Determining the uncertainty in a mechanical joint is very important and very difficult. This paper presents two methods of determining the uncertainty in the joint: maximum entropy approach and sampling methods. Maximum entropy is an approach that can quantify the aleatoric and epistemic uncertainty independently. This approach is applied on a rigid connection of the Ampair 600 Wind Turbine and shows that the epistemic uncertainty of the system is very high. Sampling methods are used on an simplified representation of the wind turbine as a lumped mass approximation. The sampling methods are able to treat the joint in a nonlinear sense by using a discrete four-parameter Iwan model as the joint model. This is able to predict accurately the data within the uncertainty bounds when considering epistemic uncertainty. The Iwan joint model is then implemented on the high fidelity model and preliminary results are presented

Quantifying Epistemic and Aleatoric Uncertainty in the Ampair 600 Wind Turbine / Robertson, Brett A.; Bonney, Matthew S.; Gastaldi, Chiara; Brake, Matthew R. W.. - ELETTRONICO. - 4:(2015), pp. 125-138. ((Intervento presentato al convegno 33rd IMAC, A Conference and Exposition on Structural Dynamics tenutosi a Orlando (USA) nel February 2-5, 2015 [10.1007/978-3-319-15209-7_12].

Quantifying Epistemic and Aleatoric Uncertainty in the Ampair 600 Wind Turbine

GASTALDI, CHIARA;
2015

Abstract

Determining the uncertainty in a mechanical joint is very important and very difficult. This paper presents two methods of determining the uncertainty in the joint: maximum entropy approach and sampling methods. Maximum entropy is an approach that can quantify the aleatoric and epistemic uncertainty independently. This approach is applied on a rigid connection of the Ampair 600 Wind Turbine and shows that the epistemic uncertainty of the system is very high. Sampling methods are used on an simplified representation of the wind turbine as a lumped mass approximation. The sampling methods are able to treat the joint in a nonlinear sense by using a discrete four-parameter Iwan model as the joint model. This is able to predict accurately the data within the uncertainty bounds when considering epistemic uncertainty. The Iwan joint model is then implemented on the high fidelity model and preliminary results are presented
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2644456
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