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Aitharaju, Venkat

Publications and source records attributed to Aitharaju, Venkat.

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗

Enhanced ductility in in-layer glass-carbon fiber/epoxy hybrid composites produced via tailored fiber placement

Experimental mechanical property results of composite materials with hybrid reinforcements (commingled glass and carbon fibers) are detailed and compared with single-fiber composite properties. In-layer hybrid materials, with carbon and glass fiber tows laid side-by-side, were produced via tailored fiber placement (TFP) technology. Detailed experiments showed a phenomenon of “enhanced ductility” of carbon fiber in the hybrid composites of 12–24% relative to an all-carbon fiber composite and multiple stress–strain peaks were observed. Here, this enhanced ductility was hypothesized to be due to the glass fibers mitigating the shock waves arising from the initial failures of carbon fibers, and preventing the premature failure of the remaining carbon fibers. A novel way to engineer the stress–strain behavior of a hybrid composite to achieve a metal-like ductile response (plateau of stress–strain behavior, often termed “elastic–plastic deformation”) was demonstrated by carefully selecting the type and composition of carbon and glass fiber materials.

36 MATERIALS SCIENCE↗

Three-Dimensional High Fidelity Progressive Failure Damage Modeling of NCF Composites

Performance prediction of off-axis laminates is of significant interest in designing composite structures for energy absorption. Phenomenological models available in most of the commercial programs, where the fiber and resin properties are smeared, are very efficient for large scale structural analysis, but lack the ability to model the complex nonlinear behavior of the resin and fail to capture the complex load transfer mechanisms between the fiber and the resin matrix. On the other hand, high fidelity mesoscale models, where the fiber tows and matrix regions are explicitly modeled, have the ability to account for the complex behavior in each of the constituents of the composite. However, creating a finite element model of a larger scale composite component could be very time consuming and computationally very expensive. In the present study, a three-dimensional mesoscale model of non-crimp composite laminates was developed for various laminate schemes. The resin material was modeled as an elastic-plastic material with nonlinear hardening. The fiber tows were modeled with an orthotropic material model with brittle failure. In parallel, new stress based failure criteria combined with several damage evolution laws for matrix stresses were proposed for a phenomenological model. The results from both the mesoscale and phenomenological models were compared with the experiments for a variety of off-axis laminates.

Aitharaju, Venkat↗

Progressive Damage Modeling of Notched Composites

There is an increased interest in using non-crimp fabric reinforced composites for primary and secondary structural weight savings in high performance automobile applications. However, one of the main challenges in implementing these composites is the lack of understanding of damage progression under a wide variety of loading conditions for general configurations. Towards that end, researchers at GM and NASA are developing new damage models to predict accurately the progressive failure of these composites. In this investigation, the developed progressive failure analysis model was applied to study damage progression in center-notched and open-hole tension specimens for various laminate schemes. The results of a detailed study with respect to the effect of element size on the analysis outcome are presented.

Aitharaju, Venkat↗

Damage Simulation in Non-Crimp Fabric Composite Plates Subjected to Impact Loads

Progressive failure analysis (PFA) of non-crimp fabric (NCF) composite laminates subjected to low velocity impact loads was performed using the COmplete STress Reduction (COSTR) damage model implemented through VUMAT and UMAT41 user subroutines in the frame works of the commercial finite element programs ABAQUS/Explicit and LS-DYNA, respectively. To validate the model, low velocity experiments were conducted and detailed correlations between the predictions and measurements for both intra-laminar and inter-laminar failures were made. The developed material and damage model predicts the peak impact load and duration very close with the experimental results. Also, the simulation results of delamination damage between the ply interfaces, in-plane matrix damages and fiber damages were all in good agreement with the measurements from the non-destructive evaluation data.

Satyanarayana, Arunkumar↗