Efficient multi-objective shape optimization using proper orthogonal decomposition with variable fidelity concept

System Identification Using the Proper Orthogonal Decomposition with Multiple Loads

The Proper Orthogonal Decomposition (POD) is a method that may be applied to linear and nonlinear structures for extracting important information from a measured structural response. Previous methods of using the POD for system identification typically use the POD for model reduction only and system identification is accomplished via standard methods. A method for using the POD-based identifica...

Proper Orthogonal Decomposition: Applications in Optimization and Control

Artificial viscosity proper orthogonal decomposition

We introduce improved reduced-order models for turbulent flows. These models are inspired from successful methodologies used in large eddy simulation, such as artificial viscosity, applied to standardmodels created by proper orthogonal decomposition of flows coupled with Galerkin projection. As a first step in the analysis and testing of our new methodology, we use the Burgers equation with a s...

Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems

In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carried out assisted by surrogate models, which are constructed by adding a local deviation estimated by the kriging method and a global model approximat...

Multi-objective optimization design of plate-fin heat sinks using an Evolutionary Algorithm Based On Decomposition

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