2017
DOI: 10.1007/s00707-017-2049-3
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Shape optimization under uncertainty of morphing airfoils

Abstract: This paper is devoted to the formulation of a novel optimization under uncertainty framework for the definition of optimal shapes for morphing airfoils, applied here to advancing/retreating 2D airfoils. In particular, the morphing strategy is conceived with the intent of changing the shape at a given frequency to enhance aerodynamic performance. The optimization of morphing airfoils presented here only takes into account the aerodynamic performance. The paper is then focused on an aerodynamic optimization to s… Show more

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Cited by 11 publications
(5 citation statements)
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“…This would at least ensure the presence of the deterministic airfoil in the robust population (as long as it satisfies the constraints). This last strategy appears more straightforward, and the results of its application are reported in another paper [48].…”
Section: Comparison With Robust Optimizationmentioning
confidence: 92%
“…This would at least ensure the presence of the deterministic airfoil in the robust population (as long as it satisfies the constraints). This last strategy appears more straightforward, and the results of its application are reported in another paper [48].…”
Section: Comparison With Robust Optimizationmentioning
confidence: 92%
“…On the other hand, robust optimization considers the impact of uncertainties directly on the objective and constraints through their second-order statistics (Padula et al 2006), i.e., variance or standard deviation. In aerodynamic shape optimization (ASO) under uncertainty, the statistical moments and their gradients are typically estimated by means of Monte Carlo sampling (Vasile and Quagliarella 2020), genetic algorithms (Fusi et al 2018), polynomial chaos expansion (Keshavarzzadeh et al 2016;Keshavarzzadeh and Ghanem 2019;Mishra et al 2020), and stochastic collocation (Petrone et al 2011;Lazarov et al 2012). On the one hand, Monte Carlo strategies are applicable to high-dimensional inputs as the convergence rate of the estimator is independent of the number of input variables, but are computationally expensive as the convergence decays with the number of samples as O(N −1∕2 ) ; relatively higher performances can be obtained with genetic algorithms at expenses of algorithmic/implementation complexities.…”
Section: Introductionmentioning
confidence: 99%
“…Burner et al [1] supplemented uncertainty analyses with assessments of experimental errors, and the utilization of experimental methods with modern designs, which helped explore the influence of various uncertainties, such as sensors and state variables on the measurement accuracy of model pitch angle in wind tunnel tests. Some academics [2][3][4][5] focused on the influence of shape deviation on aerodynamic performance. Some recent studies have shown that deterministic designs are easily influenced by uncertainties in certain geometric variables or environmental parameters [6].…”
Section: Introductionmentioning
confidence: 99%