteristics, and experimental results are discussed in detail.

Hon Man Chan↗

NASA NTRS1993

Fiber-Optic Strain Sensors With Linear Characteristics

Fiber-optic modal domain strain sensors having linear characteristics over wide range of strains proposed. Conceived in effort to improve older fiber-optic strain sensors. Linearity obtained by appropriate choice of design parameters. Pattern of light and dark areas at output end of optical fiber produced by interference between electromagnetic modes in which laser beam propagates in fiber. Photodetector monitors intensity at one point in pattern.

Egalon, Claudio O.↗

NASA NTRS2010

Smart Sensor Demonstration Payload

Sensors are a critical element to any monitoring, control, and evaluation processes such as those needed to support ground based testing for rocket engine test. Sensor applications involve tens to thousands of sensors; their reliable performance is critical to achieving overall system goals. Many figures of merit are used to describe and evaluate sensor characteristics; for example, sensitivity and linearity. In addition, sensor selection must satisfy many trade-offs among system engineering (SE) requirements to best integrate sensors into complex systems [1]. These SE trades include the familiar constraints of power, signal conditioning, cabling, reliability, and mass, and now include considerations such as spectrum allocation and interference for wireless sensors. Our group at NASA s John C. Stennis Space Center (SSC) works in the broad area of integrated systems health management (ISHM). Core ISHM technologies include smart and intelligent sensors, anomaly detection, root cause analysis, prognosis, and interfaces to operators and other system elements [2]. Sensor technologies are the base fabric that feed data and health information to higher layers. Cost-effective operation of the complement of test stands benefits from technologies and methodologies that contribute to reductions in labor costs, improvements in efficiency, reductions in turn-around times, improved reliability, and other measures. ISHM is an active area of development at SSC because it offers the potential to achieve many of those operational goals [3-5].

Schmalzel, John↗

DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Electronic Thermometer Readings

NASA Stennis' adaptive predictive algorithm for electronic thermometers uses sample readings during the initial rise in temperature and applies an algorithm that accurately and rapidly predicts the steady state temperature. The final steady state temperature of an object can be calculated based on the second-order logarithm of the temperature signals acquired by the sensor and predetermined variables from the sensor characteristics. These variables are calculated during tests of the sensor. Once the variables are determined, relatively little data acquisition and data processing time by the algorithm is required to provide a near-accurate approximation of the final temperature. This reduces the delay in the steady state response time of a temperature sensor. This advanced algorithm can be implemented in existing software or hardware with an erasable programmable read-only memory (EPROM). The capability for easy integration eliminates the expense of developing a whole new system that offers the benefits provided by NASA Stennis' technology.

Source record↗

NASA NTRS2011

Virtual Sensor Test Instrumentation

Virtual Sensor Test Instrumentation is based on the concept of smart sensor technology for testing with intelligence needed to perform sell-diagnosis of health, and to participate in a hierarchy of health determination at sensor, process, and system levels. A virtual sensor test instrumentation consists of five elements: (1) a common sensor interface, (2) microprocessor, (3) wireless interface, (4) signal conditioning and ADC/DAC (analog-to-digital conversion/ digital-to-analog conversion), and (5) onboard EEPROM (electrically erasable programmable read-only memory) for metadata storage and executable software to create powerful, scalable, reconfigurable, and reliable embedded and distributed test instruments. In order to maximize the efficient data conversion through the smart sensor node, plug-and-play functionality is required to interface with traditional sensors to enhance their identity and capabilities for data processing and communications. Virtual sensor test instrumentation can be accessible wirelessly via a Network Capable Application Processor (NCAP) or a Smart Transducer Interlace Module (STIM) that may be managed under real-time rule engines for mission-critical applications. The transducer senses the physical quantity being measured and converts it into an electrical signal. The signal is fed to an A/D converter, and is ready for use by the processor to execute functional transformation based on the sensor characteristics stored in a Transducer Electronic Data Sheet (TEDS). Virtual sensor test instrumentation is built upon an open-system architecture with standardized protocol modules/stacks to interface with industry standards and commonly used software. One major benefit for deploying the virtual sensor test instrumentation is the ability, through a plug-and-play common interface, to convert raw sensor data in either analog or digital form, to an IEEE 1451 standard-based smart sensor, which has instructions to program sensors for a wide variety of functions. The sensor data is processed in a distributed fashion across the network, providing a large pool of resources in real time to meet stringent latency requirements.

