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Data Simulation Using VLIDORT and PCRTM and its Use for Radiometric Calibration of Hyperspectral UV-NIR-SWIR Sensors

Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), a multiple-scattering multi-layer discrete ordinate scattering codes, coupled with a fast principal component (PC) radiative transfer model (PCRTM), we can simulate the radiation fields for hyperspectral UV-NIR-SWIR sensors from 250nm to 2500nm with a very high speed. Since the accurate gas absorptions, molecules and aerosol/cloud particle scatterings, and the surface reflectance have been considered in these simulations, these model data can be used for satellite or airborne sensor design, characterization, or end-to-end system level uncertainty assessment. Comparison of the modelled data with measured results has been a general practice for operational satellite sensor calibrations, such as the Ozone Mapping and Profiler Suite (OMPS) onboard on SNPP and NOAA-20. This presentation will give more details of this simulation system and models. Some examples of the generation of simulations data for CLARREO PathFinder (CPF) and the Surface Biology and Geology (SBG), as well as some examples to use these data for CPF and OMPS radiometric calibration will be presented.

Xiaozhen Xiong↗
NASA NTRS1994

Computer modeling of the sensitivity of a laser water vapor sensor to variations in temperature and air speed

Currently, there is disagreement among existing methods of determining atmospheric water vapor concentration at dew-points below -40 C. A major source of error is wall effects which result from the necessity of bringing samples into the instruments. All of these instruments also have response times on the order of seconds. NASA Langley is developing a water vapor sensor which utilizes the absorption of the infrared radiation produced by a diode laser to estimate water vapor concentration. The laser beam is directed through an aircraft window to a retroreflector located on an engine. The reflected beam is detected by an infrared detector located near the laser. To maximize signal to noise, derivative signals are analyzed. By measuring the 2f/DC signal and correcting for ambient temperature, atmospheric pressure and air speed (which results in a Doppler shifting of the laser beam), the water vapor concentration can be retrieved. Since this is an in situ measurement there are no wall effects and measurements can be made at a rate of more than 20 per second. This allows small spatial variations of water vapor to be studied. In order to study the sensitivity of the instrument to variations in temperature and air speed, a computer program which generated the 2f, 3f, 4f, DC and 2f/DC signals of the instrument as a function of temperature, pressure and air speed was written. This model was used to determine the effect of errors in measurement of the temperature and air speed on the measured water vapor concentration. Future studies will quantify the effect of pressure measurement errors, which are expected to be very small. As a result of these studied, a retrieval algorithm has been formulated, and will be applied to data taken during the PEM-West atmospheric science field mission. Spectroscopic studies of the water vapor line used by the instrument will be used to refine this algorithm. To prepare for these studies, several lasers have been studied to determine their output frequency range and power.

Tucker, George F.↗
NASA NTRS1987

Sensor-based diagnosis using knowledge of structure and function

A system for fault detection and isolation called LES, developed at the Kennedy Space Center for the Space Shuttle's Launch Processing System, is a well-developed diagnostic system that is simultaneously model-based and sensor-based. This experiment has led to a surprising result: the failure of a sensor can not only be handled in precisely the same way as the failure of any other object, but may present an especially easy case. Classical rule-based diagnostic systems need to find out whether or not their sensors are telling them the truth before they can safely draw inferences from them. By contrast, while LES does treat sensors as a special case, it does so only because there may exist a short cut that allows them to be handled more simply than other objects. LES uses both structural and functional knowledge, and has found cases in which the structural knowledge can be economically replaced by the judicious use of functional relationships; LES' functional relationships are stored in exactly one place, so they must be inverted to determine hypothetical values for possibly faulty objects. The inversion process has been extended to include conditional functions not normally considered to have inverses.

