Browsing by Author "Heidebrecht, A."

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    An automated approach to nacelle parameterization using intuitive class shape transformation curves
    (American Society of Mechanical Engineers (ASME), 2017-01-18) Christie, Robert; Heidebrecht, A.; MacManus, David G.
    A tool to create parametric aerodynamic shapes using intuitive design variables based on class shape transformation (CST) curves is presented. To enable this, a system has been developed which accepts arbitrary constraints and automatically derives the analytical expressions which describe the corresponding class shape transformation curves. Parametric geometry definitions for fan cowl and intake aero-lines were developed using the generalized method. Computational fluid dynamics (CFD) analysis of the fan cowl shows that despite the simple geometry definition, its performance characteristics are close to what would be expected of a finished design. The intake geometry was generated in a similar way and met the typical performance metrics for conventional intakes. This demonstrates the usefulness of the tool to quickly and robustly produce parametric aero-lines with good aerodynamic properties, using relatively simple intuitive design variables.
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    An optimization method for nacelle design
    (American Institute of Aeronautics and Astronautics, 2017-01) Robinson, M.; MacManus, David G.; Heidebrecht, A.
    A multi-objective optimiZation method is demonstrated using an evolutionary genetic algorithm. The applicability of this method to preliminary nacelle design is demonstrated by coupling it with a response surface model of a wide range of nacelle designs. These designs were modelled using computational fluid dynamics and a Kriging interpolation was carried out on the results. The NSGA-II algorithm was tested and verified on established multi-dimensional problems. Optimisation on the nacelle model provided 3-dimensional Pareto surfaces of optimal designs at both cruise and off-design conditions. In setting up this methodology several adaptations to the basic NSGA-II algorithm were tested including constraint handling, weighted objective functions and initial sample size. The influence of these operators is demonstrated in terms of the hyper volume of the determined Pareto set.