Fig 22 - uploaded by Chenglong Wang
Content may be subject to copyright.
Source publication
This paper addresses some practical aspects of making Isogeometric Analysis (IGA) more accessible to design engineers and analysts. An interactive parametric design-through-analysis platform is proposed to help design engineers and analysts make more effective use of IGA to improve their product design and performance. We develop several Rhino 3D p...
Similar publications
Vertical-axis wind turbines (VAWTs) are compact and efficient and have become increasingly popular for wind energy harvesting. This paper mainly focuses on free and forced vibration analysis of two different types of VAWTs, i.e., an H-type VAWT and a new hybrid VAWT. The H-type VAWT has a lower cost, while the hybrid VAWT has a better self-starting...
Citations
... This includes Geometry Independent Field approximation (GIFT), which is a generalisation of isogeometric analysis that allows different spaces for the parameterisation of the computational domain, as well as the approximation of the solution field [58][59][60][61], shape and topology optimisation [62][63][64][65][66], shell and plate structures [67][68][69][70][71][72][73][74][75], isogeometric boundary element methods [76][77][78][79], and fast reanalysis for structural modification [80][81][82][83]. The interested readers are referred to in [84][85][86] for detailed reviews and open-source implementations of IGA [87,88]. ...
This work models spatially uncorrelated (independent) load uncertainty and develops a reduced-order Monte Carlo stochastic isogeometric method to quantify the effect of the load uncertainty on the structural response of thin shells and solid structures. The approach is tested on two demonstrative applications of uncertainty, namely, spatially uncorrelated loading, with (1) Scordelis–Lo Roof shell structure, and (2) a 3D wind turbine blade. This work has three novelties. Firstly, the research models spatially uncorrelated (independent) load uncertainties (including both their magnitude and/or direction) using stochastic analysis. Secondly, the paper advances a reduced-order Monte Carlo stochastic isogeometric method to quantify the spatially uncorrelated load uncertainty. It inherits the merits of isogeometric analysis, which enables the precise representation of geometry and alleviates shell shear locking, thereby reducing the model’s uncertainties. Moreover, the method retains the generality and accuracy of classical Monte Carlo simulation (MCS), with significant efficiency gains. The demonstrative results suggest that there is a cost, which is 3% of the time used by the standard MCS. Furthermore, a significant observation is made from the conducted numerical tests. It is noticed that the standard deviation of the output (i.e., displacement) is strongly influenced when the load uncertainty is spatially uncorrelated. Namely, the standard derivation (SD) of the output is roughly 10 times smaller than the SD for correlated load uncertainties. Nonetheless, the expected values remain consistent between the two cases.
