Fiber-Optic Network Architectures for Onboard Avionics Applications Investigated

This project is part of a study within the Advanced Air Transportation Technologies program undertaken at the NASA Glenn Research Center. The main focus of the program is the improvement of air transportation, with particular emphasis on air transportation safety. Current and future advances in digital data communications between an aircraft and the outside world will require high-bandwidth onboard communication networks. Radiofrequency (RF) systems, with their interconnection network based on coaxial cables and waveguides, increase the complexity of communication systems onboard modern civil and military aircraft with respect to weight, power consumption, and safety. In addition, safety and reliability concerns from electromagnetic interference between the RF components embedded in these communication systems exist. A simple, reliable, and lightweight network that is free from the effects of electromagnetic interference and capable of supporting the broadband communications needs of future onboard digital avionics systems cannot be easily implemented using existing coaxial cable-based systems. Fiber-optical communication systems can meet all these challenges of modern avionics applications in an efficient, cost-effective manner. The objective of this project is to present a number of optical network architectures for onboard RF signal distribution. Because of the emergence of a number of digital avionics devices requiring high-bandwidth connectivity, fiber-optic RF networks onboard modern aircraft will play a vital role in ensuring a low-noise, highly reliable RF communication system. Two approaches are being used for network architectures for aircraft onboard fiber-optic distribution systems: a hybrid RF-optical network and an all-optical wavelength division multiplexing (WDM) network

The viscosity radius in dilute polymer solutions: Universal behaviour from DNA rheology and Brownian dynamics simulations

The swelling of the viscosity radius, $\alpha_\eta$, and the universal viscosity ratio, $U_{\eta R}$, have been determined experimentally for linear DNA molecules in dilute solutions with excess salt, and numerically by Brownian dynamics simulations, as a function of the solvent quality. In the latter instance, asymptotic parameter free predictions have been obtained by extrapolating simulation data for finite chains to the long chain limit. Experiments and simulations show a universal crossover for $\alpha_\eta$ and $U_{\eta R}$ from $\theta$ to good solvents in line with earlier observations on synthetic polymer-solvent systems. The significant difference between the swelling of the dynamic viscosity radius from the observed swelling of the static radius of gyration, is shown to arise from the presence of hydrodynamic interactions in the non-draining limit. Simulated values of $\alpha_\eta$ and $U_{\eta R}$ are in good agreement with experimental measurements in synthetic polymer solutions reported previously, and with the measurements in linear DNA solutions reported here.Comment: 19 pages, 14 figures, two column, Supporting Information added, to appear in Macromolecule

Parallel-Sparse Symmetrical/Unsymmetrical Finite Element Domain Decomposition Solver with Multi-Point Constraints for Structural/Acoustic Analysis

Details of parallel-sparse Domain Decomposition (DD) with multi-point constraints (MPC) formulation are explained. Major computational components of the DD formulation are identified. Critical roles of parallel (direct) sparse and iterative solvers with MPC are discussed within the framework of DD formulation. Both symmetrical and unsymmetrical system of simultaneous linear equations (SLE) can be handled by the developed DD formulation. For symmetrical SLE, option for imposing MPC equations is also provided. Large-scale (up to 25 million unknowns involving complex numbers) structural and acoustic Finite Element (FE) analysis are used to evaluate the parallel computational performance of the proposed DD implementation using different parallel computer platforms. Numerical examples show that the authors\u27 MPI/FORTRAN code is significantly faster than the commercial parallel sparse solver. Furthermore, the developed software can also conveniently and efficiently solve large SLE with MPCs, a feature not available in almost all commercial parallel sparse solvers