Aircraft design at the Naval Postgraduate School - Tactical waverider/long-range cargo aircraft

Aircraft Design, Systems, and Operations Meeting, 09 August 1993 - 11 August 1993The article of record as published may be located at https://doi.org/10.2514/6.1993-4007The graduate program of the Department of Aeronautics and Astronautics at the Naval Postgraduate School uniquely supports a comprehensive design program in aircraft, spacecraft, missile, helicopter, and engine design. This paper is focused on four aircraft configuration designs proposed by AA 4273 Military Aircraft Design course team members. The AA 4273 course is, in turn, supported by a growing research program to enhance and further develop the methodology of aircraft design. This design effort has received considerable support from the NASA/USRA Advanced Design Program in Aeronautics. Specifically, two design solutions for a long-range,carrier based, tactical, wave-rider configured fighter/interceptor aircraft are reviewed herein, as are two solutions for a global range military transport. Both types of aircraft were developed as a graduate student team response to specific design RFPs

Photon-noise limited sensitivity in titanium nitride kinetic inductance detectors

We demonstrate photon-noise limited performance at sub-millimeter wavelengths in feedhorn-coupled, microwave kinetic inductance detectors (MKIDs) made of a TiN/Ti/TiN trilayer superconducting film, tuned to have a transition temperature of 1.4~K. Micro-machining of the silicon-on-insulator wafer backside creates a quarter-wavelength backshort optimized for efficient coupling at 250~\micron. Using frequency read out and when viewing a variable temperature blackbody source, we measure device noise consistent with photon noise when the incident optical power is $>$~0.5~pW, corresponding to noise equivalent powers $>$~3$\times 10^{-17}$ W/$\sqrt{\mathrm{Hz}}$. This sensitivity makes these devices suitable for broadband photometric applications at these wavelengths

CHARACTERIZING THE YUCCA MOUNTAIN SITE FOR DEVELOPING SEISMIC DESIGN GROUND MOTIONS

Yucca Mountain, Nevada is the designated site for the first long-term geologic repository to safely dispose spent nuclear fuel and high-level nuclear waste in the U.S. Yucca Mountain consists of stacked layers of welded and non-welded volcanic tuffs. Site characterization studies are being performed to assess its future performance as a permanent geologic repository. These studies include the characterization of the shear-wave velocity (Vs) structure of the repository block and the surface facilities area. The Vs data are an input in the calculations of ground motions for the preclosure seismic design and for postclosure performance assessment and therefore their accurate estimation is needed. Three techniques have been employed: 24 downhole surveys, 15 suspension seismic logging surveys and 95 spectral-analysis-of-surface-waves (SASW) surveys have been performed to date at the site. The three data sets were compared with one another and with Vs profiles developed from vertical seismic profiling data collected by the Lawrence Berkeley National Laboratory and with Vs profiles developed independently by the University of Nevada, Reno using the refraction microtremor technique. Based on these data, base case Vs profiles have been developed and used in site response analyses. Since the question of adequate sampling arises in site characterization programs and a correlation between geology and Vs would help address this issue, a possible correlation was evaluated. To assess the influence of different factors on velocity, statistical analyses of the Vs data were performed using the method of multi-factor Analysis of Variance (ANOVA). The results of this analysis suggest that the effect of each of three factors, depth, lithologic unit, and spatial location, on velocity is statistically significant. Furthermore, velocity variation with depth is different at different spatial locations: Preliminary results show that the lithologic unit alone explains about 54% and 42% of the velocity variation in the suspension and downhole data sets, respectively. The three factors together explain about 73% and 81% of the velocity variation in the suspension and downhole data sets, respectively. Development of a relationship, using multiple regression analysis, which may be used as a predictive tool to estimate velocity at a new location, is currently being examined