PSI - Issue 38

Ronald Schrank et al. / Procedia Structural Integrity 38 (2022) 30–39 Author name / Structural Integrity Procedia 00 (2021) 000 – 000

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5. New strategy for automated test simplification using numerical optimization methods 5.1. Basics An efficient interaction of both disciplines • (state-of-the-art) fatigue analyses • numerical optimization

is the fundamental basis for an high-performance software solution, even if using heuristic methods. Depending from the complexity of the special task up to 10 4 …10 5 or even more fatigue analyses runs have to be performed within an acceptable time range.

Fig. 10. Efficient interaction as a basis for high-performance optimization

An hybrid optimization approach is implemented: • global optimization using a self-adapting evolution strategy, see Weicker (2015). • results of global optimization undergo further local optimization using a hill-climbing algorithm This combination is also called memetic algorithm by some authors, e. g. Kramer (2009). 5.2. Optimization parameters An arbitrary number of optimization parameters can be defined within the new software implementation. Referring the example of the control arm 11 optimization parameters are defined: • damping force : amplitude value, mean value • spring force : amplitude value, mean value, phase angle (related to ) • knuckle force : amplitude value, mean value, phase angle (related to ) • direction of and (both parallel due to requirements of test setup): two angles referring a local spherical coordinate system • direction of (in x-y-plane): one angle 5.3. Objective function Damage discrepancies between full test and simplified test result in error values to be summed over all hotspots: where: - error sum (to be minimized by the optimization algorithm) - number of error terms - weighting factors ( ) - error functions