The Economics Of System Integration: Toward An Evolutionary Interpretation.

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The response of turbine engine rotors to interference rubs

A method was developed for the direct integration of a rotor dynamics system experiencing a blade loss induced rotor rub. Both blade loss and rotor rub were simulated on a rotor typical of a small gas turbine. A small change in the coefficient of friction (from 0.1 to 0.2) caused the rotor to change from forward to backward whirl and to theoretically destroy itself in a few rotations. This method provides an analytical capability to study the susceptibility of rotors to rub induced backward whirl problems

Symplectic integrators for spin systems

We present a symplectic integrator, based on the canonical midpoint rule, for classical spin systems in which each spin is a unit vector in $\mathbb{R}^3$. Unlike splitting methods, it is defined for all Hamiltonians, and is $O(3)$-equivariant. It is a rare example of a generating function for symplectic maps of a noncanonical phase space. It yields an integrable discretization of the reduced motion of a free rigid body

NLSEmagic: Nonlinear Schr\"odinger Equation Multidimensional Matlab-based GPU-accelerated Integrators using Compact High-order Schemes

We present a simple to use, yet powerful code package called NLSEmagic to numerically integrate the nonlinear Schr\"odinger equation in one, two, and three dimensions. NLSEmagic is a high-order finite-difference code package which utilizes graphic processing unit (GPU) parallel architectures. The codes running on the GPU are many times faster than their serial counterparts, and are much cheaper to run than on standard parallel clusters. The codes are developed with usability and portability in mind, and therefore are written to interface with MATLAB utilizing custom GPU-enabled C codes with the MEX-compiler interface. The packages are freely distributed, including user manuals and set-up files.Comment: 37 pages, 13 figure

Julia Programming Language Benchmark Using a Flight Simulation

Julias goal to provide scripting language ease-of-coding with compiled language speed is explored. The runtime speed of the relatively new Julia programming language is assessed against other commonly used languages including Python, Java, and C++. An industry-standard missile and rocket simulation, coded in multiple languages, was used as a test bench for runtime speed. All language versions of the simulation, including Julia, were coded to a highly-developed object-oriented simulation architecture tailored specifically for time-domain flight simulation. A speed-of-coding second-dimension is plotted against runtime for each language to portray a space that characterizes Julias scripting language efficiencies in the context of the other languages. With caveats, Julia runtime speed was found to be in the class of compiled or semi-compiled languages. However, some factors that affect runtime speed at the cost of ease-of-coding are shown. Julias built-in functionality for multi-core processing is briefly examined as a means for obtaining even faster runtime speed. The major contribution of this research to the extensive language benchmarking body-of-work is comparing Julia to other mainstream languages using a complex flight simulation as opposed to benchmarking with single algorithms