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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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NASA NTRS2010

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

Ulbrich, Norbert Manfred↗
NASA NTRS2010

Dst Index in the 2008 GEM Modeling Challenge - Model Performance for Moderate and Strong Magnetic Storms

The GEM 2008 modeling challenge efforts are expanding beyond comparing in-situ measurements in the magnetosphere and ionosphere to include the computation of indices to be compared. The Dst index measures the largest deviations of the horizontal magnetic field at 4 equatorial magnetometers from the quiet-time background field and is commonly used to track the strength of the magnetic disturbance of the magnetosphere during storms. Models can calculate a proxy Dst index in various ways, including using the Dessler-Parker Sckopke relation and the energy of the ring current and Biot-Savart integration of electric currents in the magnetosphere. The GEM modeling challenge investigates 4 space weather events and we compare models available at CCMC against each other and the observed values of Ost. Models used include SWMF/BATSRUS, OpenGGCM, LFM, GUMICS (3D magnetosphere MHD models), Fok-RC, CRCM, RAM-SCB (kinetic drift models of the ring current), WINDMI (magnetosphere-ionosphere electric circuit model), and predictions based on an impulse response function (IRF) model and analytic coupling functions with inputs of solar wind data. In addition to the analysis of model-observation comparisons we look at the way Dst is computed in global magnetosphere models. The default value of Dst computed by the SWMF model is for Bz the Earth's center. In addition to this, we present results obtained at different locations on the Earth's surface. We choose equatorial locations at local noon, dusk (18:00 hours), midnight and dawn (6:00 hours). The different virtual observatory locations reveal the variation around the earth-centered Dst value resulting from the distribution of electric currents in the magnetosphere during different phases of a storm.

Rastaetter, Lutz↗
NASA NTRS2015

Results from the Wilkinson Microwave Anisotropy Probe

The Wilkinson Microwave Anisotropy Probe (WMAP) mapped the distribution of temperature and polarization over the entire sky in five microwave frequency bands. These full-sky maps were used to obtain measurements of temperature and polarization anisotropy of the cosmic microwave background with the unprecedented accuracy and precision. The analysis of two-point correlation functions of temperature and polarization data gives determinations of the fundamental cosmological parameters such as the age and composition of the universe, as well as the key parameters describing the physics of inflation, which is further constrained by three-point correlation functions. WMAP observations alone reduced the flat Λ cold dark matter (Lambda Cold Dark Matter) cosmological model (six) parameter volume by a factor of > 68, 000 compared with pre-WMAP measurements. The WMAP observations (sometimes in combination with other astrophysical probes) convincingly show the existence of non-baryonic dark matter, the cosmic neutrino background, flatness of spatial geometry of the universe, a deviation from a scale-invariant spectrum of initial scalar fluctuations, and that the current universe is undergoing an accelerated expansion. The WMAP observations provide the strongest ever support for inflation; namely, the structures we see in the universe originate from quantum fluctuations generated during inflation.

The Wilkinson Microwave Anisotropy Probe (WMAP)↗
NASA NTRS2019

Simulating response versus scan angle characterization on OCI for the upcoming PACE mission

The Plankton, Aerosol, Cloud ocean Ecosystem (PACE) mission Ocean Color Instrument (OCI) is the next generation ocean color sensor following on from the SeaWiFS and VIIRS designs, to be launched into low Earth orbit in 2022. Like those heritage sensors, OCI has cross track scanning fore-optics, the reflectivity of which (referred to here as the response versus scan angle or RVS) must be characterized prior to launch. The expected test setup, component reflectivity measurements, instrument model, and heritage sensor testing were used to generate simulated RVS data. This simulated data set was then used to verify the functionality of the analysis software in preparation for the upcoming ground test campaign. The analysis software is designed to characterize the RVS for all OCI bands over the full range of scan angles to be measured on orbit as well as verify compliance with the sensor design requirements in near real-time.

