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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Signal analysis of NEMS sensors at the output of a chromatography column

This article introduces a joined Bayesian estimation of gas samples issued from a gas chromatography column (GC) coupled with a NEMS sensor based on Giddings Eyring microscopic molecular stochastic model. The posterior distribution is sampled using a Monte Carlo Markov Chain and Gibbs sampling. Parameters are estimated using the posterior mean. This estimation scheme is finally applied on simulated and real datasets using this molecular stochastic forward model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Seismic Fracture Characterization Methodologies for Enhanced Geothermal Systems

Executive Summary The overall objective of this work was the development of surface and borehole seismic methodologies using both compressional and shear waves for characterizing faults and fractures in Enhanced Geothermal Systems. We used both surface seismic and vertical seismic profile (VSP) methods. We adapted these methods to the unique conditions encountered in Enhanced Geothermal Systems (EGS) creation. These conditions include geological environments with volcanic cover, highly altered rocks, severe structure, extreme near surface velocity contrasts and lack of distinct velocity contrasts at depth. One of the objectives was the development of methods for identifying more appropriate seismic acquisition parameters for overcoming problems associated with these geological factors. Because temperatures up to 300º C are often encountered in these systems, another objective was the testing of VSP borehole tools capable of operating at depths in excess of 1,000 m and at temperatures in excess of 200º C. A final objective was the development of new processing and interpretation techniques based on scattering and time-frequency analysis, as well as the application of modern seismic migration imaging algorithms to seismic data acquired over geothermal areas. The use of surface seismic reflection data at Brady's Hot Springs was found useful in building a geological model, but only when combined with other extensive geological and geophysical data. The use of fine source and geophone spacing was critical in producing useful images. The surface seismic reflection data gave no information about the internal structure (extent, thickness and filling) of faults and fractures, and modeling suggests that they are unlikely to do so. Time-frequency analysis was applied to these data, but was not found to be significantly useful in their interpretation. Modeling does indicate that VSP and other seismic methods with sensors located at depth in wells will be the most effective seismic tools for getting information on the internal structure of faults and fractures in support of fluid flow pathway management and EGS treatment. Scattered events similar to those expected from faults and fractures are seen in the VSP reported here. Unfortunately, the source offset and well depth coverage do not allow for detailed analysis of these events. This limited coverage also precluded the use of advanced migration and imaging algorithms. More extensive acquisition is needed to support fault and fracture characterization in the geothermal reservoir at Brady's Hot Springs. The VSP was effective in generating interval velocity estimates over the depths covered by the array. Upgoing reflection events are also visible in the VSP results at locations corresponding to reflection events in the surface seismic. Overall, the high temperature rated fiber optic sensors used in the VSP produced useful results. Modeling has been found useful in the interpretation of both surface reflection seismic and VSP data. It has helped identify possible near surface scattering in the surface seismic data. It has highlighted potential scattering events from deeper faults in the VSP data. Inclusion of more detailed fault and fracture specific stiffness parameters are needed to fully interpret fault and fracture scattered events for flow properties (Pyrak-Nolte and Morris, 2000, Zhu and Snieder, 2002). Shear wave methods were applied in both the surface seismic reflection and VSP work. They were not found to be effective in the Brady's Hot Springs area. This was due to the extreme attenuation of shear waves in the near surface at Brady's. This does not imply that they will be ineffective in general. In geothermal areas where good shear waves can be recorded, modeling suggests they should be very useful for characterizing faults and fractures.

15 GEOTHERMAL ENERGY↗
NASA NTRS1979

Multispectral Resource Sampler (MRS): Proof of concept study on atmospheric corrections. Determinations of atmospheric optical parameters using the multispectral resource sampler atmospheric optical

An investigation was performed to determine which mathematical algorithms should be used in the calculation of atmospheric optical parameters using the Multispectral Resource Sampler (MRS) sensor. A simulation of the MRS sensor was performed using a radiative-transfer model. The simulation provides the spectral radiance at the satellite sensor in terms of various atmospheric parameters, such as optical thickness, solar zenith angle, nadir view angle, relative azimuth angle, bi-directional reflectance of the target, background albedo, and wavelength. Atmospheric correction algorithms were also developed for the determination of the total spectral optical thickness of the atmosphere for: (1) homogeneous (horizontal) hazy atmospheres with diffuse targets; (2) inhomogeneous (horizontal) hazy atmospheres with diffuse targets; and (3) homogeneous (horizontal) hazy atmospheres with non-diffuse targets.

Turner, R. E.↗
NASA NTRS1986

GRO attitude control and determination

Design features of the attitude control and determination (ACAD) system for the Gamma Ray Observatory (GRO) that will eventually be launched on the Shuttle are described. A tabulation of the ACAD system components is provided and the various standby and normal pointing operational modes of the system are summarized. The system software and sensors will maintain a quaternion model of the GRO attitude on the bases of kinematic equations and inertial data. The software is standardized and has previously been used on the Solar Maximum Mission and Landsat-D. Details of the processing components, redundant electronics for sensor processing, data handling and actuator control are outlined and illustrated with block diagrams. Tests applied to validate the ACAD design are outlined, as are ground support which will be implemented once the GRO is launched.

