Efficient uncertainty quantification and management in the early stage design of composite applications. (1st November 2020) - Kumar, Dinesh; Koutsawa, Yao; Rauchs, Gaston; Marchi, Mariapia; Kavka, Carlos; Belouettar, Salim
Abstract: One of the key enablers of valued early decision-making in composite material designs is the ability to account for different aspects/scale of uncertainties within models and design processes. In order to enable well-judged decisions and to improve the trust of industrial decision-makers, measures of uncertainty, risk, and cost involved in materials are crucial. In this work, we provide i) efficient uncertainty analysis (UA) and ii) sensitivity analysis (SA) in composite structures to quantify the influence of input parameters on the output of interest accounting for the stochastic nature in multi-scale modeling with a large number of uncertain parameters. UA also provides a statistical distribution of the output of interest. The influences of different input parameters on the system responses can be estimated by conducting the global sensitivity analysis on the multi-scale models. To this end, a data-driven model approximating the relationship between the inputs and outputs is constructed by using an adaptive Sparse Polynomial Chaos Expansion (SPCE) approach. The sensitivities of the input factors on the system performances are computed analytically from the constructed data-driven model without any additional computational cost. To demonstrate the sensitivity and uncertainty management, two different test cases (composite leafspring and aircraft fuselage airframe) are considered for the stochastic structural analysis in multi-scale composite modeling. In both cases, the combined effects of multi-scale uncertainties are evaluated on the structural performances. Input parameters include the material microstructure (micro-scale uncertainties), composite layers stacking sequence (mesoscale uncertainties), and structural loading (uncertainty in macro-scale). The responses are provided in terms of their variations and probability distributions.