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At least 343 records · Page 19

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NASA NTRS1991

Advances in analytical chemistry

Implementation of computer programs based on multivariate statistical algorithms makes possible obtaining reliable information from long data vectors that contain large amounts of extraneous information, for example, noise and/or analytes that we do not wish to control. Three examples are described. Each of these applications requires the use of techniques characteristic of modern analytical chemistry. The first example, using a quantitative or analytical model, describes the determination of the acid dissociation constant for 2,2'-pyridyl thiophene using archived data. The second example describes an investigation to determine the active biocidal species of iodine in aqueous solutions. The third example is taken from a research program directed toward advanced fiber-optic chemical sensors. The second and third examples require heuristic or empirical models.

Arendale, W. F.↗

NASA NTRS2017

Stochastic Modelling of Wireless Energy Transfer

This study investigates the efficiency of a new method of powering remote sensors by the means of wireless energy transfer. The increased use of sensors for data collection comes with the inherent cost of supplying power from sources such as power cables or batteries. Wireless energy transfer technology eliminates the need for power cables or periodic battery replacement. The time and cost of setting up or expanding a sensor network will be reduced while allowing sensors to be placed in areas where running power cables or battery replacement is not feasible. This paper models wireless channels for power and data separately. Smart scheduling for the data channel is proposed to avoid transmitting data on a noisy channel where the probability of data loss is high to improve power efficiency. Analytical models have been developed and verified using simulations.

Veilleux, Shaun↗

DOE OSTI2023

Predicting chatter using machine learning and acoustic signals from low-cost microphones

Machining chatter is a phenomenon resulting from self-oscillation between a machining tool and workpiece. This self-oscillation results in variation on the machined product that reduces the ability to meet desired specifications. Chatter is a widely studied topic as it directly relates to the quality of machined products. Here, this study details the application of a Random Forest (RF) classifier with Recursive Feature Elimination (RFE) to machining audio collected by a single microphone during down-milling operations. This approach allows straightforward feature elimination that results in an easily understood set of analyzed dimensions. Stability is predicted solely based on the classification output of the RF classifier. Our approach proves highly predictive with consistent machining setup and a small sample set. We also review transferability between machining setups and present key findings. Our RF approach demonstrates the ability to analyze and classify chatter through a low-cost approach with limited training data required. The motivation for using a single microphone is to enable detection on machines without other sensors, such as accelerometers, present in the machining setup. The value of the in-process sensor and chatter classifier is highlighted because the machining setup included asymmetric dynamics that reduced the accuracy of the traditional analytical stability solution. We see a natural progression to deploying this audio-only methodology with real-time processing and classification using either a laptop or smartphone. This progression will allow visual indicators during the machining process that can alert machinists of progression into unstable machining processes.

42 ENGINEERING↗

NASA NTRS1991

Integration of SAR and DEM data: Geometrical considerations

General principles for integrating data from different sources are derived from the experience of registration of SAR images with digital elevation models (DEM) data. The integration consists of establishing geometrical relations between the data sets that allow us to accumulate information from both data sets for any given object point (e.g., elevation, slope, backscatter of ground cover, etc.). Since the geometries of the two data are completely different they cannot be compared on a pixel by pixel basis. The presented approach detects instances of higher level features in both data sets independently and performs the matching at the high level. Besides the efficiency of this general strategy it further allows the integration of additional knowledge sources: world knowledge and sensor characteristics are also useful sources of information. The SAR features layover and shadow can be detected easily in SAR images. An analytical method to find such regions also in a DEM needs in addition the parameters of the flight path of the SAR sensor and the range projection model. The generation of the SAR layover and shadow maps is summarized and new extensions to this method are proposed.

