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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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We present a comparison between two numerical methods for the extrapolation of nonlinear force-free magnetic fields, the Iterative Method (IM) and the Progressive Extension Method (PEM). The advantages and disadvantages of these two methods are summarized and the accuracy and numerical instability are discussed. On the basis of this investigation, we claim that the two methods do resemble each other qualitatively.
Contamination on bonding surfaces has been determined to be a primary cause for degraded bond strength in certain solid rocket motor bondlines. Hydrocarbon and silicone based organic contaminants that are airborne or directly introduced to a surface are a significant source of contamination. Diffuse infrared (IR) reflectance has historically been used as an effective technique for detection of organic contaminants, however, common laboratory methods involving the use of a Fourier transform IR spectrometer (FTIR) are impractical for inspecting the large bonding surface areas found on solid rocket motors. Optical methods involving the use of acousto-optic tunable filters and fixed bandpass optical filters are recommended for increased data acquisition speed. Testing and signal analysis methods are presented which provide for simultaneous measurement of contamination concentration and roughness level on rough metal surfaces contaminated with hydrocarbons.
A system for display on a single video display terminal of multiple physiological measurements is provided. A subject is monitored by a plurality of instruments which feed data to a computer programmed to receive data, calculate data products such as index of engagement and heart rate, and display the data in a graphical format simultaneously on a single video display terminal. In addition live video representing the view of the subject and the experimental setup may also be integrated into the single data display. The display may be recorded on a standard video tape recorder for retrospective analysis.
We report initial results on the statistical properties of the dimmest gamma-ray bursts (GRBs) observed with the Burst and Transient Source Experiment (BATSE), using new ground-based methods to obtain a sample of GRBs from 502 days of BATSE data. Using the most sensitive ground-based detection of GRBs, the sample extends to GRBs much fainter than those detected by the on-board trigger, but because of the temporal resolution of the data, the sample is limited to GRBs of duration of at least 2(approx.)s. For each detected event, Bayesian probabilities are calculated for the event to belong to each of seven classes of differing physical origins. The sample of GRB candidates is defined by the requirement that the Bayesian probability for belonging to the GRB class is higher than 0.5. The intensity distribution of the GRB sample is corrected using a Monte Carlo simulation of the post-flight detection efficiency. The dimmest BATSE bursts of the sample continue the hardness-intensity trend seen in brighter GREs and are consistent with isotropy.
The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.
A diverse set of data analytics systems for Earth Observations are sprouting up in the Earth Science community, with a wealth of processing algorithms and analysis methods. There is a similar wealth of data resources available via myriad data providers and clearinghouses, including large institutional systems like the Earth Observing System Data and Information System, Comprehensive Large Scale Array-data Stewardship System, and Federated Earth Observation Missions gateway. With Earth system science driving a need to work with more datasets together, and the community developing more analysis tools (some of them dataset-specific), how can we develop analysis workflows that incorporate far-flung datasets and leverage analysis resources from multiple organizations? Cloud computing points the way toward a solution in two different respects. Firstly, the access to and abstraction of virtually unlimited storage and computing power provides an environment that enables more straightforward means of pulling datasets and analysis resources together. Just as importantly, however, cloud computing serves as an example of an "ecosystem" of interoperating services, since the essence of cloud computing is the presentation of all resources as a service, from hardware to infrastructure to platform to software. This enables the combination of off-the-shelf, diverse services to construct entire systems that emerge out of an equally diverse community of architects and developers. This approach can be similarly applied to the data and analysis resources in the Earth Observation community. By exposing these resources via well understood services, and consuming resources in the same way, different organizations can construct bespoke analysis workflows and systems for their own purposes. The key leap the community needs to make is to develop analysis systems in components that interact with other components via services. The result would be a rich ecosystem of analytics components that can be combined to analyze datasets at scale and in conjunction with other datasets from other sources.
Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.
Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.
The main sensor of the Vogager plasma experiment consists of a cluster of three, modulated-grid Faraday cups whose normals are arranged symmetrically about the symmetry axis of the cluster at an angle of 20 degrees to that axis. In interplanetary space, each cup explores the positive ion distribution by accepting particles from contiguous slices in velocity space. The slices are narrow in the direction of the normal to the modulating grid but are broad in planes parallel to that grid. The resulting three sets of measurements can be combined to yield the three-dimensional distribution function in the following way: the distribution function is assumed to be gyrotropic. For each value of speed in a frame of reference moving with the bulk velocity of the solar wind, the variation of the distribution function with angle from the field direction is represented by a series of Legendre polynomials. Effects such as double-streaming and heat flow can be well represented by using only the first three terms of the series which are fully specified by the measurements. Examples of the use of this method in the analysis of Voyager data are shown.
