Sensitivity Filtering

Node based optimization methods cover the largest possible design space, but the sensitivities for this large number of design variables are prone to mesh dependent and noisy disturbances. By filtering the sensitivities, one can overcome this issue and smooth the sensitivity information. At the same time, the filter radius can be used as a design parameter du guide the optimization into a local minimum which is characterized by the chosen radius. Thereby the design of the optimal solution can be influenced, while maintaining the advantage of the large design space

Design features

Industrial designs often contain features (e.g. feature lines in automotive design) that should be conserved during the shape optimization, even though the are not included in the global optimum. Assuming the initial design contains such features, the sensitivity information usually would have local maxima at these areas, trying to remove these. If the filter radius is larger then this peak in sensitivity, it will be smoothed and the design feature is preserved.. [Hojjat et al. 2014]

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