Gas turbine engine secondary flow systems are sensitive to variation in part dimensions, clearances, flow coefficients, swirl ratios, head loss factors, tolerances, boundary conditions, etc. This paper reveals a process and software application which embodies the process wherein both offer a measurable contribution to secondary airflow system reliability. The probabilistic methodology is empirically validated by (1) applying it to an engine component that failed in a validation test, and (2) demonstrating that a multiple order sensitivity analysis performed during detailed design was unable to detect a failure mode while a probabilistic analysis revealed a small yet significant risk of catastrophic failure. Therefore, a secondary flow analyst does not have a justifiable reason to be highly confident of a design qualified by a first, second, or higher order sensitivity analysis. The last example empirically demonstrates compatibility of optimization techniques with probabilistic methods (as part of the process) to quantify the likelihood of failure and reveal an optimized design space of key characteristics where risk is eliminated and the effects of variation are controlled. Trade study analysis is more valuable if it includes a quantitative evaluation of the effects of variation on alternate designs and the response to failure modes. A key feature of the software application is a relational database with the capability to configure and effectively manage flow networks in many forms including, a status model, failure modes of the status model, multiple alternative designs as well as failure modes specific to an alternative design.

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