Design, Evaluation and Experimental Effort Toward Development of a High Strain Composite Wing for Navy Aircraft

This design development effort addressed significant technical issues concerning the use and benefits of high strain composite wing structures (Epsilon(sub ult) = 6000 micro-in/in) for future Navy aircraft. These issues were concerned primarily with the structural integrity and durability of the innovative design concepts and manufacturing techniques which permitted a 50 percent increase in design ultimate strain level (while maintaining the same fiber/resin system) as well as damage tolerance and survivability requirements. An extensive test effort consisting of a progressive series of coupon and major element tests was an integral part of this development effort, and culminated in the design, fabrication and test of a major full-scale wing box component. The successful completion of the tests demonstrated the structural integrity, durability and benefits of the design. Low energy impact testing followed by fatigue cycling verified the damage tolerance concepts incorporated within the structure. Finally, live fire ballistic testing confirmed the survivability of the design. The potential benefits of combining newer/emerging composite materials and new or previously developed high strain wing design to maximize structural efficiency and reduce fabrication costs was the subject of subsequent preliminary design and experimental evaluation effort

Estimating the Mass of the Local Group using Machine Learning Applied to Numerical Simulations

We revisit the estimation of the combined mass of the Milky Way and Andromeda (M31), which dominate the mass of the Local Group. We make use of an ensemble of 30,190 halo pairs from the Small MultiDark simulation, assuming a $\Lambda$CDM (Cosmological Constant with Cold Dark Matter) cosmology, to investigate the relationship between the bound mass and parameters characterising the orbit of the binary and their local environment with the aid of machine learning methods (artificial neural networks, ANN). Results from the ANN are most successful when information about the velocity shear is provided, which demonstrates the flexibility of machine learning to model physical phenomena and readily incorporate new information as it becomes available. The resulting estimate for the Local Group mass, when shear information is included, is $4.9 \times 10^{12} M_\odot$, with an error of $\pm0.8 \times 10^{12} M_\odot$ from the 68% uncertainty in observables, and a 68% confidence interval of $^{+1.3}_{-1.4} \times 10^{12}M_\odot$ from the intrinsic scatter from the differences between the model and simulation masses. We also consider a recently reported large transverse velocity of M31 relative to the Milky Way, and produce an alternative mass estimate of $3.6\pm0.3\pm1.4 \times 10^{12}M_\odot$. Although different methods predict similar values for the most likely mass of the LG, application of ANN compared to the Timing Argument reduces the scatter in the log mass by over half when tested on samples from the simulation.Comment: 20 pages, 6 figures, 5 table

The Language of Architecture

A video of Daniel Libeskind\u27s lecture to the University of New Mexico\u27s School of Architecture and Planning, November 7, 2013. Event sponsored by the School of Architecture and Planning, the Anderson School of Management, the International Studies Institute, and the Office of the Provost. Lecture video (streaming only) and two posters