Environmental monitoring by electronic nose sensors: a molecular modeling study
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monitoring↗
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Results for “sensor model”
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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Correlation of the SAGE III on ISS Sensor Assembly Thermal Model to Thermal Vacuum Testing
No abstract available
Amundsen, Ruth M.↗
Air Quality Analysis with Sensors, Satellites, and Models
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Air Quality↗
Air Quality Analysis with Sensors, Satellites, and Models
Poor air quality is a major global public health concern, which is only projected to get worse in coming years. A comprehensive understanding of current and potential future air quality and its key drivers spanning from local to global scales is needed to tackle this important problem. This presentation will outline the sources of information that we use to understand air quality, including ground-based measurements, satellites, and models. After giving an overview of these data sources and outlining their strengths and limitations, we will take a look at how they can be used together to give us a better picture of air quality locally and globally.
Carl Malings↗
Air Quality Data Fusion with Sensors, Satellites, and Models
Global forecasting models, satellite remote sensing, and ground-based regulatory and low-cost monitors all have strengths and weaknesses with respect to providing locally relevant information about air quality. This presentation will give a brief overview of these data sources and then discuss a method for combining them via data fusion to support near-real-time air quality estimation and forecasting at sub-city scales. The basic idea behind the approach will be summarized, followed by an update on recent developments towards creating an operational system using Google Earth Engine and on quantifying uncertainties related to data fusion outputs.
Carl Malings↗
Air Quality Data Fusion with Sensors, Satellites, and Models
Poor air quality is a major global public health concern, which is only projected to get worse in coming years. A comprehensive understanding of current and potential future air quality and its key drivers spanning from local to global scales is needed to tackle this important problem. This presentation will outline the sources of information that we use to understand air quality, including ground-based measurements, satellites, and models. After giving an overview of these data sources and outlining their strengths and limitations, we will take a look at how they can be used together to give us a better picture of air quality locally and globally with data fusion techniques.
Carl Malings↗
Air Quality Data Fusion with Sensors, Satellites, and Models
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Carl Malings↗
Fast Machine Learning Lidar Surrogate Simulator: Pristine Clear Sky
The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.
Shan Zeng↗
Model Evaluation by Measurements from Collocated Remote Sensors in Complex Terrain
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. Here the model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s –1 . Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s –1 . Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ~0.1 m s –1 , but some reached 0.3 m s –1 . All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
54 ENVIRONMENTAL SCIENCES↗
Diagnosability-Based Sensor Placement through Structural Model Decomposition
Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.
Daigle, Matthew↗
Stress modeling of microdiaphragm pressure sensors
A finite element program analysis was used to model the stress distribution of two monocrystalline silicon diaphragm pressure sensors. One configuration consists of an anisotropically backside etched diaphragm into a 250 micron thick, (100) oriented, silicon wafer. The diaphragm and total chip dimensions are given. The device is rigidly clamped on the back to a support substrate. Another configuration consists of a monocrystalline, (100), microdiaphragm which is formed on top of the wafer and whose area is reduced by a factor of 25 over the first configuration. The diaphragm is rigidly clamped to the silicon wafer. The stresses were calculated at a gauge pressure of 300 mm Hg and used to estimate the piezoresistive responses of resistor elements which were placed parallel and perpendicular near the diaphragm edges.
Tack, P. C.↗
Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment
Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry• Gas absorptiono Line-by-line calculationso Fast transmittance tables• Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations• Requires full Stokes matriceso And absorption vector• Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon)• Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs• Atmospheric Informationo Temperatureo Pressure / heighto Water vapor• Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics• Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean• Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ• Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference• Corresponds to the presence of aligned icecrystals• Look at trends for both simulations andobservations• Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input• Single frequency radar retrievals• Multiple scattering enhancementapparent at W band• Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015• DC-8 and ER-2 flightso Focus on APR-3 (DC-8)• Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults• Retrievals match probeso Good qualitative match• Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction• Significant amounts ofsupercooled liquid water
Adams, Ian S.↗
Evaluation of electrolytic tilt sensors for wind tunnel model angle-of-attack (AOA) measurements
The results of a laboratory evaluation of three types of electrolytic tilt sensors as potential candidates for model attitude or angle of attack (AOA) measurements in wind tunnel tests are presented. Their performance was also compared with that from typical servo accelerometers used for AOA measurements. Model RG-37 electrolytic tilt sensors were found to have the highest overall accuracy among the three types. Compared with the servo accelerometer, their accuracies are about one order of magnitude worse and each of them cost about two-thirds less. Therefore, the sensors are unsuitable for AOA measurements although they are less expensive. However, the potential for other applications exists where the errors resulting from roll interaction, vibration, and response time are less, and sensor temperature can be controlled.