PACE↗
NASA NTRS2013

Further Development of Rotating Rake Mode Measurement Data Analysis

The Rotating Rake mode measurement system was designed to measure acoustic duct modes generated by a fan stage. After analysis of the measured data, the mode amplitudes and phases were quantified. For low-speed fans within axisymmetric ducts, mode power levels computed from rotating rake measured data would agree with the far-field power levels on a tone by tone basis. However, this agreement required that the sound from the noise sources within the duct propagated outward from the duct exit without reflection at the exit and previous studies suggested conditions could exist where significant reflections could occur. To directly measure the modes propagating in both directions within a duct, a second rake was mounted to the rotating system with an offset in both the axial and the azimuthal directions. The rotating rake data analysis technique was extended to include the data measured by the second rake. The analysis resulted in a set of circumferential mode levels at each of the two rake microphone locations. Radial basis functions were then least-squares fit to this data to obtain the radial mode amplitudes for the modes propagating in both directions within the duct. The fit equations were also modified to allow evanescent mode amplitudes to be computed. This extension of the rotating rake data analysis technique was tested using simulated data, numerical code produced data, and preliminary in-duct measured data.

Dahl, Milo D.↗
NASA NTRS1991

Linear and circular digital spectral analysis of serial data

Two methods of digital spectral analysis of unevenly sampled data are developed and illustrated here. One method uses a linear function of time (or space), the other uses circular functions. The circular method turns out to be essentially equivalent to a least-squares sine-wave analysis. The linear, anharmonic method uses only the field of real numbers and elementary algebraic operations, and hence it can be made computationally very fast and accurate. Both methods are very general, properly handling all kinds of time series ranging from simple series consisting only of the times of events to complicated series consisting of pulses with long duty cycles. The two methods are here applied to the analysis of annual mean relative sunspot numbers.

Stothers, Richard B.↗
NASA NTRS2017

Guidelines for VCCT-Based Interlaminar Fatigue and Progressive Failure Finite Element Analysis

This document is intended to detail the theoretical basis, equations, references and data that are necessary to enhance the functionality of commercially available Finite Element codes, with the objective of having functionality better suited for the aerospace industry in the area of composite structural analysis. The specific area of focus will be improvements to composite interlaminar fatigue and progressive interlaminar failure. Suggestions are biased towards codes that perform interlaminar Linear Elastic Fracture Mechanics (LEFM) using Virtual Crack Closure Technique (VCCT)-based algorithms [1,2]. All aspects of the science associated with composite interlaminar crack growth are not fully developed and the codes developed to predict this mode of failure must be programmed with sufficient flexibility to accommodate new functional relationships as the science matures.

Deobald, Lyle R.↗
NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

software↗
NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

python↗
NASA NTRS2013

Acoustic Emission Analysis Applet (AEAA) Software

NASA Glenn Research and NASA White Sands Test Facility have developed software supporting an automated pressure vessel structural health monitoring (SHM) system based on acoustic emissions (AE). The software, referred to as the Acoustic Emission Analysis Applet (AEAA), provides analysts with a tool that can interrogate data collected on Digital Wave Corp. and Physical Acoustics Corp. software using a wide spectrum of powerful filters and charts. This software can be made to work with any data once the data format is known. The applet will compute basic AE statistics, and statistics as a function of time and pressure (see figure). AEAA provides value added beyond the analysis provided by the respective vendors' analysis software. The software can handle data sets of unlimited size. A wide variety of government and commercial applications could benefit from this technology, notably requalification and usage tests for compressed gas and hydrogen-fueled vehicles. Future enhancements will add features similar to a "check engine" light on a vehicle. Once installed, the system will ultimately be used to alert International Space Station crewmembers to critical structural instabilities, but will have little impact to missions otherwise. Diagnostic information could then be transmitted to experienced technicians on the ground in a timely manner to determine whether pressure vessels have been impacted, are structurally unsound, or can be safely used to complete the mission.

Nichols, Charles T.↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗
NASA NTRS1972

Data Transmission Signal Design and Analysis

The error performances of several digital signaling methods are determined as a function of a specified signal-to-noise ratio. Results are obtained for Gaussian noise and impulse noise. Performance of a receiver for differentially encoded biphase signaling is obtained by extending the results of differential phase shift keying. The analysis presented obtains a closed-form answer through the use of some simplifying assumptions. The results give an insight into the analysis problem, however, the actual error performance may show a degradation because of the assumptions made in the analysis. Bipolar signaling decision-threshold selection is investigated. The optimum threshold depends on the signal-to-noise ratio and requires the use of an adaptive receiver.

