In-service transient load measurement on a large composite panel

This source preferred by Mehran Koohgilani and Philip Sewell

Authors: Reza Ramazani, M., Sewell, P., Noroozi, S., Koohgilani, M. and Cripps, B.

Journal: Applied Mechanics and Materials

Volume: 248

Pages: 204-211

DOI: 10.4028/www.scientific.net/AMM.248.204

Current practices to estimate the pressure loads on the hull of small high-speed craft in a seaway are based on determination of the wave loads by applying rules and standards which itself relies either on often conservative methods, leading to a craft that is heavier and slower than it could be otherwise. There are rather large uncertainties in the wave load predictions for ships mainly caused by not necessarily sufficient theoretical basis of the calculation methods. Direct pressure measurement techniques can only provide data at each transducer location and classical analytical techniques require a large amount of experimental data to be collected to relate pressure to the structures response. The evaluation of wave generated hydrodynamic loads is less reliable as the dynamic nature of the loading as well as transient effects such as slamming and green water on deck still demands more investigations. Therefore, a novel technique is required to overcome these limitations by providing a method of measuring the pressure load with relatively few sensors and minimal data collection. This paper reports on research undertaken to develop an inverse problem approach utilising an Artificial Neural Network (ANN) for quantification of in-service, transient loads in real-time acting on the craft from the craft’s structural response (strain response to load). This study investigates suitability and performance of utilising ANN as an inverse problem approach to estimate impact loads applied to up to 13 locations on the structure in real-time from 16 strain measurements.

This data was imported from Scopus:

Authors: Ramazani, M.R., Sewell, P., Noroozi, S., Koohgilani, M. and Cripps, B.

Journal: Applied Mechanics and Materials

Volume: 248

Pages: 204-211

eISSN: 1662-7482

ISSN: 1660-9336

DOI: 10.4028/www.scientific.net/AMM.248.204

Current practices to estimate the pressure loads on the hull of small high-speed craft in a seaway are based on determination of the wave loads by applying rules and standards which itself relies either on often conservative methods, leading to a craft that is heavier and slower than it could be otherwise. There are rather large uncertainties in the wave load predictions for ships mainly caused by not necessarily sufficient theoretical basis of the calculation methods. Direct pressure measurement techniques can only provide data at each transducer location and classical analytical techniques require a large amount of experimental data to be collected to relate pressure to the structures response. The evaluation of wave generated hydrodynamic loads is less reliable as the dynamic nature of the loading as well as transient effects such as slamming and green water on deck still demands more investigations. Therefore, a novel technique is required to overcome these limitations by providing a method of measuring the pressure load with relatively few sensors and minimal data collection. This paper reports on research undertaken to develop an inverse problem approach utilising an Artificial Neural Network (ANN) for quantification of in-service, transient loads in real-time acting on the craft from the craft's structural response (strain response to load). This study investigates suitability and performance of utilising ANN as an inverse problem approach to estimate impact loads applied to up to 13 locations on the structure in real-time from 16 strain measurements. © (2013) Trans Tech Publications, Switzerland.

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