Wong, Douglas T.↗
Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 1: A cloud ensemble/radiative parameterization for sensor response (report version)
The physical retrieval of geophysical parameters based upon remotely sensed data requires a sensor response model which relates the upwelling radiances that the sensor observes to the parameters to be retrieved. In the retrieval of precipitation water contents from satellite passive microwave observations, the sensor response model has two basic components. First, a description of the radiative transfer of microwaves through a precipitating atmosphere must be considered, because it is necessary to establish the physical relationship between precipitation water content and upwelling microwave brightness temperature. Also the spatial response of the satellite microwave sensor (or antenna pattern) must be included in the description of sensor response, since precipitation and the associated brightness temperature field can vary over a typical microwave sensor resolution footprint. A 'population' of convective cells, as well as stratiform clouds, are simulated using a computationally-efficient multi-cylinder cloud model. Ensembles of clouds selected at random from the population, distributed over a 25 km x 25 km model domain, serve as the basis for radiative transfer calculations of upwelling brightness temperatures at the SSM/I frequencies. Sensor spatial response is treated explicitly by convolving the upwelling brightness temperature by the domain-integrated SSM/I antenna patterns. The sensor response model is utilized in precipitation water content retrievals.
Olson, William S.↗
Performance modeling of earth resources remote sensors
A technique is presented for constructing a mathematical model of an earth resources remote sensor. The technique combines established models of electronic and optical components with formulated models of scan and vibration effects, and it includes a model of the radiation effects of the earth's atmosphere. The resulting composite model is useful for predicting in-flight sensor performance, and a descriptive set of performance parameters is derived in terms of the model. A method is outlined for validating the model for each sensor of interest. The validation for one airborne infrared scanning system is accomplished in part by a satisfactory comparison of predicted response with laboratory data for that sensor.
Kidd, R. H.↗
Model of hot-film sensor with substrate
A detailed model is constructed to investigate parameters influencing a hot-film anemometer system used to study cross-flow phenomena on swept wings. This anemometer is designed to detect vortices set up by cross-flow. The nature of the vortices is not well known, and a well-verified instrumentation model is considered essential to allow accurate interpretation of experimental results. Previous investigators have indicated that heat conduction to the film's substrate is significant. Two variations on the inclusion of substrate effects in the model are discussed. In the first, the substrate is assumed to have a constant temperature throughput: in the second more elaborate model, the temperature of the substrate is allowed to vary. This variation is related to the convective heat removal at the substrate/gas interface.
Mcree, Griffith J.↗
Hydrogen Sensors Boost Hybrids; Today's Models Losing Gas?
Advanced chemical sensors are used in aeronautic and space applications to provide safety monitoring, emission monitoring, and fire detection. In order to fully do their jobs, these sensors must be able to operate in a range of environments. NASA has developed sensor technologies addressing these needs with the intent of improving safety, optimizing combustion efficiencies, and controlling emissions. On the ground, the chemical sensors were developed by NASA engineers to detect potential hydrogen leaks during Space Shuttle launch operations. The Space Shuttle uses a combination of hydrogen and oxygen as fuel for its main engines. Liquid hydrogen is pumped to the external tank from a storage tank located several hundred feet away. Any hydrogen leak could potentially result in a hydrogen fire, which is invisible to the naked eye. It is important to detect the presence of a hydrogen fire in order to prevent a major accident. In the air, the same hydrogen-leak dangers are present. Stress and temperature changes can cause tiny cracks or holes to form in the tubes that line the Space Shuttle s main engine nozzle. Such defects could allow the hydrogen that is pumped through the nozzle during firing to escape. Responding to the challenges associated with pinpointing hydrogen leaks, NASA endeavored to improve propellant leak-detection capabilities during assembly, pre-launch operations, and flight. The objective was to reduce the operational cost of assembling and maintaining hydrogen delivery systems with automated detection systems. In particular, efforts have been focused on developing an automated hydrogen leak-detection system using multiple, networked hydrogen sensors that are operable in harsh conditions.
Source record↗
A model-based reasoning approach to sensor placement for monitorability
An approach is presented to evaluating sensor placements to maximize monitorability of the target system while minimizing the number of sensors. The approach uses a model of the monitored system to score potential sensor placements on the basis of four monitorability criteria. The scores can then be analyzed to produce a recommended sensor set. An example from our NASA application domain is used to illustrate our model-based approach to sensor placement.
Chien, Steve↗