Kropatsch, Walter G.↗

NASA NTRS2004

Hubble Space Telescope Fine Guidance Sensor Post-Flight Bearing Inspection

Aerospace mechanism engineering success stories often, if not always, consist of overcoming developmental, test and flight anomalies. Many times it is these anomalies that stimulate technology growth and more reliable future systems. However, one must learn from these to achieve an ultimately successful mission. It is not often that a spacecraft engineer is able to inspect hardware that has flown in orbit for several years. However, in February 1997, the Fine Guidance Sensor-I (FGS-1) was removed from the Hubble Space Telescope (HST) and returned to NASA Goddard Space Flight Center (GSFC) during the second Servicing Mission (SM2). At the time of removal, FGS-1 had nearly 7 years of service and the bearings in the Star Selector Servos (SSS) had accumulated approximately 25 million Coarse Track (CT) cycles. The main reason for its replacement was due to a bearing torque anomaly leading to stalling of the B Star Selector Servo (SSS-B) when reversing direction during a vehicle offset maneuver, referred to herein as a Reversal Bump (RB). The returned HST FGS SSS bearings were disassembled for post-service condition assessment to better understand the actual cause of the torque spikes, identify potential process/design improvements, and provide information for remedial on-orbit operation modifications. The methods and technology utilized for this inspection are not unique to this system and can be adapted to most investigation ai varying stages of the mechanism life from development, through testing, io post night evaluation. The systematic methods used for the HST Fine Guidance Sensor (FGS) SSS and specific findings are the subjects presented in this paper. The lessons learned include the importance of cleanliness and handling for precision instrument bearings and the potential effects from contamination. The paper describes in detail, the analytical techniques used for the SSS and their importance in this investigation. Inspection analytical data and photographs are included throughout the paper.

Pellicciotti, Joseph↗

NASA NTRS2004

Hubble Space Telescope Fine Guidance Sensor Post-Flight Bearing Inspection

Aerospace mechanism engineering success stories often, if not always, consist of overcoming developmental, test and flight anomalies. Many times it is these anomalies that stimulate technology growth and more reliable future systems. However, one must learn from these to achieve an ultimately successful mission. It is not often that a spacecraft engineer is able to inspect hardware that has flown in orbit for several years. However, in February 1997, the Fine Guidance Sensor-1 (FGS-1) was removed from the Hubble Space Telescope (HST) and returned to NASA Goddard Space Flight Center (GSFC) during the second Servicing Mission (SM2). At the time of removal, FGS-1 had nearly 7 years of service and the bearings in the Star Selector Servos (SSS) had accumulated approximately 25 million Coarse Track (CT) cycles. The main reason for its replacement was due to a bearing torque anomaly leading to stalling of the B Star Selector Servo (SSS-B) when reversing direction during a vehicle offset maneuver, referred to herein as a Reversal Bump (RB). The returned HST FGS SSS bearings were disassembled for post-service condition assessment to better understand the actual cause of the torque spikes, identify potential process/design improvements, and provide information for remedial on-orbit operation modifications. The methods and technology utilized for this inspection are not unique to this system and can be adapted to most investigations at varying stages of the mechanism life from development, through testing, to post flight evaluation. The systematic methods used for the HST Fine Guidance Sensor (FGS) SSS and specific findings are the subjects presented in this paper. The lessons learned include the importance of cleanliness and handling for precision instrument bearings and the potential effects from contamination. The paper describes in detail, the analytical techniques used for the SSS and their importance in this investigation. Inspection analytical data and photographs are included throughout the paper.

Pellicciotti, J.↗

DOE OSTI2013

Identifying, Visualizing, and Fusing Social Media Data to Support Nonproliferation and Arms Control Treaty Verification: Preliminary Results

While international nonproliferation and arms control verification capabilities have their foundations in physical and chemical sensors, state declarations, and on-site inspections, verification experts are beginning to consider the importance of open source data to complement and support traditional means of verification. One of those new, and increasingly expanding, sources of open source information is social media, which can be ingested and understood through social media analytics (SMA). Pacific Northwest National Laboratory (PNNL) is conducting research to further our ability to identify, visualize, and fuse social media data to support nonproliferation and arms control treaty verification efforts. This paper will describe our preliminary research to examine social media signatures of nonproliferation or arms control proxy events. We will describe the development of our preliminary nonproliferation and arms control proxy events, outline our initial findings, and propose ideas for future work.

