A robustness analysis of the bonding process of joints in wind turbine blades

Spangenberg, Jon; Uzal, Anil; Nielsen, Michael Wenani; Hattel, Jesper Henri

doi:10.1016/j.ijadhadh.2018.06.009

Cited by 7 publications

(6 citation statements)

References 20 publications

(21 reference statements)

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“…It is possible then to measure the spreading of the adhesive during bonding from top view (Figure 12), and to observe the qualitative 3D shape during and after the bonding process. If a squeeze out pudding with a cylindrical shape has been observed during bonding as can be found in the literature [54] , the final shape of the adhesive at the Plexiglas boundary is a meniscus. This is due to the high deformability of the non-cured EC2216 which does not keep its round shape, immediately obtained during bonding because of its incompressibility.…”

Section: Experimental Testingmentioning

confidence: 90%

“…The analytical method is used for simple geometries (flat plates, for example) and non-complex fluid behaviour (without dependency on other parameters, for example) [11,23,29,32,34] . In the majority of cases, an analytic method cannot solve the question and the numerical simulation is applied [28,45,46,54,63,68] .…”

Section: Finite Volume Methods (Fvm)mentioning

confidence: 99%

“…As shown in [28] , the cover surface and overflowing adhesive are important variables in cases where thickness is fixed. In [28,54] the authors took an interest in validating the final form after the assembly process, including the surface covered and overflowing adhesive using Computational Fluid Dynamics (CFD). In the case presented in, [55] the authors study the adhesive flow, but here to study the impact of the deposition process with the application of two adhesive cords.…”

Section: Interest In Simulating the Adhesive Squeezementioning

confidence: 99%

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Numerical simulations of adhesive spreading during bonding-induced squeeze

Silva

Espinosa

Paroissien

et al. 2021

The Journal of Adhesion

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The current work is intended to give an overview of issues related to the numerical simulation of adhesive spreading for liquid to semi-liquid adhesives. The advantages and limitations are presented in order to guide the choice of the suitable approach depending on the case under consideration. It is shown that methods are of two categories, whether they are grid-based or meshless. In the first, the movement of the matter is directly dependent on the mesh size and distribution. Contrariwise, in the meshfree methods, the particles are free to move and each carries its properties. Besides, cases of application are presented to provide a database for calculating adhesive spreading with the particulate SPH method. It is shown that it is possible to use simple behaviour laws to win this case. KEYWORDSSpreading < methods of analysis; rheology < methods of analysis; numerical analysis < methods of analysis; bonding < applications; SPH < methods of analysis; fluid mechanics < methods of analysis CONTACT Lorraine Aparecida Silva

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Section: Experimental Testingmentioning

confidence: 90%

Section: Finite Volume Methods (Fvm)mentioning

confidence: 99%

Section: Interest In Simulating the Adhesive Squeezementioning

confidence: 99%

See 1 more Smart Citation

Numerical simulations of adhesive spreading during bonding-induced squeeze

Silva

Espinosa

Paroissien

et al. 2021

The Journal of Adhesion

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“…In complement to experimental studies, numerical simulations can deliver a vast amount of information about the process and have proven to be a predictive tool in the analysis of different manufacturing methods [36][37][38][39]. In the context of material extrusion AM, the extrusion flow has been investigated using two-dimensional [40][41][42], and three-dimensional Computational Fluid Dynamics (CFD) simulations [43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Numerical simulations of the mesostructure formation in material extrusion additive manufacturing

Serdeczny

Comminal

Pedersen

et al. 2019

Additive Manufacturing

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A computational fluid dynamics model is used to predict the mesostructure formed by the successive deposition of parallel strands in material extrusion additive manufacturing. The numerical model simulates the extrusion of the material onto the substrate. The model takes into account the effect of the presence of the previously extruded material on the shape of the subsequently deposited strands. The simulated mesostructures are compared to optical micrographs of the mesostructures of 3D-printed samples, and the predictions agree well with the experiments. In addition, the influence of the layer thickness, the strand-to-strand distance, and the deposition configuration (with aligned or skewed layers) on the formation of the mesostructure is investigated. The simulations provide detailed information about the porosity, the inter-and intra-layer bond line densities, and the surface roughness of the mesostructures, which potentially can be used in a model-based slicing software.

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“…In the case where the blade performance metrics are too sensitive to the design parameters or their robustness is relatively weak, a slight fluctuation in design parameters may lead to a significant drop in the blade performance metrics. [16][17][18][19] This selection of an appropriate optimization algorithm is challenging for composite structural design problem that contains plenty of variables.…”

Section: Introductionmentioning

confidence: 99%

Robust optimization for composite blade of wind turbine based on kriging model

Yan

Wei

et al. 2020

Advanced Composites Letters

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Structural optimization models often feature many uncertain factors, which can be handled by robust optimization. This work presents a complete robust optimization program for composite blade based on the kriging approximation model. Two case studies were given and performed using a genetic algorithm. The first being typical optimization, where the first natural frequency of the blade is selected as the optimized objective and the optimal sizing distribution for the entire blade shell is sought to ignore the uncertain factors. The other case determines the standard deviation of the optimized objective in the first case as another optimization goal. Moreover, a 6 σ robustness for the optimization results of the two cases was evaluated. The result shows that typical optimization increases the first natural frequency of the blade by 19%, while its robustness level has a reduction of 61% compared with the first blade. Nevertheless, the robust optimization not only results in an increment of 15.4% in the first natural frequency of the blade but also increases its robustness level by up to 90%. Therefore, the proposed approach can effectively improve optimization objectives, especially reduce the impacts of uncertainties on the objective functions.

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A robustness analysis of the bonding process of joints in wind turbine blades

Cited by 7 publications

References 20 publications

Numerical simulations of adhesive spreading during bonding-induced squeeze

Numerical simulations of adhesive spreading during bonding-induced squeeze

Numerical simulations of the mesostructure formation in material extrusion additive manufacturing

Robust optimization for composite blade of wind turbine based on kriging model

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