Moore, J. D.↗
NASA NTRS1973

The development of a pseudo-nyquist analysis technique for hybrid sampled-data control systems

The stability characteristics of a launch vehicle, as a function of gain and phase variations at the thrust vector controller, cannot be obtained using classical sampled-data control theory if the launch vehicle attitude control system contains both sampled-data and continuous feedback control loops. A method was developed which can be used to generate a sampled-data pseudo-Nyquist plot for gain and phase variations at the controller. This method was developed and used to determine the stability characteristics of the Saturn 1B launch vehicle in the backup guidance mode.

Burnitt, M. G.↗
NASA NTRS1990

The diffuse galactic gamma ray emission

The EGRET (Energetic Gamma-Ray Experiment Telescope) detector will provide a much more detailed view of the diffuse galactic gamma ray intensity in terms of higher resolution, greater statistical significance, and broader energy range than earlier missions. These observations will furnish insight into a number of very important questions related to the dynamics and structure of the Galaxy. A diffuse emission model is being developed that incorporates the latest information on matter distribution and source functions. In addition, it is tailored to the EGRET instrument response functions. The analysis code of the model maintains flexibility to accommodate the quality of the data that is anticipated. The discussion here focuses on the issues of the distributions of matter, cosmic rays, and radiation fields, and on the important source functions that enter into the model calculation of diffuse emission.

Bertsch, David L.↗
NASA NTRS2014

Guided Wave Delamination Detection and Quantification With Wavefield Data Analysis

Unexpected damage can occur in aerospace composites due to impact events or material stress during off-nominal loading events. In particular, laminated composites are susceptible to delamination damage due to weak transverse tensile and inter-laminar shear strengths. Developments of reliable and quantitative techniques to detect delamination damage in laminated composites are imperative for safe and functional optimally-designed next-generation composite structures. In this paper, we investigate guided wave interactions with delamination damage and develop quantification algorithms by using wavefield data analysis. The trapped guided waves in the delamination region are observed from the wavefield data and further quantitatively interpreted by using different wavenumber analysis methods. The frequency-wavenumber representation of the wavefield shows that new wavenumbers are present and correlate to trapped waves in the damage region. These new wavenumbers are used to detect and quantify the delamination damage through the wavenumber analysis, which can show how the wavenumber changes as a function of wave propagation distance. The location and spatial duration of the new wavenumbers can be identified, providing a useful means not only for detecting the presence of delamination damage but also allowing for estimation of the delamination size. Our method has been applied to detect and quantify real delamination damage with complex geometry (grown using a quasi-static indentation technique). The detection and quantification results show the location, size, and shape of the delamination damage.

Tian, Zhenhua↗
NASA NTRS2011

Ares I-X In-Flight Modal Identification

Operational modal analysis is a procedure that allows the extraction of modal parameters of a structure in its operating environment. It is based on the idealized premise that input to the structure is white noise. In some cases, when free decay responses are corrupted by unmeasured random disturbances, the response data can be processed into cross-correlation functions that approximate free decay responses. Modal parameters can be computed from these functions by time domain identification methods such as the Eigenvalue Realization Algorithm (ERA). The extracted modal parameters have the same characteristics as impulse response functions of the original system. Operational modal analysis is performed on Ares I-X in-flight data. Since the dynamic system is not stationary due to propellant mass loss, modal identification is only possible by analyzing the system as a series of linearized models over short periods of time via a sliding time-window of short time intervals. A time-domain zooming technique was also employed to enhance the modal parameter extraction. Results of this study demonstrate that free-decay time domain modal identification methods can be successfully employed for in-flight launch vehicle modal extraction.

Bartkowicz, Theodore J.↗
NASA NTRS1975

Advances in automatic extraction of information from multispectral scanner data

The state-of-the-art of automatic multispectral scanner data analysis and interpretation is reviewed. Sources of system variability which tend to obscure the spectral characteristics of the classes under consideration are discussed, and examples of the application of spatial and temporal discrimination bases are given. Automatic processing functions, techniques and methods, and equipment are described with particular attention to those that are applicable to large land surveys using satellite data. The development and characteristics of the Multivariate Interactive Digital Analysis System (MIDAS) for processing aircraft or satellite multispectral scanning data are discussed in detail. The MIDAS system combines the parallel digital implementation capabilities of a low-cost processor with a general purpose PDP-11/45 minicomputer to provide near-real-time data processing. The preprocessing functions are user-selectable. The input subsystem accepts data stored on high density digital tape, computer compatible tape, and analog tape.

Erickson, J. D.↗