social media, arms control, nonproliferation↗

DOE OSTI2015

Rapid response sensor for analyzing Special Nuclear Material

Rapid in-situ analytical techniques are attractive for characterizing Special Nuclear Material (SNM). Present techniques are time consuming, and require sample dissolution. Proof-of-principal studies are performed to demonstrate the utility of employing low energy neutrons from a portable pulsed neutron generator for non-destructive isotopic analysis of nuclear material. In particular, time-sequenced data acquisition, operating synchronously with the pulsing of a neutron generator, partitions the characteristic elemental prompt gamma-rays according to the type of the reaction; inelastic neutron scattering reactions during the ON state and thermal neutron capture reactions during the OFF state of the generator. Thus, the key challenge is isolating these signature gamma- rays from the prompt fission and β-delayed gamma-rays that are also produced during the neutron interrogation. A commercial digital multi-channel analyzer has been specially customized to enable time-resolved gamma-ray spectral data to be acquired in multiple user-defined time bins within each of the ON/OFF gate periods of the neutron generator. Preliminary results on new signatures from depleted uranium as well as modeling and benchmarking of the concept are presented, however this approach should should be applicable for virtually all forms of SNM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Characterization of Cameras for the COSMO K-coronagraph

Digital image sensors are ubiquitous in astronomical instrumentation and it is well known that they suffer from issues that must be corrected for data to be scientifically useful. I present discussion on errors resulting from digitization and characterization of nonlinearity and ADC errors of the PhotonFocus MV-D1024E cameras selected for the K-coronagraph of the Coronal Solar Magnetism Observatory. I derive an analytic expression for quantization errors. The MV-D1024E camera has adequate bit depth for which quantization error is not an issue. I show that this is not the case for all cameras, particularly those with deep wells and low read noise. The impact of nonlinearity and ADC errors on science observations of the K-coronagraph is analyzed using a simplified telescope model. Errors caused by the camera ADCs result in systematic errors in the measurement of the polarimetric signal of several times 10{sup −9} B {sub ⊙}, which is about an order of magnitude above the desired sensitivity. I demonstrate a method for post-facto data correction using a lookup table and derive parameters from camera characterization measurements that were made with a lab setup. Nonlinearity is traditionally addressed with a global correction. I show through analysis of calibration data that for the MV-D1024E this correction leaves residual systematic errors after dark and gain correction of up to 1% of the signal. I demonstrate that a pixel-wise correction of nonlinearity reduces the errors to below 0.1%. These corrections are necessary for the K-coronagraph data products to meet the science requirements. They have been implemented in the instrument data acquisition system and data reduction pipeline. While no other instruments besides the K-coronagraph or cameras besides the MV-D1024E are discussed here, the results are illustrative for all instruments and cameras.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1996

An Optimal Deconvolution Method for Reconstructing Pneumatically Distorted Near-Field Sonic Boom Pressure Measurements

In-flight measurements of the SR-71 near-field sonic boom were obtained by an F-16XL airplane at flightpath separation distances from 40 to 740 ft. Twenty-two signatures were obtained from Mach 1.60 to Mach 1.84 and altitudes from 47,600 to 49,150 ft. The shock wave signatures were measured by the total and static sensors on the F-16XL noseboo. These near-field signature measurements were distorted by pneumatic attenuation in the pitot-static sensors and accounting for their effects using optimal deconvolution. Measurement system magnitude and phase characteristics were determined from ground-based step-response tests and extrapolated to flight conditions using analytical models. Deconvolution was implemented using Fourier transform methods. Comparisons of the shock wave signatures reconstructed from the total and static pressure data are presented. The good agreement achieved gives confidence of the quality of the reconstruction analysis. although originally developed to reconstruct the sonic boom signatures from SR-71 sonic boom flight tests, the methods presented here generally apply to other types of highly attenuated or distorted pneumatic measurements.

