2019
DOI: 10.2514/1.c035329
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Prediction of Preliminary Maximum Wing Bending Moments Under Discrete Gust

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Cited by 3 publications
(3 citation statements)
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“…Even though different gust prediction methods were developed in recent years to Different methodologies have been proposed in recent years to quickly identify worst-case gust load scenarios [50,51]. However, as the focus of the present work is on gust loads at design cruise inflow conditions, the number of required simulations to predict the critical gust scenario is manageable.…”
Section: A Prestudy -Critical Gust Loadsmentioning
confidence: 99%
“…Even though different gust prediction methods were developed in recent years to Different methodologies have been proposed in recent years to quickly identify worst-case gust load scenarios [50,51]. However, as the focus of the present work is on gust loads at design cruise inflow conditions, the number of required simulations to predict the critical gust scenario is manageable.…”
Section: A Prestudy -Critical Gust Loadsmentioning
confidence: 99%
“…Cook et al (2017) investigated the effects of aircraft flexibility on 1 g and gust loads based on an intrinsic beam theory to understand the effects of large geometrically nonlinear deformations on the loads due to level fight and atmospheric turbulence in the form of 1-cosine gusts. Fournier et al (2019) presented a reliable and fast methodology for estimating critical load cases for weight-variant aircrafts at the preliminary design phase. After identifying the set of preliminary parameters, the proposed methodology used the orthogonal greedy algorithm (OGA) to identify the coefficient of the second-order polynomial response surfaces from an initial database aircraft, which included the weight-variant aircraft.…”
Section: Introductionmentioning
confidence: 99%
“…The number of simulations to build a meta-model is still high, but negligible compared to the case without such a method. Other applications of meta-models are found in Fournier et al (2019) applying a second-order polynomial expansion or in Nazzeri et al (2015) using a meta-model relying on artificial neural networks.…”
Section: Introductionmentioning
confidence: 99%