Sensitivity analyses are frequently used during the design process of engineering systems to qualify and quantify the effect of parametric variation on the performance of a system. Two primary types of sensitivity analyses are generally used: local and global. Local analyses, generally involving derivative-based measures, have a significantly lower computational burden than global analyses but only provide measures of sensitivity around a nominal point. Global analyses, generally performed with a Monte Carlo sampling approach and variation-based measures, provide a complete description of a concept’s sensitivity but incur a large computational burden and require significantly more information regarding the distributions of the design parameters in a concept. Local analyses are generally suited to the early stages of design when parametric information is limited and a large number of concepts must be considered (necessitating a light computational burden). Global analyses are more suited towards the later stages of design when more information regarding parametric distributions is available and fewer concepts are being considered. Current derivative-based local approaches provide a significantly different set of measures than a global variation-based analysis. This makes a direct comparison of local to global measures impossible. To reconcile local and global sensitivity analyses, a hybrid local variation based sensitivity (HyVar) approach is presented. This approach has a similar computational burden to a local approach but produces measures in the same format as a global variation-based approach (contribution percentages). This HyVar sensitivity analysis is developed in the context of a functionality-based design and behavioral modeling framework. An example application of the method is presented along with a summary of results produced from a more comprehensive example.

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