Scarl, Ethan A.↗
NASA NTRS2012

Characterization Approaches to Place Invariant Sites on SI-Traceable Scales

The effort to understand the Earth's climate system requires a complete integration of remote sensing imager data across time and multiple countries. Such an integration necessarily requires ensuring inter-consistency between multiple sensors to create the data sets needed to understand the climate system. Past efforts at inter-consistency have forced agreement between two sensors using sources that are viewed by both sensors at nearly the same time, and thus tend to be near polar regions over snow and ice. The current work describes a method that would provide an absolute radiometric calibration of a sensor rather than an inter-consistency of a sensor relative to another. The approach also relies on defensible error budgets that eventually provides a cross comparison of sensors without systematic errors. The basis of the technique is a model-based, SI-traceable prediction of at-sensor radiance over selected sites. The predicted radiance would be valid for arbitrary view and illumination angles and for any date of interest that is dominated by clear-sky conditions. The effort effectively works to characterize the sites as sources with known top-of-atmosphere radiance allowing accurate intercomparison of sensor data that without the need for coincident views. Data from the Advanced Spaceborne Thermal Emission and Reflection and Radiometer (ASTER), Enhanced Thematic Mapper Plus (ETM+), and Moderate Resolution Imaging Spectroradiometer (MODIS) are used to demonstrate the difficulties of cross calibration as applied to current sensors. Special attention is given to the differences caused in the cross-comparison of sensors in radiance space as opposed to reflectance space. The radiance comparisons lead to significant differences created by the specific solar model used for each sensor. The paper also proposes methods to mitigate the largest error sources in future systems. The results from these historical intercomparisons provide the basis for a set of recommendations to ensure future SI-traceable cross calibration using future missions such as CLARREO and TRUTHS. The paper describes a proposed approach that relies on model-based, SI-traceable predictions of at-sensor radiance over selected sites. The predicted radiance would be valid for arbitrary view and illumination angles and for any date of interest that is dominated by clear-sky conditions. The basis of the method is highly accurate measurements of at-sensor radiance of sufficient quality to understand the spectral and BRDF characteristics of the site and sufficient historical data to develop an understanding of temporal effects from changing surface and atmospheric conditions.

Thome, Kurtis↗
NASA NTRS2021

Evaluation of 18 Satellite- and Model-Based Soil Moisture Products Using in Situ Measurements From 826 Sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E SWI , SMOS SWI , AMSR2 SWI , and ASCAT SWI , with the L-band-based SMAPL3E SWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI SWI ), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3E SWI . The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Hylke E. Beck↗
NASA NTRS1990

A model for the space shuttle main engine high pressure oxidizer turbopump shaft seal system

A simple static model is presented which solves for the flow properties of pressure, temperature, and mass flow in the Space Shuttle Main Engine pressure Oxidizer Turbopump Shaft Seal Systems. This system includes the primary and secondary turbine seals, the primary and secondary turbine drains, the helium purge seals and feed line, the primary oxygen drain, and the slinger/labyrinth oxygen seal pair. The model predicts the changes in flow variables that occur during and after failures of the various seals. Such information would be particularly useful in a post flight situation where processing of sensor information using this model could identify a particular seal that had experienced excessive wear. Most of the seals in the system are modeled using simple one dimensional equations which can be applied to almost any seal provided that the fluid is gaseous. A failure is modeled as an increase in the clearance between the shaft and the seal. Thus, the model does not attempt to predict how the failure process actually occurs (e.g., wear, seal crack initiation). The results presented were obtained using a FORTRAN implementation of the model running on a VAX computer. Solution for the seal system properties is obtained iteratively; however, a further simplified implementation (which does not include the slinger/labyrinth combination) was also developed which provides fast and reasonable results for most engine operating conditions. Results from the model compare favorably with the limited redline data available.

