<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; mso-layout-grid-align: none;" align="left"><span style="mso-bidi-font-weight: bold;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">This paper presents a distributed software system for a wireless sensor network application that remotely monitors the effects of hurricane winds on manmade structures. The software system is divided into three independent segments that are distributed across the Internet to provide real-time collection and transmission of data between wireless remote sensor units and a centralized server. The software system uses a custom-designed communications architecture that is built upon existing wireless networking standards (IEEE 802.11 and HSPA) and that benefit from capabilities of Microsoft .NET development framework. By segmenting the software and separating application-specific code from the communications architecture, the software can be reused and applied towards a wide variety of wireless sensor networks operating in harsh environments. The system is currently under test and will be deployed for the 2009 hurricane season. <em></em></span></span></span></p>
Rotational machinery such as horizontal axis wind turbines exhibits complex and nonlinear dynamics (e.g. precession and Coriolis effects, torsional coupling) and is subjected to nonlinear constrained conditions (i.e. aeroelastic interaction). For those reasons, aeroelastic and computer-aided models reproduced under controlled conditions may fail to predict the correct non-stationary loading and resistance patterns of wind turbines in actual operation. Operational techniques for extracting modal properties under actual non-stationary loadings are needed in order to improve computer-aided elasto-aerodynamic models to better characterize the actual behavior of horizontal axis wind turbines in operational scenarios, monitor and diagnose the system for integrity and damage through time, and optimize control systems. For structural health monitoring applications, model updating of stochastic aerodynamic problems has gained interest over the past decades. A probability theory framework is employed in this study to update a horizontal axis wind turbine model using such a stochastic global optimization approach. Structural identification is addressed under regular wind turbine operation conditions for non-stationary, unmeasured, and uncontrolled excitations by means of stochastic subspace identification techniques. This numerical framework is then coupled with an adaptive simulated annealing numerical engine for solving the problem of model updating. Numerical results are presented for an experimental deployment of a small horizontal axis wind turbine structure.
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