Sensitivity Analysis of Random Frequency Responses of Hybrid Multi-functionally Graded Sandwich Shells

Purpose This paper presents the sensitivity analysis (SA) of the random natural frequency responses of hybrid multi-functionally graded sandwich (HMGS) shells for establishing a unified measure in the case of multi-objective performances. The functionally graded materials, laminated composites, and sandwich cores are employed to develop such novel structures to tailor the benefits of each component in a single structure. Methods A novel MARS-based sensitivity analysis of these hybrid multi-functionally graded sandwich shells is developed to achieve computational efficiency without compromising with the outcome. Such surrogate-assisted FE approaches are crucial for computationally intensive multi-objective systems. The basic governing equations of random natural frequency are framed based on finite element formulation. The variabilities of major influencing random input parameters (here, geometric and material properties) are carried out by employing Monte Carlo simulation (MCS). The multivariate adaptive regression spline (MARS) is adopted as a surrogate model to increase computational efficiency. Results and Conclusion The results are portrayed to showcase the significant effects of variable input parameters (sensitivity) on random frequency responses of such novel HMGS shells. Hence, it provides the predominant random input parameters and their relative degree of importance while designing such multi-dimensional structural systems. Thus, the contribution of this article lies in both the development of a computationally efficient sensitivity analysis approach and the insightful numerical results for hybrid structures presented thereafter. The comprehensive and collective sensitivity quantification considering multi-functional objectives, as presented in this article, would lead to efficient computational modelling of complex structural systems for more optimized designs and better quality control during manufacturing.