Wang, Roy↗

NASA NTRS2012

MODIS Radiometric Calibration Program, Methods and Results

As a key instrument for NASA s Earth Observing System (EOS), the Moderate Resolution Imaging Spectroradiometer (MODIS) has made significant contributions to the remote sensing community with its unprecedented amount of data products continuously generated from its observations and freely distributed to users worldwide. MODIS observations, covering spectral regions from visible (VIS) to long-wave infrared (LWIR), have enabled a broad range of research activities and applications for studies of the earth s interactive system of land, oceans, and atmosphere. In addition to extensive pre-launch measurements, developed to characterize sensor performance, MODIS carries a set of on-board calibrators (OBC) that can be used to track on-orbit changes of various sensor characteristics. Most importantly, dedicated and continuous calibration efforts have been made to maintain sensor data quality. This paper provides an overview of the MODIS calibration program, on-orbit calibration activities, methods, and performance. Key calibration results and lessons learned from the MODIS calibration effort are also presented in this paper.

Xiong, Xiaoxiong↗

NASA NTRS2019

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

ocean colour↗

DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Active Region Extent Assessment with X-rays (AREA-X)

The development of semiconductor sensors for new particle tracking detectors places increasing limits on sensor characteristics such as uniformity, size and shape of inefficient areas and size of active compared to inactive sensor areas. Accurately assessing these relatively subtle effects requires either measurements in particle beams or the modification of samples to be used in dedicated laser test setups. Active Region Extent Assessment with X-rays (AREA-X) has been developed as an alternative method for the fast, efficient and precise study of the active area of a semiconductor sensor. It uses a monochromatic, micro-focused X-ray beam with a 10–20 keV energy range as provided by several synchrotron beam lines and uses the photo current induced in the sensor to measure the depth of the responsive sensor volume. It can be used to study local inhomogeneities or inefficiencies, the overall extent of the active sensor volume and its shape and its localised application, which makes the need to gather statistics over a large area unnecessary, allowing for fast readout, which enables studies of the sensor behaviour at a range of external parameters, e.g. temperature or applied bias voltage. This paper presents the measurement concept and technical setup of the measurement, results from initial measurements as well as capabilities and limitations of the method.

47 OTHER INSTRUMENTATION↗

NASA NTRS1980

The spatial resolving power of earth resources satellites: A review

The significance of spatial resolving power on the utility of current and future Earth resources satellites is critically discussed and the relative merits of different approaches in defining and estimating spatial resolution are outlined. It is shown that choice of a particular measure of spatial resolution depends strongly on the particular needs of the user. Several experiments have simulated the capabilities of future satellite systems by degradation of aircraft images. Surprisingly, many of these indicated that improvements in resolution may lead to a reduction in the classification accuracy of land cover types using computer assisted methods. However, where the frequency of boundary pixels is high, the converse relationship is found. Use of imagery dependent upon visual interpretation is likely to benefit more consistently from higher resolutions. Extraction of information from images will depend upon several other factors apart from spatial resolving power: these include characteristics of the terrain being sensed, the image processing methods that are applied as well as certain sensor characteristics.

Townshend, J. R. G.↗

NASA NTRS1984

New in-flight calibration adjustment of the Nimbus 6 and 7 earth radiation budget wide field of view radiometers

In-flight calibration adjustments are developed to process data obtained from the wide-field-of-view channels of Nimbus-6 and Nimbus-7 after the failure of the Nimbus-7 longwave scanner on June 22, 1980. The sensor characteristics are investigated; the satellite environment is examined in detail; and algorithms are constructed to correct for long-term sensor-response changes, on/off-cycle thermal transients, and filter-dome absorption of longwave radiation. Data and results are presented in graphs and tables, including comparisons of the old and new algorithms.

