In this paper we have shown how the direction cosine method of stripmap-mode IFSAR maybe modified for use in the spotlight-mode case. Spotlight-mode IFSAR geometry dictates a common aperture phase center, velocity vector, and baseline vector for every pixel in an image. Angle with respect to the velocity vector is the same for every pixel in a given column and can be computed from the column index, the Doppler of the motion compensation point and the Doppler column sample spacing used in image formation. With these modifications, the direction cosines and length of the line of sight vector to every scatterer in the scene may be computed directly from the raw radar measurements of range, Doppler, and interferometric phase.
One of the key elements of the Stochastic Finite Element Method, namely the polynomial chaos expansion, has been utilized in a nonlinear shock and vibration application. As a result, the computed response was expressed as a random process, which is an approximation to the true solution process, and can be thought of as a generalization to solutions given as statistics only. This approximation to the response process was then used to derive an analytically-based design specification for component shock response that guarantees a balanced level of marginal reliability. Hence, this analytically-based reference SRS might lead to an improvement over the somewhat ad hoc test-based reference in the sense that it will not exhibit regions of conservativeness. nor lead to overtesting of the design.
Experienced experimentalists have gone through the process of attempting to identify a final set of modal parameters from several different sets of extracted parameters. Usually, this is done by visually examining the mode shapes. With the advent of automated modal parameter extraction algorithms such as SMAC (Synthesize Modes and Correlate), very accurate extractions can be made to high frequencies. However, this process may generate several hundred modes that then must be consolidated into a final set of modal information. This has motivated the authors to generate a set of tools to speed the process of consolidating modal parameters by mathematical (instead of visual) means. These tools help quickly identify the best modal parameter extraction associated with several extractions of the same mode. The tools also indicate how many different modes have been extracted in a nominal frequency range and from which references. The mathematics are presented to achieve the best modal extraction of multiple modes at the same nominal frequency. Improvements in the SMAC graphical user interface and database are discussed that speed and improve the entire extraction process.
The solution-mediated synthesis and single crystal structure of (CN{sub 3}H{sub 6}){sub 2} {center_dot} Zn(HPO{sub 3}){sub 2} are reported. This phase is built up from a three-dimensional framework of vertex-linked ZnO{sub 4} and HPO{sub 3} building units encapsulating the extra-framework guanidinium cations. The structure is stabilized by template-to-framework hydrogen bonding. The inorganic framework shows a surprising similarity to those of some known zinc phosphates. Crystal data: (CN{sub 3}H{sub 6}){sub 2} {center_dot} Zn(HPO{sub 3}){sub 2}, AI,= 345.50, orthorhombic, space group Fdd2 (No. 43), a = 15.2109 (6) {angstrom}, b = 11.7281 (5) {angstrom}, c = 14.1821 (6) {angstrom}, V = 2530.0 (4){angstrom}{sup 3}, Z = 8, T = 298 (2)K, R(F) = 0.020, wR(F) = 0.025.
The thermal-hydrologic (TH) and coupled process models describe the evolution of a potential geologic repository as heat is released from emplaced waste. The evolution (thermal, hydrologic, chemical, and mechanical) of the engineered barrier and geologic systems is heavily dependent on the heat released by the waste packages and how the heat is transferred from the emplaced wastes through the drifts and through the repository host rock. The essential elements of this process are extracted (or abstracted) from the process-level models that incorporate the basic energy and mass conservation principles and applied to the total system models used to describe the overall performance of the potential repository. The process of total system performance assessment (TSPA) abstraction is the following. First is a description of the parameter inputs used in the process-level models. A brief description is given hereof past inputs for the viability assessment (e.g., for TSPA-VA) and current inputs for the site recommendation (TSPA-SR). This is followed by a highlight of the process-level models from which the abstractions are made. These include descriptions of TH, thermal-hydrologic-chemical (THC), and thermal-mechanical (TM) processes used to describe the performance of individual waste packages and waste emplacement drifts as well as the repository as a whole. Next is a description of what (and how) information is abstracted from the process-level models. This also includes an accounting of the features, events, and processes (FEPs) that are important to both the regulators and the international repository community in general. Finally, an identification of the TSPA model components that utilize the abstracted information to characterize the overall performance of a potential geologic repository is given.
In this paper an optimization-based method of drift prevention is presented for learning control of underdetermined linear and weakly nonlinear time-varying dynamic systems. By defining a fictitious cost function and the associated model-based sub-optimality conditions, a new set of equations results, whose solution is unique, thus preventing large drifts from the initial input. Moreover, in the limiting case where the modeling error approaches zero, the input that the proposed method converges to is the unique feasible (zero error) input that minimizes the fictitious cost function, in the linear case, and locally minimizes it in the (weakly) nonlinear case. Otherwise, under mild restrictions on the modeling error, the method converges to a feasible sub-optimal input.
The abstraction model used for seepage into emplacement drifts in recent TSPA simulations has been presented. This model contributes to the calculation of the quantity of water that might contact waste if it is emplaced at Yucca Mountain. Other important components of that calculation not discussed here include models for climate, infiltration, unsaturated-zone flow, and thermohydrology; drip-shield and waste-package degradation; and flow around and through the drip shield and waste package. The seepage abstraction model is stochastic because predictions of seepage are necessarily quite uncertain. The model provides uncertainty distributions for seepage fraction fraction of waste-package locations flow rate as functions of percolation flux. In addition, effects of intermediate-scale flow with seepage and seep channeling are included by means of a flow-focusing factor, which is also represented by an uncertainty distribution.