Stability analysis methods for hybrid control systems.
Stability margins for hybrid continuous discrete data control systems, developing open loop transfer function
TRANSFER FUNCTION↗
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Results for “methods: data analysis”
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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Aircraft motion analysis using limited flight and radar data
The development and application of methods for reconstructing, from a limited set of recorded data, a comprehensive scenario of aircraft motions before and during an accident are described. The accuracy of these analytical methods is investigated using data recorded onboard the Ames CV-990 research aircraft. In these experiments, the expanded set of data, derived from either foil or ATC records, is compared with corresponding values measured by the research instrumentation system onboard the aircraft. The results indicate that many of the derived quantities are in good agreement with the corresponding onboard measurements. A recent application of this procedure using actual accident records is presented and potential applications are briefly reviewed.
Wingrove, R. C.↗
The Scale Method as an Approach for Prompt Fission Neutron Spectra Analysis and Evaluation
The Scale Method was applied for an analysis of experimental and evaluated data of Prompt Fission Neutron Spectra for different isotopes and incident neutron energies below 6 MeV. It was demonstrated that the method describes all experimental data sets in the outgoing neutron energy range 0–7 MeV within uncertainties. Theoretical arguments were provided to support the application of this method. Some important peculiarities connected with the neutron emission mechanism were identified, and proposals for future theoretical and experimental investigations were discussed. It was concluded that the new evaluations completed recently for {sup 235}U systematically overestimate the average energy of experimental prompt fission neutron spectra.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Application of a Split-Fiber Probe to Velocity Measurement in the NASA Research Compressor
A split-fiber probe was used to acquire unsteady data in a research compressor. The probe has two thin films deposited on a quartz cylinder 200 microns in diameter. A split-fiber probe allows simultaneous measurement of velocity magnitude and direction in a plane that is perpendicular to the sensing cylinder, because it has its circumference divided into two independent parts. Local heat transfer considerations indicated that the probe direction characteristic is linear in the range of flow incidence angles of +/- 35. Calibration tests confirmed this assumption. Of course, the velocity characteristic is nonlinear as is typical in thermal anemometry. The probe was used extensively in the NASA Glenn Research Center (GRC) low-speed, multistage axial compressor, and worked reliably during a test program of several months duration. The velocity and direction characteristics of the probe showed only minute changes during the entire test program. An algorithm was developed to decompose the probe signals into velocity magnitude and velocity direction. The averaged unsteady data were compared with data acquired by pneumatic probes. An overall excellent agreement between the averaged data acquired by a split-fiber probe and a pneumatic probe boosts confidence in the reliability of the unsteady content of the split-fiber probe data. To investigate the features of unsteady data, two methods were used: ensemble averaging and frequency analysis. The velocity distribution in a rotor blade passage was retrieved using the ensemble averaging method. Frequencies of excitation forces that may contribute to high cycle fatigue problems were identified by applying a fast Fourier transform to the absolute velocity data.
Lepicovsky, Jan↗
Data from: Comparing traditional and Bayesian approaches to ecological meta-analysis
Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.
54 ENVIRONMENTAL SCIENCES↗
Active controls: A look at analytical methods and associated tools
A review of analytical methods and associated tools for active controls analysis and design problems is presented. Approaches employed to develop mathematical models suitable for control system analysis and/or design are discussed. Significant efforts have been expended to develop tools to generate the models from the standpoint of control system designers' needs and develop the tools necessary to analyze and design active control systems. Representative examples of these tools are discussed. Examples where results from the methods and tools have been compared with experimental data are also presented. Finally, a perspective on future trends in analysis and design methods is presented.
Newsom, J. R.↗
Active control - A look at analytical methods and associated tools
A review of analytical methods and associated tools for active controls analysis and design problems is presented. Approaches employed to develop mathematical models suitable for control system analysis and/or design are discussed. Significant efforts have been expended to develop tools to generate the models from the standpoint of control system designers' needs and to develop the tools necessary to analyze and design active control systems. Representative examples of these tools are discussed. Examples where results from the methods and tools have been compared with experimental data are also presented. Finally, a perspective on future trends in analysis and design methods is presented.
Newsom, J. R.↗
A novel method to predict internal relative humidity in cementitious materials by 1H NMR
In sealed cementitious materials, the internal relative humidity (RH) decrease is mainly due to the water activity depression caused by menisci formation in partially-saturated pores. This process can be conveniently described with the evolution of the Kelvin radius. To obtain the Kelvin radius, a novel method based on the evolution of pore volumes quantified by {sup 1}H nuclear magnetic resonance (NMR) and mercury intrusion porosimetry (MIP) was developed. This approach was validated against experimental results for cementitious materials with a range of water to cement ratios from 0.30 to 0.46. A comparison between the Kelvin radius calculated with this approach with a previously published method using data obtained from MIP and chemical shrinkage was presented. A sensitivity analysis for the new prediction method was performed using a bootstrapping technique.
36 MATERIALS SCIENCE↗
classLog: Logistic regression for the classification of genetic sequences
Introduction Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. Methods We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. Results When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Discussion Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .
Zeller, Michael A.↗
Voyager design and flight loads comparison
Estimates of flight loads for Voyager 1 and Voyager 2 are summarized. These member loads are obtained by using the measured flight accelerations at the launch vehicle/spacecraft interface as forcing functions for the Voyager mathematical model. The flight loads are compared to the Voyager design loads obtained from the shock spectra/impedance method and to the loads obtained using space vehicle transient loads analysis. Finally, based on these data, an assessment of the shock spectra/impedance loads method used for Voyager is presented.