Paxson, Daniel E.↗
NASA NTRS2013

Pressure-Sensitive Paints Advance Rotorcraft Design Testing

The rotors of certain helicopters can spin at speeds as high as 500 revolutions per minute. As the blades slice through the air, they flex, moving into the wind and back out, experiencing pressure changes on the order of thousands of times a second and even higher. All of this makes acquiring a true understanding of rotorcraft aerodynamics a difficult task. A traditional means of acquiring aerodynamic data is to conduct wind tunnel tests using a vehicle model outfitted with pressure taps and other sensors. These sensors add significant costs to wind tunnel testing while only providing measurements at discrete locations on the model's surface. In addition, standard sensor solutions do not work for pulling data from a rotor in motion. "Typical static pressure instrumentation can't handle that," explains Neal Watkins, electronics engineer in Langley Research Center s Advanced Sensing and Optical Measurement Branch. "There are dynamic pressure taps, but your costs go up by a factor of five to ten if you use those. In addition, recovery of the pressure tap readings is accomplished through slip rings, which allow only a limited amount of sensors and can require significant maintenance throughout a typical rotor test." One alternative to sensor-based wind tunnel testing is pressure sensitive paint (PSP). A coating of a specialized paint containing luminescent material is applied to the model. When exposed to an LED or laser light source, the material glows. The glowing material tends to be reactive to oxygen, explains Watkins, which causes the glow to diminish. The more oxygen that is present (or the more air present, since oxygen exists in a fixed proportion in air), the less the painted surface glows. Imaged with a camera, the areas experiencing greater air pressure show up darker than areas of less pressure. "The paint allows for a global pressure map as opposed to specific points," says Watkins. With PSP, each pixel recorded by the camera becomes an optical pressure tap. "Instead of having 100 or 200 pressure taps, you can have in theory several million, up to whatever the resolution of your camera is." Watkins explains that typical wind tunnel testing requires two models: one with very little instrumentation, and a pressure model with a significant amount of sensors applied. "If you can make all of your measurements on one model with PSP, you've decreased your model costs by at least a factor of two and preferably your testing costs by about that much," he says. PSP technology has been around for almost 20 years, but a PSP solution for gathering instantaneous dynamic pressure data from surfaces moving at high speeds, such as rotor blades, was not available until a NASA partnership led to a game-changing innovation.

Source record↗
NASA NTRS1991

Analysis of simulated image sequences from sensors for restricted-visibility operations

A real time model of the visible output from a 94 GHz sensor, based on a radiometric simulation of the sensor, was developed. A sequence of images as seen from an aircraft as it approaches for landing was simulated using this model. Thirty frames from this sequence of 200 x 200 pixel images were analyzed to identify and track objects in the image using the Cantata image processing package within the visual programming environment provided by the Khoros software system. The image analysis operations are described.

Kasturi, Rangachar↗
DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗
NASA NTRS2003

Fracture Evaluation of In-Situ Sensors for High Temperature Applications

The feasibility of fabricating an in-situ crack sensor for real-time detection of surface cracks propagating in engine components was evaluated using a computational fracture mechanics model. The in-situ sensor system would be required to: (1) Be capable of sustaining normal function in a severe environment; (2) Transmit a signal if a detected crack in the component was above a predetermined length, but below a critical length that would lead to failure; (3) Act neutrally upon the overall performance of the engine system and not interfere with the engine maintenance operations. In this work, fracture mechanics methodologies are used to identify the requirements for an in-situ sensor system that could withstand the engine operating environment, foreign object damage, and minimally degrade engine performance. A computational fracture mechanics model was developed to evaluate the feasibility of fabricating an in-situ crack sensor for real-time damage propagation detection in engine components.

