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Offline optimization of controller parameters for complex non-linear processes can be time consuming, even with high performance computers. This paper demonstrates how Metamodelling techniques can be utilized to quickly tune the controller parameters for a nonlinear process. The process used in this study is the mixing process which is a multivariable and intrinsically non-linear plant. The Radial Basis Function Neural Network Metamodel founded a good approximation to the optimum controller parameters in this case. This paper proposes an intuitive methodology to use only a small fraction of the design space to create a Radial Basis Function Neural Network Metamodel that is good enough to optimize the system. Comparisons were made between the controllers optimized using the Metamodelling technique and the original large space design.

We present an analysis of the influence of various cycle counting approaches on the estimated fatigue life for critical rotorcraft components. The entire study is demonstrated using the pitch link of a utility helicopter similar to the UH-60 Black Hawk helicopter as a representative component. Different counting methods predict fatigue life differently due to the varied fundamental parameters or approaches they take to characterize the stress profile. Hence, it becomes vital to choose an appropriate method for a particular application so that it does not over or under predict fatigue life during the design process. Based on this, we investigated four methods namely, Rainflow, Peak, Level and Range counting methods using the stresses developed in the pitch link due to aerodynamic maneuvers. Cycle counts obtained were used in a high cycle fatigue model to estimate cycles/blocks to failure. We also performed a sensitivity analysis to understand the effect of changing vehicle control input parameters on the prediction by counting methods.