Methodologies for tracking of load extremes and error estimation using probabilistic techniques

R. Flores, E. Ortega, R. López

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Methodologies for tracking of load extremes and error estimation using probabilistic techniques

R. Flores, E. Ortega, R. López

Abstract

This work, conducted at CIMNE under ALEF project task 1.2.3, presents an investigation
about the potential capabilities of neural networks to assist simulation campaigns. The
discrete gust response of an aircraft has been chosen as a typical problem in which the
determination of the critical loads requires exploring a large parameter space.

A very simple model has been used to compute the aerodynamic loads. This allows creating
a large database while at the same time retaining some of the fundamental properties of the
problem. Using this comprehensive dataset the effects of network structure, training method
and sampling strategy on the level of approximation over the complete domain have been
investigated. The capabilities of the neural network to predict the peak load as well as the
critical values of the design parameters have also been assessed. The applicability of neural
networks to the combination of multi-fidelity results is also explored.

How to cite

R. Flores, E. Ortega and R. López, Methodologies for tracking of load extremes and error estimation using probabilistic techniques, (2012). Research Report, Nº PI375 URL https://www.scipedia.com/public/Flores_et_al_2018a

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