Whitmore, Stephen A.↗

NASA NTRS1973

Development of a rotating gravity gradiometer for earth orbit applications (AAFE)

Some preliminary mission studies are described along with the design, fabrication, and test of a breadboard model of an earth orbital, rotating gravity gradiometer with a design goal of 10 to the minus 11th power/sec sq (0.01 EU) in a 35-sec integration time. The proposed mission uses a Scout vehicle to launch one (or two orthogonally oriented) spin-stabilized satellites into a 330-km circular polar orbit some 20 days before an equinox. During the short orbital lifetime, the experiment would obtain two complete maps of the gravity gradient field with a resolution approaching 270 km (degree 75). The breadboard model of the gradiometer demonstrated a combined thermal and electronic noise threshold of 0.015 EU per data channel. The design changes needed to reduce the noise to less than 0.01 EU were identified. Variations of the sensor output signal with temperature were experimentally determined and a suitable method of temperature compensation was developed and tested. Other possible error sources, such as sensor interaction with satellite dynamics and magnetic fields, were studied analytically and shown to be small.

Forward, R. L.↗

NASA NTRS2018

International Space Station Future Correlation Analysis Improvements

Ongoing modal analyses and model correlation are performed on different configurations of the International Space Station (ISS). These analyses utilize on-orbit dynamic measurements collected using four main ISS instrumentation systems: External Wireless Instrumentation System (EWIS), Internal Wireless Instrumentation System (IWIS), Space Acceleration Measurement System (SAMS), and Structural Dynamic Measurement System (SDMS). Remote Sensor Units (RSUs) are network relay stations that acquire flight data from sensors. Measured data is stored in the Remote Sensor Unit (RSU) until it receives a command to download data via RF to the Network Control Unit (NCU). Since each RSU has its own clock, it is necessary to synchronize measurements before analysis. Imprecise synchronization impacts analysis results. A study was performed to evaluate three different synchronization techniques: (i) measurements visually aligned to analytical time-response data using model comparison, (ii) Frequency Domain Decomposition (FDD), and (iii) lag from cross-correlation to align measurements. This paper presents the results of this study.

time synchronization↗

NASA NTRS1994

Real-time sensor data validation

This report describes the status of an on-going effort to develop software capable of detecting sensor failures on rocket engines in real time. This software could be used in a rocket engine controller to prevent the erroneous shutdown of an engine due to sensor failures which would otherwise be interpreted as engine failures by the control software. The approach taken combines analytical redundancy with Bayesian belief networks to provide a solution which has well defined real-time characteristics and well-defined error rates. Analytical redundancy is a technique in which a sensor's value is predicted by using values from other sensors and known or empirically derived mathematical relations. A set of sensors and a set of relations among them form a network of cross-checks which can be used to periodically validate all of the sensors in the network. Bayesian belief networks provide a method of determining if each of the sensors in the network is valid, given the results of the cross-checks. This approach has been successfully demonstrated on the Technology Test Bed Engine at the NASA Marshall Space Flight Center. Current efforts are focused on extending the system to provide a validation capability for 100 sensors on the Space Shuttle Main Engine.

Bickmore, Timothy W.↗

NASA NTRS1977

Analytic investigation of the AEM-A/HCMM attitude control system performance

The Heat Capacity Mapping Mission (HCMM), scheduled for launch in 1978, will be three-axis stabilized relative to the earth in a 600-kilometer altitude, polar orbit. The autonomous attitude control system consists of three torquing coils and a momentum wheel driven in response to error signals computed from data received from an infrared horizon sensor and a magnetometer. This paper presents a simple model of the attitude dynamics and derives the equations that determine the stability of the system during both attitude acquisition (acquisition-mode) and mission operations (mission-mode). Modifications to the proposed mission-mode control laws which speed the system's response to transient attitude errors and reduce the steady-state attitude errors are suggested. Numerical simulations are performed to validate the results obtained with the simple model.