Jerkovsky, W.↗
DOE OSTI2019

A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks: Preprint

This paper presents a reliable, scalable, and transferable framework to predict occupancy in a building utilizing diverse, multi-modal information. We propose a new methodology for learning-driven occupancy detection built on the concepts of probabilistic graphical modeling and observable Markov chain modeling. To capture the relationship between multi-sensor data and occupancy, we propose this Occ-STPN framework that is flexible to support both multivariate and univariate formulations. While the multivariate Occ-STPN performs feature-level fusion of multiple predictors and occupancy time-series data, the univariate Occ-STPN involves decision fusion of occupancy predictions using individual predictors based on a mutual information weighted fusion scheme. We also propose a new metric to evaluate the performance of occupancy prediction algorithms. Two popular datasets are used to validate our approach and demonstrate that our framework is scalable in terms of the number of information sources (e.g., sensors) as well as it is possible to transfer trained models from one building to another without significant reduction in performance. Reliability of the algorithm is also tested by injecting noise into the datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks

This paper presents a reliable, scalable, and transferable framework to predict occupancy in a building utilizing diverse, multi-modal information. We propose a new methodology for learning-driven occupancy detection built on the concepts of probabilistic graphical modeling and observable Markov chain modeling. To capture the relationship between multi-sensor data and occupancy, we propose this Occ-STPN framework that is flexible to support both multivariate and univariate formulations. While the multivariate Occ-STPN performs feature-level fusion of multiple predictors and occupancy time-series data, the univariate Occ-STPN involves decision fusion of occupancy predictions using individual predictors based on a mutual information weighted fusion scheme. We also propose a new metric to evaluate the performance of occupancy prediction algorithms. Two popular datasets are used to validate our approach and demonstrate that our framework is scalable in terms of the number of information sources (e.g., sensors) as well as it is possible to transfer trained models from one building to another without significant reduction in performance. Reliability of the algorithm is also tested by injecting noise into the datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

System and method for assessing spectroscopic sensor accuracy

A method for assessing spectroscopic sensor accuracy, includes building an a priori simulation of generalized etalon drift. A spectroscopic sensor is tested to determine use parameters. A specific drift model is generated by applying the determined use parameters to the built a priori simulation of generalized etalon drift. The specific drift model is analyzed to determine whether the spectroscopic sensor is satisfactory.

Green, William J.↗
DOE OSTI2019

An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical setting is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. Furthermore, the numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.

17 WIND ENERGY↗
DOE OSTI2019

Nonlinear size dependent analysis and effectiveness of nanocrystalline micro/nanogyroscopes

Vibratory micro/nanogyroscopes made of nanocrystalline silicon have a complex microstructure consisting of grains, voids, and interface material phases. Given that the length scale of such inertial sensors is close to the size of the grains themselves, classical continuum mechanics theories are no longer adequate to accurately model the dynamic behaviors of these sensors. In this work, the couple stress and surface elasticity are incorporated to the governing equations of an electrically-actuated, amplitude-based, capacitive sensing cantilever gyroscope to obtain a comprehensive model of the nano-system. Then, the impact of the size dependent effects on the nonlinear dynamic behavior of the gyroscope is investigated by studying its performance for gyroscopes ranging in size from the micro-to the nano-scale. The present numerical study shows that the additional stiffness contributions from the couple stress and surface elasticity and changes in scale cause significant changes in the frequency response, nonlinear behaviors, and sensitivity of the gyroscope to the rotation rates. Furthermore, this numerical analysis demonstrates the importance of accounting for size dependent effects for micro/nanogyroscopes in order to avoid any deterioration of the effectiveness of the gyroscope operability.

42 ENGINEERING↗
DOE OSTI2024

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

42 ENGINEERING↗
DOE OSTI2026

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications

There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Causal simulation and sensor planning in predictive monitoring

Two issues are addressed which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. The approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, useful in assessing the relative importance of events so that sensor resources can be allocated effectively. The potential applicability of this work to the execution monitoring of robot task plans is briefly discussed.

Doyle, Richard J.↗
NASA NTRS2000

High-Speed Research Surveillance Symbology Assessment Experiment

Ten pilots flew multiple approach and departure scenarios in a simulation experiment of the High-Speed Civil Transport to evaluate the utility of different airborne surveillance display concepts. The primary eXternal Visibility System (XVS) display and the Navigation Display (ND) were used to present tactical and strategic surveillance information, respectively, to the pilot. Three sensors, the Traffic Alert and Collision Avoidance System, radar, and the Automatic Dependent Surveillance-Broadcast system, were modeled for this simulation and the sensors surveillance information was presented in two different symbology sets to the pilot. One surveillance symbology set used unique symbol shapes to differentiate among the sensors, while the other set used common symbol shapes for the sensors. Surveillance information in the form of escape guidance from threatening traffic was also presented to the pilots. The surveillance information (sensors and escape guidance) was either presented head-up on the primary XVS display and head-down on the ND or head-down on the ND only. Both objective and subjective results demonstrated that the display concepts having surveillance information presented head-up and head-down have surveillance performance benefits over those concepts having surveillance information displayed head-down only. No significant symbology set differences were found for surveillance task performance.

