Design optimization applied in structural dynamics

This paper introduces the design optimization strategies, especially for structures which have dynamic constraints. Design optimization involves first the modeling and then the optimization of the problem. Utilizing the Finite Element (FE) model of a structure directly in an optimization process requires a long computation time. Therefore the Backpropagation Neural Networks (NNs) are introduced as a so called surrogate model for the FE model. Optimization techniques mentioned in this study cover the Genetic Algorithm (GA) and the Sequential Quadratic Programming (SQP) methods. For the applications of the introduced techniques, a multisegment cantilever beam problem under the constraints of its first and second natural frequency has been selected and solved using four different approaches

Dynamic substructuring and reanalysis methods in a surrogate based design optimization environment

Abstract In light weight structure design, vibration control is necessary to meet strict stability requirements and to improve the fatigue life of structural components. Due to ever-increasing demands on products, it is generally more convenient to include vibration prerequisites in a design process instead of using vibration control devices on fixed designs. One of the main difficulties associated to design optimization of complex and/or large structures is the numerous computationally demanding Finite Element (FE) calculations. The objective of this research is to present a novel strategy for efficient and accurate optimization of vibration characteristics of structures. In the proposed strategy, a sub-structuring method is utilized. The FE model of the complete structure is partitioned, educed and then reassembled. This increases the computational efficiency of dynamic analyses. Moreover, this method is coupled with a novel reanalysis technique to speed up the repeated structural analyses. These methods are finally embedded in a surrogate-based design optimization procedure. An academic test problem is used for the validation of this novel approach. Keywords Dynamic substructuring · Reanalysis methods · Surrogate-based optimizatio

Optimizing the dynamic behavior of structures using substructuring and surrogate modeling

During a design process, analysis and, when necessary, modification of the vibration characteristics of a structure are important. Thanks to the developments in computer technology and in numerical analysis methods, particularly the Finite Element Method, a structure can be analyzed extensively in the computer environment long before its first prototype is built. To improve its design and to find a globally optimal configuration, in theory it is possible to directly couple a Finite Element (FE) model with a global optimization method. However, in practice this may not be feasible for complex structures due to the required number of the FE calculations and the corresponding computational costs. Analysis time grows rapidly with the amount of details in the FE model. If the vibration characteristics of a structure need to be improved by modifying the design of the detailed sections, long running analyses are a bottleneck in optimization. The objective of this thesis is to develop an efficient strategy for optimizing the dynamic behavior of complex structures. The strategy is required to be robust, accurate and able to provide a solution which is as close to the global optimum as possible

On the coupling of reanalysis techniques with a surrogate-based design optimization method

In this research, a Surrogate-Based Optimization (SBO) method is coupled with reanalysis techniques to improve the computational efficiency during optimization even further. The reanalysis techniques are used to speed up a reduction and a substructuring method, Craig-Bampton, which is utilized at the analysis step of the proposed SBO strategy. This strategy is suitable for solving problems where the modal and the harmonic responses of structures are required to be modified. An academic test problem is utilized for the demonstration

On a novel approach for optimizing composite materials panel using surrogate models

This paper describes an optimization procedure to design thermoplastic composite panels under axial compressive load conditions. Minimum weight is the goal. The panel design is subject to buckling constraints. The presence of the bending-twisting coupling and of particular boundary conditions does not allow an analytical solution for the critical buckling load. Surrogate models are used to approximate the buckling response of the plate in a fast and reliable way. Therefore, two surrogate models are compared to study their effectiveness in composite optimization. The first one is a linear approximation based on the buckling constitutive equation. The second consists in the application of the Kriging surrogate. Constraints given from practical blending rules are also introduced in the optimization. Discrete values of ply thicknesses is a requirement. An ad-hoc discrete optimization strategy is developed, which enables to handle discrete variables

Generic linking of finite element models for non-linear static and global dynamic analyses for aircraft structures

Depending on the type of analysis, Finite Element(FE) models of different fidelity are necessary. Creating these models manually is a labor intensive task. This paper discusses a generic approach for generating FE models of different fidelity from a single reference FE model. These different fidelity models are created by sub-structuring and a multi-scale superposition technique. Efficiency of the developed approach is demonstrated via non-linear static and modal analysis of a carbon-fiber stiffened panel