Improving the DCC Invariant Target Stability Methodology using EPIC and VIIRS
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The magnitude of yield and flow stresses in aged Inconel 718 are observed to be different in tension and compression. This phenomenon, called the Strength differential (SD), contradicts the metal plasticity axiom that the second deviatoric stress invariant alone is sufficient for representing yield and flow. Apparently, at least one of the other two stress invariants is also significant. A unified viscoplastic model was developed that is able to account for the SD effect in aged Inconel 718. Building this model involved both theory and experiments. First, a general threshold function was proposed that depends on all three stress invariants and then the flow and evolution laws were developed using a potential-based thermodynamic framework. Judiciously chosen shear and axial tests were conducted to characterize the material. Shear tests involved monotonic loading, relaxation, and creep tests with different loading rates and load levels. The axial tests were tension and compression tests that resulted in sufficiently large inelastic strains. All tests were performed at 650 C. The viscoplastic material parameters were determined by optimizing the fit to the shear tests, during which the first and the third stress invariants remained zero. The threshold surface parameters were then fit to the tension and compression test data. An experimental procedure was established to quantify the effect of each stress invariant on inelastic deformation. This requires conducting tests with nonproportional three-dimensional load paths. Validation of the model was done using biaxial tests on tubular specimens of aged Inconel 718 using proportional and nonproportional axial-torsion loading. These biaxial tests also helped to determine the most appropriate form of the threshold function; that is, how to combine the stress invariants. Of the set of trial threshold functions, the ones that incorporated the third stress invariant give the best predictions. However, inclusion of the first stress invariant does not significantly improve the model predictions. The model shows excellent predictive capability for nonproportional load paths. Additionally, it reduces to the well-known models of Mises Drucker and Drucker-Prager. The requisite experiments involve reasonably simple load paths in the axial-shear stress plane and hence can be performed on a variety of different materials: be they metallic, geological. polymeric, ceramic or granular. The general form of the threshold function allows representation of inelastic deformation in a range of materials.
The theory for the generalized first invariant for adiabatic motion of charged particles in regions where there are large gradients in magnetic or electric fields is developed. The general condition for an invariant to exist in such regions is that the potential well in which the particle oscillates change its shape slowly as the particle drifts. It is shown how the Kruskal (1962) procedure can be applied to obtain expressions for the invariant and for drift velocities that are asymptotic in a smallness parameter epsilon. The procedure is illustrated by obtaining the invariant and drift velocities for particles traversing a perpendicular shock, and the generalized invariant is compared with the magnetic moment, and the drift orbits with the actual orbits, for a particular case. In contrast to the magnetic moment, the generalized first invariant is better for large gyroradii (large kinetic energies) than for small gyroradii. Expressions for the invariant when an electrostatic potential jump is imposed across the perpendicular shock, and when the particle traverses a rotational shear layer with a small normal component of the magnetic field are given.
A preliminary investigation of the geometry of flow patterns in numerically simulated compressible and incompressible mixing layers was carried out using 3-D critical point methodology. Motions characterized by high rates of kinetic energy dissipation and/or high enstrophy were of particular interest. In the approach the partial derivatives of the velocity field are determined at every point in the flow. These are used to construct the invariants of the velocity gradient tensor and the rate-of-strain tensor (P, Q, R, and P(sub s), Q(sub s), R(sub s) respectively). For incompressible flow the first invariant is zero. For the conditions of the compressible simulation, the first invariant is found to be everywhere small, relative to the second and third invariants, and so in both cases the local topology at a point is mainly determined by the second and third invariants. The data at every grid point is used to construct scatter plots of Q versus R and Q(sub s) versus R(sub s). Most points map to a cluster near the origin in Q-R space. However, fine scale motions, that is motions which are characterized by velocity derivatives which scale with the square root of R(sub delta), tend to map to regions which lie far from the origin. Definite trends are observed for motions characterized by high enstrophy and/or high dissipation. The observed trends suggest that, for these motions, the second and third invariants of the velocity gradient and rate-of-strain tensors are strongly correlated. The second and third invariants of the rate-of-strain tensor are related by K(-Q(sub s))(exp 3/2), which is consistent with the above scaling of velocity derivatives. The quantity K appears to depend on Reynolds number with an upper limit K = 2(the square root of 3)/9 corresponding to locally axisymmetric flow. For both the compressible and incompressible mixing layer, regions corresponding to high rates of dissipation are found to be characterized by comparable magnitudes of R(sub ij)R(sub ij) and S(sub ij)S(sub ij). For the incompressible mixing layer, regions characterized by the highest values of enstrophy are found to have relatively low strain rates.