Kramer, Lynda J.↗
NASA NTRS2002

The New NASA Orbital Debris Engineering Model ORDEM2000

The NASA Orbital Debris Program Office at Johnson Space Center has developed a new computer-based orbital debris engineering model, ORDEM2000, which describes the orbital debris environment in the low Earth orbit region between 200 and 2000 km altitude. The model is appropriate for those engineering solutions requiring knowledge and estimates of the orbital debris environment (debris spatial density, flux, etc.). ORDEM2000 can also be used as a benchmark for ground-based debris measurements and observations. We incorporated a large set of observational data, covering the object size range from 10 mm to 10 m, into the ORDEM2000 debris database, utilizing a maximum likelihood estimator to convert observations into debris population probability distribution functions. These functions then form the basis of debris populations. We developed a finite element model to process the debris populations to form the debris environment. A more capable input and output structure and a user-friendly graphical user interface are also implemented in the model. ORDEM2000 has been subjected to a significant verification and validation effort. This document describes ORDEM2000, which supersedes the previous model, ORDEM96. The availability of new sensor and in situ data, as well as new analytical techniques, has enabled the construction of this new model. Section 1 describes the general requirements and scope of an engineering model. Data analyses and the theoretical formulation of the model are described in Sections 2 and 3. Section 4 describes the verification and validation effort and the sensitivity and uncertainty analyses. Finally, Section 5 describes the graphical user interface, software installation, and test cases for the user.

Liou, Jer-Chyi↗
DOE OSTI2021

A mathematical framework for ejecta cloud dynamics with application to source models and piezoelectric mass measurements

We present a mathematical framework for describing the dynamical evolution of an ejecta cloud generated by a generic ejecta source model. We consider a piezoelectric sensor fielded in the path of an ejecta cloud, for experimental configurations in which the ejecta are created at a singly shocked planar surface and fly ballistically through vacuum to the stationary sensor. To do so, we introduce the concept of a time- and velocity-dependent ejecta “areal mass function.” We derive expressions for the analytic (“true”) accumulated ejecta areal mass at the sensor and the measured (“inferred”) value obtained via the standard method for analyzing piezoelectric voltages. In this way, we derive an exact expression and upper bound for the error imposed upon a piezoelectric ejecta mass measurement (in a perfect system) by the assumption of instantaneous creation, which is commonly required for momentum diagnostic analyses. This error term is zero for truly instantaneous source models; otherwise, the standard piezoelectric analysis is guaranteed to overestimate the true mass. When combined with a piezoelectric dataset, this framework provides a unique solution for the ejecta particle velocity distribution, subject to the assumptions inherent in the data analysis. The framework also leads to strong boundary conditions that any ejecta source model must satisfy in order to be consistent with apparently global properties of piezoelectric measurements from a wide range of experiments. We demonstrate this methodology by applying it to the Richtmyer–Meshkov instability+self-similar velocity distribution ejecta source model currently under development at Los Alamos National Laboratory.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Efficient placement of structural dynamics sensors on the space station

System identification of the space station dynamic model will require flight data from a finite number of judiciously placed sensors on it. The placement of structural dynamics sensors on the space station is a particularly challenging problem because the station will not be deployed in a single mission. Given that the build-up sequence and the final configuration for the space station are currently undetermined, a procedure for sensor placement was developed using the assembly flights 1 to 7 of the rephased dual keel space station as an example. The procedure presented approaches the problem of placing the sensors from an engineering, as opposed to a mathematical, point of view. In addition to locating a finite number of sensors, the procedure addresses the issues of unobserved structural modes, dominant structural modes, and the trade-offs involved in sensor placement for space station. This procedure for sensor placement will be applied to revised, and potentially more detailed, finite element models of the space station configuration and assembly sequence.

Lepanto, Janet A.↗
NASA NTRS2005

Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra

Various instruments are used to create images of the Earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high resolution spectral measurements may be too costly to perform on a large sample and therefore lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. In past work [1], we addressed this problem using Virtual Sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. We demonstrated this method by using models trained on the high spectral resolution Terra MODIS instrument to estimate what the equivalent of the MODIS 1.6 micron channel would be for the NOAA AVHRR2 instrument. The scientific motivation for the simulation of the 1.6 micron channel is to improve the ability of the AVHRR2 sensor to detect clouds over snow and ice. This work contains preliminary experiments demonstrating that the use of spatial information can improve our ability to estimate these spectra.

Oza, Nikunj C.↗