Kyle, H. L.↗

NASA NTRS1999

Evaluation of the Linear Aerospike SR-71 Experiment (LASRE) Oxygen Sensor

The Linear Aerospike SR-71 Experiment (LASRE) was a propulsion flight experiment for advanced space vehicles such as the X-33 and reusable launch vehicle. A linear aerospike rocket engine was integrated into a semi-span of an X-33-like lifting body shape (model), and carried on top of an SR-71 aircraft at NASA Dryden Flight Research Center. Because no flight data existed for aerospike nozzles, the primary objective of the LASRE flight experiment was to evaluate flight effects on the engine performance over a range of altitudes and Mach numbers. Because it contained a large quantity of energy in the form of fuel, oxidizer, hypergolics, and gases at very high pressures, the LASRE propulsion system posed a major hazard for fire or explosion. Therefore, a propulsion-hazard mitigation system was created for LASRE that included a nitrogen purge system. Oxygen sensors were a critical part of the nitrogen purge system because they measured purge operation and effectiveness. Because the available oxygen sensors were not designed for flight testing, a laboratory study investigated oxygen-sensor characteristics and accuracy over a range of altitudes and oxygen concentrations. Laboratory test data made it possible to properly calibrate the sensors for flight. Such data also provided a more accurate error prediction than the manufacturer's specification. This predictive accuracy increased confidence in the sensor output during critical phases of the flight. This paper presents the findings of this laboratory test.

Ennix, Kimberly A.↗

NASA NTRS1999

Evaluation of the Linear Aerospike SR-71 Experiment (LASRE) Oxygen Sensor

Ennix, Kimberly A.↗

DOE OSTI2024

Conducting polymer-based electrochemical sensors: Progress, challenges, and future perspectives

Conducting polymers are promising due to their unique properties, such as excellent electrical and optical properties, physical and chemical stability, high conductivity, and effective redox properties with high-temperature stability and biocompatibility. Due to these properties, conducting polymers are useful in diverse applications like sensors, batteries, oil industries, biosensors, biomedicines, catalysis, cancer treatment, etc. This review article aims to discuss the recent trends and analysis of conducting polymer-based electrochemical sensors in diverse areas with all required sensor characteristics, such as the derived limit of detection, utilized techniques for the sensing analysis and derived linear dynamic range with the stability of the sensors. Conducting polymers and their nanocomposites-based electrochemical sensors have demonstrated exceptional capabilities towards detecting various biomolecules, heavy metals, pesticides, and viruses like SARS-COV-2. Incorporation of redox mediators, use of conducting hydrogels, and molecular imprinting are promising strategies for better performance of the derived sensor. The article has demonstrated the existing challenges and limitations and provided solutions in the field. In the future, conducting polymers-based electrochemical sensors can be utilized in wearable sensors and integrated with IoT devices for better reach in real-time applications. They can also be made more accessible with precise control and data output by following specific methodologies. Utilizing green and sustainable conducting polymers can be crucial in advancing eco-friendly practices in the future. Conducting polymer-based electrochemical sensors has affectivity in neurochemical and pathogen sensing, which is essential for brain function and mental health.

42 ENGINEERING↗

DOE OSTI2021

Selection of the silicon sensor thickness for the Phase-2 upgrade of the CMS Outer Tracker

During the operation of the CMS experiment at the High-Luminosity LHC the silicon sensors of the Phase-2 Outer Tracker will be exposed to radiation levels that could potentially deteriorate their performance. Previous studies had determined that planar float zone silicon with n-doped strips on a p-doped substrate was preferred over p-doped strips on an n-doped substrate. The last step in evaluating the optimal design for the mass production of about 200 m$^{2}$ of silicon sensors was to compare sensors of baseline thickness (about 300 μm) to thinned sensors (about 240 μm), which promised several benefits at high radiation levels because of the higher electric fields at the same bias voltage. This study provides a direct comparison of these two thicknesses in terms of sensor characteristics as well as charge collection and hit efficiency for fluences up to 1.5 × 10$^{15}$ n$_{eq}$/cm$^{2}$. The measurement results demonstrate that sensors with about 300 μm thickness will ensure excellent tracking performance even at the highest considered fluence levels expected for the Phase-2 Outer Tracker.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