Review of Metamodeling Techniques in Support of Engineering Design Optimization

Abstract

Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.

Efficient global optimization of reservoir geomechanical parameters based on synthetic aperture radar-derived ground displacements

“…For problems of relatively large dimension (up to 10 uncertain parameters), the forward efficient global optimization (EGO) approach (Jones et al, 1998) has proven a very efficient procedure, becoming largely used for mechanical engineering applications in recent years (Jones, 2001;Wang and Shan, 2007;Lovison and Rigoni, 2010). Owing to exact convergence properties and small computational burden, EGO has the potential to be an effective optimization method also for geomechanical applications.…”

Weighted Gradient-Enhanced Kriging for High-Dimensional Surrogate Modeling and Design Optimization

“…Therefore, the applications of this method are usually limited to either two-dimensional aerodynamic configurations [61] or three-dimensional (3-D) configurations using low-fidelity and fast CFD methods [62]. And the third approach is surrogate-based optimization (SBO) [1][2][3][4][5][6][7][8][9]. As previously mentioned, the surrogate models enable us to find the global optimum within a very limited number of expensive evaluations [30,38].…”

“…= lift, drag, and pitching moment coefficients O VER the past two decades, surrogate-based modeling and optimization have played an increasingly important role in different areas of aerospace engineering [1][2][3][4][5][6][7][8], such as aerodynamic design optimization [9,10], structural optimization [11], and multidisciplinary design optimization of aircraft or spacecraft [12], where high-fidelity and expensive numerical simulations, such as computational fluid dynamics (CFD) or computational solid dynamics (CSD), are often employed. Surrogate model is also called "response surface model", "metamodel", "approximation model", or "emulator" by the literature in different research areas.…”

On the use of surrogate-based modeling for the numerical analysis of Low Impact Development techniques

“…The validation can be conducted by evaluating the agreement between values of the variable of interest predicted by both the surrogate and original models on an independent set of sample points. Cross validation strategies such as k-fold and leave-one-out cross validation have also been used in the literature (Wang and Shan, 2007). In the present study, an independent set of sample points was generated using the LHS and used to validate the model.…”