NASA has collected vibroacoustic response data on a variety of complex, aerospace structures to support research into numerical modeling of such structures. This data is being made available to the modeling community to promote the development and validation of analysis methods for these types of structures. Existing data from two structures is described, as well as plans for a data set from a third structure. The first structure is a 1.22 m by 1.22 m stiffened aluminum panel, typical of a commercial aircraft sidewall section. The second is an enclosed, stiffened aluminum cylinder, approximately 3.66 m long and 1.22 m in diameter, constructed to resemble a small aircraft fuselage with no windows and a periodic structure. The third structure is a filament-wound composite cylinder with composite stiffeners. Numerous combinations of excitation and response variables were measured on the structures, including: shaker excitation; diffuse acoustic field; velocity response from a laser vibrometer; intensity scans; and point acceleration.
A joint Army/NASA program was conducted to design, fabricate, and test an advanced, small gas turbine, reverse-flow combustor utilizing a compliant metal/ceramic (CMC) wall cooling concept. The objectives of this effort were to develop a design method (basic design data base and analysis) for the CMC cooling technique and then demonstrate its application to an advanced cycle, small, reverse-flow combustor with 3000 F burner outlet temperature. The CMC concept offers significant improvements in wall cooling effectiveness resulting in a large reduction in cooling air requirements. Therefore, more air is available for control of burner outlet temperature pattern in addition to the benefits of improved efficiency, reduced emissions, and lower smoke levels. The program was divided into four tasks. Task 1 defined component materials and localized design of the composite wall structure in conjunction with development of basic design models for the analysis of flow and heat transfer through the wall. Task 2 included implementation of the selected materials and validated design models during combustor preliminary design. Detail design of the selected combustor concept and its refinement with 3D aerothermal analysis were completed in Task 3. Task 4 covered detail drawings, process development and fabrication, and a series of burner rig tests. The purpose of this paper is to provide details of the investigation into the fundamental flow and heat transfer characteristics of the CMC wall structure as well as implementation of the fundamental analysis method for full-scale combustor design.
The cost and effectiveness of developing land cover information derived from Landsat imagery for hydrologic studies are compared with the cost and effectiveness of conventional sources. The analysis shows that the conventional and Landsat methods are nearly equally effective in providing adequate land cover data for hydrologic studies. The total cost effectiveness analysis demonstrates that the conventional method is cost effective for a study area of less than 26 sq km and that the Landsat method is to be preferred for areas of more than 26 sq km.
During conceptual design studies of advanced aircraft, the usual practice is to use linear theory to calculate the aerodynamic characteristics of candidate rigid (nonflexible) geometric external shapes. Recent developments and improvements in computational methods, especially computational fluid dynamics (CFD), provide significantly improved capability to generate detailed analysis data for the use of all disciplines involved in the evaluation of a proposed aircraft design. A multidisciplinary application of such analysis methods to calculate the effects of nonlinear aerodynamics and static aeroelasticity on the mission performance of a fighter aircraft concept is described. The aircraft configuration selected for study was defined in a previous study using linear aerodynamics and rigid geometry. The results from the previous study are used as a basis of comparison for the data generated herein. Aerodynamic characteristics are calculated using two different nonlinear theories, potential flow and rotational (Euler) flow. The aerodynamic calculations are performed in an iterative procedure with an equivalent plate structural analysis method to obtain lift and drag data for a flexible (nonrigid) aircraft. These static aeroelastic data are then used in calculating the combat and mission performance characteristics of the aircraft.
A method to separate main rotor and tail rotor noise from a helicopter in flight is explored. Being the sum of two periodic signals of disproportionate, or incommensurate frequencies, helicopter noise is neither periodic nor stationary. The single Fourier transform divides signal energy into frequency bins of equal size. Incommensurate frequencies are therefore not adequately represented by any one chosen data block size. A two-dimensional Fourier analysis method is used to separate main rotor and tail rotor noise. The two-dimensional spectral analysis method is first applied to simulated signals. This initial analysis gives an idea of the characteristics of the two-dimensional autocorrelations and spectra. Data from a helicopter flight test is analyzed in two dimensions. The test aircraft are a Boeing MD902 Explorer (no tail rotor) and a Sikorsky S-76 (4-bladed tail rotor). The results show that the main rotor and tail rotor signals can indeed be separated in the two-dimensional Fourier transform spectrum. The separation occurs along the diagonals associated with the frequencies of interest. These diagonals are individual spectra containing only information related to one particular frequency.
