Numerical computations on one-dimensional inverse scattering problems

An approximate method to determine the index of refraction of a dielectric obstacle is presented. For simplicity one dimensional models of electromagnetic scattering are treated. The governing equations yield a second order boundary value problem, in which the index of refraction appears as a functional parameter. The availability of reflection coefficients yield two additional boundary conditions. The index of refraction by a k-th order spline which can be written as a linear combination of B-splines is approximated. For N distinct reflection coefficients, the resulting N boundary value problems yield a system of N nonlinear equations in N unknowns which are the coefficients of the B-splines

Advanced turboprop noise prediction: Development of a code at NASA Langley based on recent theoretical results

The development of a high speed propeller noise prediction code at Langley Research Center is described. The code utilizes two recent acoustic formulations in the time domain for subsonic and supersonic sources. The structure and capabilities of the code are discussed. Grid size study for accuracy and speed of execution on a computer is also presented. The code is tested against an earlier Langley code. Considerable increase in accuracy and speed of execution are observed. Some examples of noise prediction of a high speed propeller for which acoustic test data are available are given. A brisk derivation of formulations used is given in an appendix

Atom-Molecule Laser Fed by Stimulated Three-Body Recombination

Using three-body recombination as the underlying process, we propose a method of coherently driving an atomic Bose-Einstein condensate (BEC) into a molecular BEC. Superradiant-like stimulation favors atom-to-molecule transitions when two atomic BECs collide at a resonant kinetic energy, the result being two molecular BEC clouds moving with well defined velocities. Potential applications include the construction of a molecule laser.Comment: 4 pgs, 3 figs, RevTeX4, submitted to PRL; Corrected numerical example

Cool-season grass cultivars for athletic fields (2001)

New 7/93, Revised 12/01/5M

Managing Pastures During and After Drought

Drought provides dramatic evidence of the role of short- and long-term management of native rangeland and tame pastures. Proper management of range and pasture resources during drought is critical for sustainable livestock production and centers on one key strategy: reducing stocking rate

Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database

Robust infrastructure networks are vital to ensure community resilience; their failure leads to severe societal disruption and they have important postdisaster functions. However, as these networks consist of interconnected, but geographically-distributed, components, system resilience is difficult to assess. In this paper the authors propose the use of an extension to the catastrophe (CAT) risk modeling approach, which is primarily used to perform risk assessments of independent assets, to be adopted for these interdependent systems. To help to achieve this, fragility curves, a crucial element of CAT models, are developed for overhead electrical lines using an empirical approach to ascribe likely failures due to wind storm hazard. To generate empirical fragility curves for electrical overhead lines, a dataset of over 12,000 electrical failures is coupled to a European reanalysis (ERA) wind storm model, ERA-Interim. The authors consider how the spatial resolution of the electrical fault data affects these curves, generating a fragility curve with low resolution fault data with a R2R2 value of 0.9271 and improving this to a R2R2 value of 0.9889 using higher spatial resolution data. Recommendations for deriving similar fragility curves for other infrastructure systems and/or hazards using the same methodological approach are also made. The authors argue that the developed fragility curves are applicable to other regions with similar electrical infrastructure and wind speeds, although some additional calibration may be required

Working With the Wave Equation in Aeroacoustics: The Pleasures of Generalized Functions

The theme of this paper is the applications of generalized function (GF) theory to the wave equation in aeroacoustics. We start with a tutorial on GFs with particular emphasis on viewing functions as continuous linear functionals. We next define operations on GFs. The operation of interest to us in this paper is generalized differentiation. We give many applications of generalized differentiation, particularly for the wave equation. We discuss the use of GFs in finding Green s function and some subtleties that only GF theory can clarify without ambiguities. We show how the knowledge of the Green s function of an operator L in a given domain D can allow us to solve a whole range of problems with operator L for domains situated within D by the imbedding method. We will show how we can use the imbedding method to find the Kirchhoff formulas for stationary and moving surfaces with ease and elegance without the use of the four-dimensional Green s theorem, which is commonly done. Other subjects covered are why the derivatives in conservation laws should be viewed as generalized derivatives and what are the consequences of doing this. In particular we show how we can imbed a problem in a larger domain for the identical differential equation for which the Green s function is known. The primary purpose of this paper is to convince the readers that GF theory is absolutely essential in aeroacoustics because of its powerful operational properties. Furthermore, learning the subject and using it can be fun

Testing the Foundations of Signal Detection Theory in Recognition Memory

Signal detection theory (SDT) plays a central role in the characterization of human judgments in a wide range of domains, most prominently in recognition memory. But despite its success, many of its fundamental properties are often misunderstood, especially when it comes to its testability. The present work examines five main properties that are characteristic of existing SDT models of recognition memory: (a) random-scale representation, (b) latent-variable independence, (c) likelihood-ratio monotonicity, (d) ROC function asymmetry, and (e) nonthreshold representation. In each case, we establish testable consequences and test them against data collected in the appropriately designed recognition-memory experiment. We also discuss the connection between yes–no, forced-choice, and ranking judgments. This connection introduces additional behavioral constraints and yields an alternative method of reconstructing yes–no ROC functions. Overall, the reported results provide a strong empirical foundation for SDT modeling in recognition memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved