Abstract
The design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.
Highlights
The development of aerospace systems involves sophisticated system engineering
The merits of multidisciplinary design optimization (MDO) have been widely proven in the design practices of engineering systems, such as aircraft [5–10], automobiles [11–13], ships [14,15], and electric devices [16, 17]
An MDO problem is essentially treated as a general constrained nonlinear optimization problem, where the computationally intensive multidisciplinary analysis (MDA) process is replaced with metamodels to reduce the number of expensive function calls
Summary
The development of aerospace systems involves sophisticated system engineering. To reduce the computational cost of expensive blackbox optimization problems, metamodel-based design optimization (MBDO) techniques have been developed over the past two decades [28]. These methods are called surrogate-assisted analysis and optimization methods [29]. They involve the construction of a metamodel (or a surrogate) based on a set of samples to approximate the original expensive simulation models for analysis or optimization These MBDO methods can be applied to solve MDO problems. In this case, an MDO problem is essentially treated as a general constrained nonlinear optimization problem, where the computationally intensive MDA process is replaced with metamodels to reduce the number of expensive function calls.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
