Real‐time distributed hybrid testing: coupling geographically distributed scientific equipment across the Internet to extend seismic testing capabilities |
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| Authors: | Martin S Williams Matt S Dietz Anthony Blakeborough Ignacio Lamata Martínez |
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| Institution: | 1. Department of Engineering Science, University of Oxford, Oxford, UK;2. Department of Civil Engineering, University of Bristol, Bristol, UK |
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| Abstract: | Large‐scale testing and qualification of structural systems and their components is crucial for the development of earthquake engineering knowledge and practice. However, laboratory capacity is often limited when attempting larger experiments due to the sheer size of the structures involved. To overcome traditional laboratory capacity limitations, we present a new earthquake engineering testing method: real‐time distributed hybrid testing. Extending current approaches, the technique enables geographically distributed scientific equipment including controllers, dynamic actuators and sensors to be coupled across the Internet in real‐time. As a result, hybrid structural emulations consisting of physical and numerical substructures need no longer be limited to a single laboratory. Larger experiments may distribute substructures across laboratories located in different cities whilst maintaining correct dynamic coupling, required to accurately capture physical rate effects. The various aspects of the distributed testing environment have been considered. In particular, to ensure accurate control across an environment not designed for real‐time testing, new higher level control protocols are introduced acting over an optimised communication system. New large time‐step prediction algorithms are used, capable of overcoming both local actuation and distributed system delays. An overview of the architecture and algorithms developed is presented together with results demonstrating a number of current capabilities. Copyright © 2013 John Wiley & Sons, Ltd. |
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| Keywords: | distributed dynamic hybrid testing Internet prediction real time |
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