Forth, Scott C.↗
NASA NTRS2002

Observer-Based Magnetic Bearing Controller Developed for Aerospace Flywheels

A prototype of a versatile, observer-based magnetic bearing controller for aerospace flywheels was successfully developed and demonstrated on a magnetic bearing test rig (see the photograph) and an actual flywheel module. The objective of this development included a fast, yet low risk, control development process, and a robust, high-performance controller for a large variety of flywheels. This required a good system model, an efficient development procedure, and a model-based controller that addressed the key problems associated with flywheel and bearing imbalance, sensor error, and vibration. The model used in this control development and tuning procedure included the flexible rotor dynamics and motor-induced vibrations. Such a model was essential for low-risk scheduling of speed-dependent control parameters and for reliable evaluation of novel control strategies. The successfully tested control prototype utilized an extended Kalman filter to estimate the true rotor principal-axis motion from the raw sensor position feedback. For control refinement, the extended Kalman filter also estimated and eliminated the combined effects of mass-imbalance and sensor runouts from the input data. A key advantage of the design based on the extended Kalman filter is its ability to accurately estimate both the rotor's principal-axis position and gyroscopic rates with the least amount of phase lag. This is important for control parameter scheduling to dampen the gyroscopic motions. Because of large uncertainties in the magnetic bearing and imbalance characteristics, this state-estimation scheme alone is insufficient for containing the rotor motion within the desired 1-mil excursion radius. A nonlinear gain adjustment based on an estimation of the principal-axis orbit size was needed to provide a coarse (nonoptimal), but robust, control of the orbit growth. Control current minimization was achieved with a (steepest gradient) search of synchronous errors in the principal-axis position input data. Actual flywheel tests of this observer-based controller (developed entirely in-house) at the NASA Glenn Research Center showed that the model correctly predicted the rotor orbit growth as a function of rotational speed, and it demonstrated the capability of gain adjustments to arrest this growth. Data from these tests on an actual flywheel module spun to 26,000 rpm proved that the controller was able to contain the shaft motion to within much less than 0.5 mils of radial excursion with axis currents less than 300 mA in root-mean-square estimate. The test speed range was limited because of thermal expansion concerns for this particular flywheel unit, not because of any deficiency in the controller. Simulations for this unit indicated that the controller should be robust up to its top operating speed of 60,000 rpm. Aside from these important achievements, and most significantly, it took less than 1 week to adapt this controller from the simple test rig to the actual flywheel and to demonstrate full five-axis levitation and control. This demonstration showed that both the controller and the model-based development and tuning framework are easily adaptable to a wide range of rotors and bearing configurations and, hence, are capable of reducing design risks and costs for many future flywheel technology developments.

Le, Dzu K.↗
DOE OSTI2024

Surface Meteorological System (MET) Instrument Handbook

The Surface Meteorological System (MET) consist mainly of conventional in situ sensors that obtain a defined “core” set of measurements. The core set of measurements is: Barometric Pressure (kPa), Temperature (°C), Relative Humidity (%), Arithmetic-Averaged Wind Speed (m/s), Vector-Averaged Wind Speed (m/s), and Vector-Averaged Wind Direction (deg). The sensors that collect the core variables are mounted at the standard heights defined for each variable: • Winds: 10 meters • Temperature and Relative Humidity: 2 meters • Barometric Pressure: 1 meter. Depending upon the geographical location, different models and types of sensors may be used to measure the core variables due to the conditions experienced at those locations. Most sites have additional sensors that measure other variables that are unique to that site or are well suited for the climate of the location but not at others.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

New devices for flow measurements: Hot film and burial wire sensors, infrared imagery, liquid crystal, and piezo-electric model

An experimental program aimed at identifying areas in low speed aerodynamic research where infrared imaging systems can make significant contributions is discussed. Implementing a new technique, a long electrically heated wire was placed across a laminar flow. By measuring the temperature distribution along the wire with the IR imaging camera, the flow behavior was identified.

Mcree, Griffith J., Jr.↗
NASA NTRS1995

Sea surface winds-A critical input to oceanic models, but are they accurately measured?

Wind, driving oceans, and the links between them to the atmosphere compose a critical parameter for the world circulation model as well as for the evaluation of climate changes. Traditionally, wind velocities have been reported by ships of oppurtunity and recorded on a network of buoys; they have also recently been generated by numerical weather prediction models and mapped with spaceborne remote sensors. Wind speeds from buoy measurements, ship observations, and model computations are compared, using the globally available altimeter returns that they have in common. Large, systematic deviations are found among the results obtained with these techniques, cautioning against use of these wind speeds.

Wu, Jin↗
NASA NTRS2004

Experimental Test-Bed for Intelligent Passive Array Research

This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.

Solano, Wanda M.↗
DOE OSTI2024

Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning

Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.

03 NATURAL GAS↗