다양한 인공 신경망을 적용한 광대역 스펙트럼의 피로손상 예측
Fatigue Damage Estimation of Wide Band Spectrum Considering Various Artificial Neural Networks
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The fatigue damage caused by wide band loadings has generally been predicted using fatigue damage models in the frequency domain rather than a rain-flow counting method in the time domain because of its computation cost. This study showed that these fatigue damage models can be simplified in the form of normalized fatigue damage as a function of the S-N curve slope and bandwidth parameters. Based on numerical simulations of various wide band spectra, it was found that fatigue damage models in the form of normalized fatigue damage with one S-N curve slope and two bandwidth parameters( α1 , α2 ) provided less reasonable fatigue damage. Therefore, an additional bandwidth parameter needs to be considered based on a sensitivity study using various neural networks, which proved that α1.5 would be the dominant factor of a fatigue damage model as an additional bandwidth parameter.
Abstract
The fatigue damage caused by wide band loadings has generally been predicted using fatigue damage models in the frequency domain rather than a rain-flow counting method in the time domain because of its computation cost. This study showed that these fatigue damage models can be simplified in the form of normalized fatigue damage as a function of the S-N curve slope and bandwidth parameters. Based on numerical simulations of various wide band spectra, it was found that fatigue damage models in the form of normalized fatigue damage with one S-N curve slope and two bandwidth parameters( α1 , α2 ) provided less reasonable fatigue damage. Therefore, an additional bandwidth parameter needs to be considered based on a sensitivity study using various neural networks, which proved that α1.5 would be the dominant factor of a fatigue damage model as an additional bandwidth parameter.
참고문헌 (11)
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