We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization of sequence of actions.
Plasmas, such as those found in the space environment or in plasma confinement devices, are often modeled as electrically conducting fluids. When fluids and plasmas are energetically stirred, regions of highly nonlinear, chaotic behavior known as turbulence arise. Understanding the fundamental nature of turbulence is a long-standing theoretical challenge. The present work describes a statistical theory concerning a certain class of nonlinear, finite dimensional, dynamical models of turbulence. These models arise when the partial differential equations describing incompressible, ideal (i.e., nondissipative) homogeneous fluid and magnetofluid (i.e., plasma) turbulence are Fourier transformed into a very large set of ordinary differential equations. These equations define a divergenceless flow in a high-dimensional phase space, which allows for the existence of a Liouville theorem, guaranteeing a distribution function based on constants of the motion (integral invariants). The novelty of these particular dynamical systems is that there are integral invariants other than the energy, and that some of these invariants behave like pseudoscalars under two of the discrete symmetry transformations of physics, parity, and charge conjugation. In this work the 'rugged invariants' of ideal homogeneous turbulence are shown to be the only significant scalar and pseudoscalar invariants. The discovery that pseudoscalar invariants cause symmetries of the original equations to be dynamically broken and induce a nonergodic structure on the associated phase space is the primary result presented here. Applicability of this result to dissipative turbulence is also discussed.
Consistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target-modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2 percent and 3 percent for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions. The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1 percent for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1 percent and 2 percent for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.
The electrostatic field associated with the rotation of Jupiter, relative to the rest frame of Io, would be distorted if the satellite were an electrical conductor. An idealized two-dimensional model of the distorted electric-field configuration, in the limit of a perfectly conducting satellite or satellite ionosphere, has been constructed and used to trace the adiabatic guiding-center trajectories of energetic protons and electrons across Jupiter's magnetic field lines, which are taken as rectilinear. The adiabatic trajectories of very low-energy particles (cold plasma) are found to avoid the satellite and escape absorption. In the limit of very high particle energies, the adiabatic trajectories are undistorted, and absorption proceeds as if Io were an insulator. The interpolation between these limits is monotonic for protons, such that Io sweeps out a drift shell half as wide as the satellite for first invariants of the order of 1 GeV per gauss. The situation for electrons is more complicated, and no absorption from adiabatic trajectories is found at first invariants not exceeding 46 GeV per gauss. Electrons having first invariants of at least 50 GeV per gauss are typically swept out of drift shells wider than the satellite itself. However, electrons can impact only a portion of Io's exposed hemisphere for first invariants of 50-200 GeV per gauss. Thus, the particle-absorbing characteristics of an electrically conducting Jovian satellite are found to depend on both the species and the energy of the incident particle, and the satellite's particle-absorbing cross section differs systematically from its geometric cross section.
The invariant imbedding technique is applied to the problem of resonance line scattering with frequency redistribution. By applying the principles of invariance to the resonance line scattering problem a set of non-linear, integrodifferential equations for the scattering and transmission functions are derived. Thus, an alternate approach to the resonance line scattering problem is presented. Rather than solving for the specific intensity directly the invariant imbedding equations may be solved for the scattering and transmission functions from which the specific intensity may be calculated. The technique is very general and in principle, can accommodate scattering in inhomogeneous atmospheres with arbitrary frequency redistribution and scattering phase functions. Solutions to the invariant imbedding equations in some simple cases are presented. If the scattering is isotropic and completely noncoherent and the atmosphere may be modeled as homogeneous, the scattering and transmission functions may be expressed in terms of generalized H, X and Y functions. This technique is extended to the problems of coupled spectral lines and scattering of radiation from internal sources.
HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.