Mathematical networks for thermal transient and non-transient progressive fatigue of engine components

Abstract

Engine components can experience varying centrifugal loads, gas loads, oxidation, micro- structure transformation at high temperatures and stresses induced by temperature gradients. The life consumption of hot engine components depends not only on these factors but also on the time spent at constant-amplitude loads. The damage mechanism of engine components is therefore complex and requires formidable models. These models are not suitable for fatigue management or on-board systems because of their high computational costs. There is a need for efficient simulations that can accurately portray this complex damage mechanism and, at the same time, can be embedded in fatigue management and on-board systems. Mathematical networks were developed to fulfil this need and successfully synthesized the fatigue damage of aircraft structural components from flight parameters. In this paper, the feasibility of training the mathematical networks to synthesize fatigue of engine components is demonstrated. The mathematical attributes of the networks were based on information supplied by Rolls-Royce. The networks’ training mechanism was targeted at the minimization of errors in synthesized accumulative damage values. The mathematical networks synthesized the accumulative fatigue damage of three engine components successfully. One component was subject to non-thermal transient stresses and two components were subject to thermal transient stresses.