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The theory and applications of the Akaike information criterion (AIC) Mixture model simulations and the application of the AIC to a large portion of a LANDSAT segment are presented.
Recent developments in the three-dimensional aerodynamic theory of inviscid flow in transonic axial compressors are reviewed. Emphasis is placed on the newly completed lifting surface theory of a transonic ducted rotor. The relationship between the lifting surface theory and axisymmetric through-theories of turbomachines is illustrated; a few examples of the additional information obtainable from the new theory are then given. Quasi-two-dimensional cascade theory can also be extracted from the present analysis and the relevance of cascade theory to the actual three-dimensional problem assessed. Details are reported elsewhere, but some of the qualitative conclusions are discussed here. Even moderate departure from uniform spanwise loading of the rotor blades, for example, leads to a rather profound influence of the downstream wakes, suggesting the need for considerable care in applying cascade data on a direct quasi-two-dimensional basis.
Gravitational radiation in the far field was examined using a formalism that encompassed all metric theories of gravity. There are six possible modes of polarization, which can be completely resolved by feasible experiments. A theoretical framework is set forth for classification of waves and theories, based on the Lorentz transformation properties of the six modes. Also shown in detail is how the six modes may be experimentally identified and to what extent such information limits the correct theory of gravity.
Gravitational-wave observations can be powerful tools in the testing of relativistic theories of gravity - perhaps the only tools for distinguishing between certain extant theories in the foreseeable future. In this paper we examine gravitational radiation in the far field using a formalism that encompasses all 'metric theories of gravity.' There are six possible modes of polarization, which can be completely resolved by feasible experiments. We set forth a theoretical framework for classification of waves and theories, based on the Lorentz transformation properties of the six modes. We also show in detail how the six modes may be experimentally identified and to what extent such information limits the 'correct' theory of gravity.
It is possible to obtain the masses of Cepheid variables by several methods involving the pulsation theory. However, these masses are frequently smaller than those indicated by the theory of stellar evolution. The cause of this discrepancy is not fully understood. Since the pulsation theory indicates that there is a relation among the mass, the radius and the period, the discrepancy also manifests itself in the radii of these stars. With this in mind, radius determinations for two Cepheids, SU Cas and TU Cas, were undertaken. It is concluded that because of the agreement between the present radius and the beat radius of TU Cas, the pulsation theory is giving correct information about the radii of beat Cepheids. This implies that the luminosities of short period Cepheids have been overestimated. Thus, the solution to the mass discrepancy should perhaps be sought in the theory of stellar evolution or in the possibility of mass loss.
A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.
When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.
Both in practical engineering and in control of muscular systems, low level subsystems automatically provide crude approximations to the proper response. Through low level tuning of these approximations, the proper response variant can emerge from standardized high level commands. Such systems are expressly suited to emerging large scale integrated circuit technology. A computer, using symbolic descriptions of subsystem responses, can select and shape responses of low level digital or analog microcircuits. A mathematical theory that reveals significant informational units in this style of control and software for realizing such information structures are formulated.
A computerized wave climate model is developed that applies linear wave theory and shelf depth information to predict wave behavior as they pass over the continental shelf as well as the resulting wave energy distributions along the coastline. Reviewed are also the geomorphology of the Mid-Atlantic Continental Shelf, wave computations resulting from 122 wave input conditions, and a preliminary analysis of these data.
A discrete, time-invariant system is represented by an equivalent continuous, periodic system. A combined continuous plant and discrete controller may then be formulated as a single continuous, periodic coefficient system. The exact stability solution for the combined system is obtained, as a matrix eigenvalue problem. Periodic system theory also gives some information about the type of instabilities which may be encountered in the combined plant and controller.
A framework is presented for a systematic kinetic theory of turbulence originating from the Liouville equation for the Fourier coefficients of fluid variables. The real and imaginary parts of these Fourier coefficients play the role that particle coordinates (positions and momenta) play in the BBGKY theory. The basic relations of the problem are the incompressible Navier-Stokes equations in two dimensions with zero viscosity, with the probability distributions of Fourier coefficients rather than moments being the basic variables of the theory. A kinetic equation is derived and shown to possess a number of requirements that any reasonable kinetic equation must have: conservation laws, positive-definite spectral densities, and an H-theorem. The major lack in the theory is any reliable information on the relaxation predicted by the complicated linear operator H. Closure of the hierarchy is achieved by the hypothesis that the five-coefficient correlation function is negligible. Problems associated with inclusion of viscosity and external driving forces are discussed.
