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Item type:Publication, Access status: without files , A variable-fidelity hybrid surrogate approach for quantifying uncertainties in the nonlinear response of braided compositesThe ultimate strength prediction of textile composite materials requires high-fidelity FE modeling with information-passing multiscale schemes and damage initiation and propagation algorithms. The numerical demand of this procedure together with the complexity of the observed response surface, hampers the quantification of uncertainties contributing to the scatter of strength values. This study proposes a surrogate methodology able to efficiently emulate the nonlinear multiscale procedure, based on a combination of artificial neural networks and Kriging modeling under a variable-fidelity framework. A triaxially braided textile under longitudinal tension is used as a use-case and the methodology is employed to identify the most critical parameters in terms of variance via a global sensitivity analysis technique. Results show strong interaction effects between the uncertain parameters. The approach is non-intrusive and can be easily extended to other types of textiles and load cases.Publicationtype: Journal Article99 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Stochastic modeling techniques for textile yarn distortion and waviness with 1D random fieldsWithin the concept of simulation approaches for manufacturing-induced imperfections of composite structures, this work proposes modeling frameworks for the consideration of stochastic deviations concerning the yarns of textile composite materials. The random distortion of a yarn's cross-section, is addressed by flexible 1D Fourier-based random fields, with the potential to be calibated from measurements of the deviations from the nominal yarn shape and their statistical characteristics. Furthermore, a Kriging-based modeling approach is presented, able to randomize any nominal yarn path in short or long range problems, considering data for the correlation and variance in a straightforward manner. The effects of defects due to stochastic yarn distortion and waviness, are investigated by simulating a forward uncertainty propagation problem of a triaxially braided composite material. The response variability concerning stiffness and strength for different uncertainty levels is highlighted, while several comments are offered regarding numerical issues and potential surrogate modeling techniques.Publicationtype: Journal Article151 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Metamodel-Based Uncertainty Quantification for the Mechanical Behavior of Braided CompositesThis chapter presents an uncertainty quantification framework for triaxially braided composites simulation, dealing with the stochastic stiffness prediction via numerical multiscale analysis. Efficiency is achieved by using various metamodeling techniques, such as neural networks, polynomial chaos expansion and Kriging modeling. Uncertainties accounting for material and geometric randomness are propagating through the scales to the final scatter of the mechanical properties of the macroscale. Information about the stochastic input and the dominating uncertain parameters is offered via application of a variance-based global sensitivity analysis. All methods employed in this work are non-intrusive, hence the framework can be used for all sorts of composite materials and numerical models. The need for realistic uncertainty quantification is highlighted.Publicationtype: Book Part113 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Stochastic Strength Prediction of Braided Composites: Multiscale Damage Modeling and Uncertainty QuantificationPublicationtype: Conference Presentation40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Computational Modeling for the Stochastic Yarn Variability of Braided Composites Due to Manufacturing ProcessesThe several manufacturing stages a braided textile must go through until its completion, append random material defects in terms of geometry. These uncertainties are not only difficult to predict, but also hard to be minimized, and since they mostly occur in lower scales, they significantly affect the response of materials with such a heterogeneous nature. Realistic numerical, multiscale models should account for the variability of their representative volume elements, thus have the ability to include spatial randomness.Publicationtype: Conference Presentation51 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Metamodel-based uncertainty quantification for braided composites with manufacturing considerationsStochastic analysis in engineering sciences takes into account the uncertainties that may exist and affect a certain physical system in an a priori unknown manner. As the design of structures gets increasingly complex over the years, the impact of those uncertainties onto the system response has to be studied in order to implement numerical procedures for virtual testing platforms.Publicationtype: Conference Presentation44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertaintyThe stiffness prediction of textile composites has been studied intensively over the last 20 years. It is the complex yarn architecture that adds exceptional properties but also requires computationally expensive methods for the accurate solution of the homogenization problem. Braided composites are of special interest for the aerospace and automotive industry and have thus drawn the attention of many researchers, studying and developing analytical and numerical methods for the extraction of the effective elastic properties. This paper intends to study the effect of uncertainties caused by the automated manufacturing procedure, to the elastic behavior of braided composites. In this direction, a fast FEM-based multiscale algorithm is proposed, allowing for uncertainty introduction and response variability calculation of the macro-scale properties of 3D braided composites, within a Monte Carlo framework. Artificial neural networks are used to reduce the computational effort even more, since they allow for rapid generation of large samples when trained. With this approach it is feasible to apply a variance-based global sensitivity analysis in order to identify the most crucial uncertain parameters through the costly Sobol indices. The proposed method is straightforward, quite accurate and highlights the importance of realistic uncertainty quantification.Publicationtype: Journal Article140