A method to separate main rotor and tail rotor noise from a helicopter in flight is explored. Being the sum of two periodic signals of disproportionate, or incommensurate frequencies, helicopter noise is neither periodic nor stationary. The single Fourier transform divides signal energy into frequency bins of equal size. Incommensurate frequencies are therefore not adequately represented by any one chosen data block size. A two-dimensional Fourier analysis method is used to separate main rotor and tail rotor noise. The two-dimensional spectral analysis method is first applied to simulated signals. This initial analysis gives an idea of the characteristics of the two-dimensional autocorrelations and spectra. Data from a helicopter flight test is analyzed in two dimensions. The test aircraft are a Boeing MD902 Explorer (no tail rotor) and a Sikorsky S-76 (4-bladed tail rotor). The results show that the main rotor and tail rotor signals can indeed be separated in the two-dimensional Fourier transform spectrum. The separation occurs along the diagonals associated with the frequencies of interest. These diagonals are individual spectra containing only information related to one particular frequency.
A method to separate main rotor and tail rotor noise from a helicopter in flight is explored. Being the sum of two periodic signals of disproportionate, or incommensurate frequencies, helicopter noise is neither periodic nor stationary. The single Fourier transform divides signal energy into frequency bins of equal size. Incommensurate frequencies are therefore not adequately represented by any one chosen data block size. A two-dimensional Fourier analysis method is used to separate main rotor and tail rotor noise. The two-dimensional spectral analysis method is first applied to simulated signals. This initial analysis gives an idea of the characteristics of the two-dimensional autocorrelations and spectra. Data from a helicopter flight test is analyzed in two dimensions. The test aircraft are a Boeing MD902 Explorer (no tail rotor) and a Sikorsky S-76 (4-bladed tail rotor). The results show that the main rotor and tail rotor signals can indeed be separated in the two-dimensional Fourier transform spectrum. The separation occurs along the diagonals associated with the frequencies of interest. These diagonals are individual spectra containing only information related to one particular frequency.
Aircraft designers commit significant resources to the design of aircraft in meeting performance goals. Despite fulfilling traditional design requirements, many fighter aircraft have encountered buffet loads when demonstrating their high angle-of-attack maneuver capabilities. As a result, during test or initial production phases of fighter development programs, many new designs are impacted, usually in a detrimental way, by resulting in reassessing designs or limiting full mission capability. These troublesome experiences usually stem from overlooking or completely ignoring the effects of buffet during the design phase of aircraft. Perhaps additional requirements are necessary that addresses effects of buffet in achieving best aircraft performance in fulfilling mission goals. This paper describes a reliable, fairly simple, but quite general buffet loads analysis method to use in the initial design phases of fighter-aircraft development. The method is very similar to the random gust load analysis that is now commonly available in a commercial code, which this analysis capability is based, with some key modifications. The paper describes the theory and the implementation of the methodology. The method is demonstrated on a JSF prototype example problem. The demonstration also serves as a validation of the method, since, in the paper, the analysis is shown to nearly match the flight data. In addition, the paper demonstrates how the analysis method can be used to assess candidate design concepts in determining a satisfactory final aircraft configuration.
Aircraft designers commit significant resources to the design of aircraft in meeting performance goals. Despite fulfilling traditional design requirements, many fighter aircraft have encountered buffet loads when demonstrating their high angle-of-attack maneuver capabilities. As a result, during test or initial production phases of fighter development programs, many new designs are impacted, usually in a detrimental way, by resulting in reassessing designs or limiting full mission capability. These troublesome experiences usually stem from overlooking or completely ignoring the effects of buffet during the design phase of aircraft. Perhaps additional requirements are necessary that addresses effects of buffet in achieving best aircraft performance in fulfilling mission goals. This paper describes a reliable, fairly simple, but quite general buffet loads analysis method to use in the initial design phases of fighter-aircraft development. The method is very similar to the random gust load analysis that is now commonly available in a commercial code, which this analysis capability is based, with some key modifications. The paper describes the theory and the implementation of the methodology. The method is demonstrated on a JSF prototype example problem. The demonstration also serves as a validation of the method, since, in the paper, the analysis is shown to nearly match the flight data. In addition, the paper demonstrates how the analysis method can be used to assess candidate design concepts in determining a satisfactory final aircraft configuration.