Finite element model updating using deterministic optimisation: A global pattern search approach

• Published in: Engineering structures Vol. 195; pp. 373 - 381
• Main Authors: Hofmeister, Benedikt, Bruns, Marlene, Rolfes, Raimund
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.09.2019; Elsevier BV
• Subjects: Algorithms; Convergence; Damage localization; Derivative-free methods; Deterministic optimisation; Finite element method; Global optimization; Heuristic methods; Mathematical models; Model updating; Objective function; Pattern search; Rotor blades; Rotor blades (turbomachinery); Search algorithms; Structural damage; Turbines; Wind damage; Wind power; Wind turbine; Wind turbines; Finite element method; Deterministic optimisation; Rotor blades; Derivative-free methods; Wind turbine; Model updating
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2019.05.047

•Generalisation of the pattern search algorithm leads to a global optimisation method.•Application of deterministic optimisation to finite element model updating problem.•Damage localisation and quantification of fictitious defect in wind turbine blade.•Deterministic global optimisation algorithms can outperform metaheuristic ones. With this work, we present a novel derivative-free global optimisation approach for finite element model updating. The aim is to localise structural damage in a wind turbine rotor blade. For this purpose, we create a reference finite element model of the blade as well as a model with a fictitious damage. To validate the approach, we use a model updating scheme to locate the artificially induced damage. This scheme employs numerical optimisation using the parameterised finite element model and an objective function based on modal parameters. Metaheuristic algorithms are the predominant class of optimisers for global optimisation problems. With this work, we show that deterministic approaches are competitive for engineering problems such as model updating. The proposed optimisation algorithm is deterministic and a generalisation of the pattern search algorithm. It picks up features known from local deterministic algorithms and transfers them to a global algorithm. We demonstrate the convergence, discuss the numerical performance of the proposed optimiser with respect to several analytical test problems and propose a possible trade-off between parallelisation and convergence rate. Additionally, we compare the numerical performance of the proposed deterministic algorithm concerning the model updating problem to the performance of well-established metaheuristic and local optimisation algorithms. The introduced algorithm converges quickly on test functions as well as on the model updating problem. In some cases, the deterministic algorithm outperforms metaheuristic algorithms. We conclude that deterministic optimisation algorithms should receive more attention in the field of engineering optimisation.