Lerner, G. M.↗

DOE OSTI2017

(U) Physics Validation of the RMI-Based Ejecta Source Model Implementation in FLAG: L2 Milestone #6035 Report

The Los Alamos Physics and Engineering Models (PEM) program has developed a model for Richtmyer-Meshkov instability (RMI) based ejecta production from shock-melted surfaces, along with a prescription for a self-similar velocity distribution (SSVD) of the resulting ejecta particles. We have undertaken an effort to validate this source model using data from explosively driven tin coupon experiments. The model’s current formulation lacks a crucial piece of physics: a method for determining the duration of the ejecta production interval. Without a mechanism for terminating ejecta production, the model is not predictive. Furthermore, when the production interval is hand-tuned to match time-integrated mass data, the predicted time-dependent mass accumulation on a downstream sensor rises too sharply at early times and too slowly at late times because the SSVD overestimates the amount of mass stored in the fastest particles and underestimates the mass stored in the slowest particles. The functional form of the resulting m(t) is inconsistent with the available time-dependent data; numerical simulations and analytic studies agree on this point. Simulated mass tallies are highly sensitive to radial expansion of the ejecta cloud. It is not clear if the same effect is present in the experimental data but if so, depending on the degree, this may challenge the model’s compatibility with tin coupon data. The current implementation of the model in FLAG is sensitive to the detailed interaction between kinematics (hydrodynamic methods) and thermodynamics (material models); this sensitivity prohibits certain physics modeling choices. The appendices contain an extensive analytic study of piezoelectric ejecta mass measurements, along with test problems, excerpted from a longer work (LA-UR-17-21218).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

NASA NTRS1992

The Quickest, Lowest-cost Lunar Resource Assessment Program: Integrated High-tech Earth-based Astronomy

Science and technology applications for the Moon have not fully kept pace with technical advancements in sensor development and analytical information extraction capabilities. Appropriate unanswered questions for the Moon abound, but until recently there has been little motivation to link sophisticated technical capabilities with specific measurement and analysis projects. Over the last decade enormous technical progress has been made in the development of (1) CCD photometric array detectors; (2) visible to near-infrared imaging spectrometers; (3)infrared spectroscopy; (4) high-resolution dual-polarization radar imaging at 3.5, 12, and 70 cm; and equally important (5) data analysis and information extraction techniques using compact powerful computers. Parts of each of these have been tested separately, but there has been no programmatic effort to develop and optimize instruments to meet lunar science and resource assessment needs (e.g., specific wavelength range, resolution, etc.) nor to coordinate activities so that the symbiotic relation between different kinds of data can be fully realized. No single type of remotely acquired data completely characterizes the lunar environment, but there has been little opportunity for integration of diverse advanced sensor data for the Moon. Two examples of technology concepts for lunar measurements are given. Using VIS/near-IR spectroscopy, the mineral composition of surface material can be derived from visible and near-infrared radiation reflected from the surface. The surface and subsurface scattering properties of the Moon can be analyzed using radar backscattering imaging.

Pieters, Carle M.↗

NASA NTRS2013

Performance Evaluation of Large Aperture 'Polished Panel' Optical Receivers Based on Experimental Data

Recent interest in hybrid RF/Optical communications has led to the development and installation of a "polished-panel" optical receiver evaluation assembly on the 34-meter research antenna at Deep-Space Station 13 (DSS-13) at NASA's Goldstone Communications Complex. The test setup consists of a custom aluminum panel polished to optical smoothness, and a large-sensor CCD camera designed to image the point-spread function (PSF) generated by the polished aluminum panel. Extensive data has been obtained via realtime tracking and imaging of planets and stars at DSS-13. Both "on-source" and "off-source" data were recorded at various elevations, enabling the development of realistic simulations and analytic models to help determine the performance of future deep-space communications systems operating with on-off keying (OOK) or pulse-position-modulated (PPM) signaling formats with photon-counting detection, and compared with the ultimate quantum bound on detection performance for these modulations. Experimentally determined PSFs were scaled to provide realistic signal-distributions across a photon-counting detector array when a pulse is received, and uncoded as well as block-coded performance analyzed and evaluated for a well-known class of block codes.

on-off keying (OOK)↗