A scanning electron microscope equipped with an energy dispersive spectrographic unit was used to study in detail the Al2O3 oxide morphology and oxide-metal interface of a nickel-rich NiAl alloy (Ni-42 at% Al) with a view toward providing information relevant to spalling theories in the MCrAlY system. In addition, the kinetics of spalling was studied for critical evaluation of one of the step-process spall models and for collection of pertinent experimental data. Cyclic oxidation tests were run in 1-atm air at about 1100 C, and isothermal specimens were examined from 2 min to 200 hr of exposure. It is shown that oxide spalling occurs primarily at the oxide-metal interface in an intergranular mode, that crystallographic Kirkendall voids arise as a result of oxidation, and that the fractional area of spalled oxide varies widely in cyclic tests but increases with the square of oxide thickness for isothermal tests. A spall model is developed in the form of a summation series.
Reception of the exceedingly small signals from spacecraft typical of deep space communication requires antennas of enormous size, complexity, and precision. The two Voyager spacecraft each have 20-watt X-band transmitters; and at their Saturn encounter distances from Earth of approximately 1.5 billion kilometers, the power density received on the Earth was less than 10 to the minus 19th power watts per square meters. The thrust in deep-space communications improvement was in the areas of ground and spacecraft antenna size and performance increases, spacecraft transmitter power increase, ground receiving system design, and telemetry information coding. Ground antenna theory, design, and performance as related to the particular problem of receiving spacecraft signals using the Deep Space Network (DSN) is described.
The effects of relatively strong upstream acoustic excitation on the mixing of heated jets with the surrounding air are investigated. To determine the extent of the available information on experiments and theories dealing with acoustically excited heated jets, an extensive literature survey was carried out. The experimental program consisted of flow visualization and flowfield velocity and temperature measurements for a broad range of jet operating and flow excitation conditions. A 50.8-mm-diam nozzle was used for this purpose. Parallel to the experimental study, an existing theoretical model of excited jets was refined to include the region downstream of the jet potential core. Excellent agreement was found between theory and experiment in moderately heated jets. However, the theory has not yet been confirmed for highly heated jets. It was found that the sensitivity of heated jets to upstream acoustic excitation varies strongly with the jet operating conditions and that the threshold excitation level increases with increasing jet temperature. Furthermore, the preferential Strouhal number is found not to change significantly with a change of the jet operating conditions. Finally, the effects of the nozzle exit boundary layer thickness appear to be similar for both heated and unheated jets at low Mach numbers.
Two papers are included in this progress report. In the first, the compressible Navier-Stokes equations have been used to compute leading edge receptivity of boundary layers over parabolic cylinders. Natural receptivity at the leading edge was simulated and Tollmien-Schlichting waves were observed to develop in response to an acoustic disturbance, applied through the farfield boundary conditions. To facilitate comparison with previous work, all computations were carried out at a free stream Mach number of 0.3. The spatial and temporal behavior of the flowfields are calculated through the use of finite volume algorithms and Runge-Kutta integration. The results are dominated by strong decay of the Tollmien-Schlichting wave due to the presence of the mean flow favorable pressure gradient. The effects of numerical dissipation, forcing frequency, and nose radius are studied. The Strouhal number is shown to have the greatest effect on the unsteady results. In the second paper, a transition model for low-speed flows, previously developed by Young et al., which incorporates first-mode (Tollmien-Schlichting) disturbance information from linear stability theory has been extended to high-speed flow by incorporating the effects of second mode disturbances. The transition model is incorporated into a Reynolds-averaged Navier-Stokes solver with a one-equation turbulence model. Results using a variable turbulent Prandtl number approach demonstrate that the current model accurately reproduces available experimental data for first and second-mode dominated transitional flows. The performance of the present model shows significant improvement over previous transition modeling attempts.
Magnetospheric Multiscale (MMS), a NASA four-spacecraft constellation mission launched on March 12, 2015, will investigate magnetic reconnection in the boundary regions of the Earth's magnetosphere, particularly along its dayside boundary with the solar wind and the neutral sheet in the magnetic tail. The most important goal of MMS is to conduct a definitive experiment to determine what causes magnetic field lines to reconnect in a collisionless plasma. The significance of the MMS results will extend far beyond the Earth's magnetosphere because reconnection is known to occur in interplanetary space and in the solar corona where it is responsible for solar flares and the disconnection events known as coronal mass ejections. Active research is also being conducted on reconnection in the laboratory and specifically in magnetic-confinement fusion devices in which it is a limiting factor in achieving and maintaining electron temperatures high enough to initiate fusion. Finally, reconnection is proposed as the cause of numerous phenomena throughout the universe such as comet-tail disconnection events, magnetar flares, supernova ejections, and dynamics of neutron-star accretion disks. The MMS mission design is focused on answering specific questions about reconnection at the Earth's magnetosphere. The prime focus of the mission is on determining the kinetic processes occurring in the electron diffusion region that are responsible for reconnection and that determine how it is initiated; but the mission will also place that physics into the context of the broad spectrum of physical processes associated with reconnection. Connections to other disciplines such as solar physics, astrophysics, and laboratory plasma physics are expected to be made through theory and modeling as informed by the MMS results.
As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.