Chen, J. C.↗
Testing Fractal Methods on Observed and Simulated Solar Magnetograms
The term "magnetic complexity" has not been sufficiently quantified. To accomplish this, we must understand the relationship between the observed magnetic field of solar active regions and fractal dimension measurements. Using data from the Marshall Space Flight Center's vector magnetograph ranging from December 1991 to July 2001, we compare the results of several methods of calculating a fractal dimension, e.g., Hurst coefficient, the Higuchi method, power spectrum, and 2-D Wavelet Packet Analysis. In addition, we apply these methods to synthetic data, beginning with representations of very simple dipole regions, ending with regions that are magnetically complex.
Adams, M.↗
Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2
Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.
Europa↗
Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]
Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.
Rao, Ashwin P. (ORCID:0000000319312568)↗
Detecting Risk and Anomalies in Airplane Dynamics Through Entropic Analysis of Time Series Data
Despite recent efforts to move away from traditional threshold exceedance detection methods for aircraft state monitoring, modern aircraft still rely on safety thresholds to communicate to pilots the identification of an anomaly in the aircraft when a threshold is surpassed. Current anomaly detection methods mainly depend on uninterpretable machine learning models to learn complex patterns and relationships contained in the time series data of aircraft. Although these methods are capable of identifying known anomalies, their deficiency in interpretability presents a challenge when translating them to different aircraft. To overcome this deficiency, entropic analysis of aircraft dynamics seeks to characterize the complexity, or lack thereof, of the aircraft dynamics prior to the development of a risk scenario. This complexity characterization provides a more straightforward summary of state changes in the dynamics of flight variables. To build a foundation for entropic analysis, we analyzed the complexity of unstable approaches, an anomalous event present in many of today’s aviation accidents. The analysis revealed a statistically significant difference in the complexity distribution of flight variables under a stable approach versus an unstable approach. These differences in complexity were especially notable minutes before an approach was identified as unstable. Moreover, the multiscale entropic analysis revealed the presence of signal complexity at multiple time scales across multiple time windows before landing. By capturing state changes and corrections in the aircraft dynamics using entropy, advanced, yet still interpretable, sensor systems based on entropic frameworks from this study can be constructed in the future using classical machine learning approaches.
Risk detection↗
First- and Second-Order Methods for Online Convolutional Dictionary Learning
Convolutional sparse representations are a form of sparse representation with a structured, translation-invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage and severely limits the training data size that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size than batch methods. This study extends our prior work, improving a number of aspects of our previous algorithm; proposing an entirely new one, with better performance, that supports the inclusion of a spatial mask for learning from incomplete data; and providing a rigorous theoretical analysis of these methods.
97 MATHEMATICS AND COMPUTING↗
Aerodynamic predictions and experimental results for an Advanced Manned Launch System orbiter configuration
The results from a series of wind-tunnel tests are presented along with predictions of the aerodynamic characteristics for a proposed Advanced Manned Launch System orbiter configuration. The predicted characteristics were made using the Aerodynamic Preliminary Analysis System (APAS), and were shown to be of sufficient accuracy for use in preliminary configuration analysis. Various methods of analysis were used in the APAS system, and the results of each were compared with the wind-tunnel dam. The accuracy of the predicted data varied over the Mach range studied. The best agreement between the wind-tunnel data and the APAS predictions occurr4ed in the low subsonic and the high supersonic regimes, while the low supersonic predictions resulted in only fair agreement. The APAS paneled-body analysis method (low speed) and the HABP impact analysis method (high speed) gave the most accurate results when compared against the experimental data.
Engelund, Walter C.↗
Dual-Use Space Technology Transfer Conference and Exhibition
This is the second volume of papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools; systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development; perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; and robotics technologies.
Krishen, Kumar↗
An Independent Analysis of the Spitzer/IRAC Phase Curves of WASP43 b
We present here a reanalysis of the Spitzer Space Telescope phase curves of the hot Jupiter WASP43 b, using the wavelet pixel-independent component analysis, a blind signal-source separation method. The data analyzed were recorded with the Infrared Array Camera and consisted of two visits at 3.6 μm, and one visit at 4.5 μm, each visit covering one transit and two eclipse events. To test the robustness of our technique we repeated the analysis on smaller portions of the phase curves, and by employing different instrument ramp models. Our reanalysis presents significant updates of the planetary parameters compared to those reported in the original phase curve study of WASP43 b. In particular, we found (1) higher nightside temperatures, (2) smaller hotspot offsets, (3) a greater consistency (∼1σ) between the two 3.6 μm visits, and (4) a greater similarity with the predictions of the atmospheric circulation models. Our parameter results are consistent within 1σ with those reported by a recent reanalysis of the same data sets. For each visit we studied the variation of the retrieved transit parameters as a function of various sets of stellar limb-darkening coefficients, finding significant degeneracy between the limb-darkening models and the analysis output. Furthermore, we performed the analysis of the single transit and eclipse events, and we examined the differences between these results with the ones obtained with the whole phase curve. Finally we provide a formula useful to optimize the trade-off between precision and duration of observations of transiting exoplanets.
79 ASTRONOMY AND ASTROPHYSICS↗