Metamodel-based multidisciplinary design optimization methods for aerospace system

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.

Prediction and optimization of the design decisions of liquid cooling systems of battery modules using artificial neural networks

“…On the other hand, researchers prefer the metamodeling method to avoid this situation. The surrogate metamodeling approach is commonly utilized in different engineering cases 37,38 . Commonly used metamodeling methods in CFD studies are RBF, KGA, and ANN.…”

“…The surrogate metamodeling approach is commonly utilized in different engineering cases. 37,38 Commonly used metamodeling methods in CFD studies are RBF, KGA, and ANN. ANN can be used instead of surrogate modeling as an artificial intelligence-based approximation technique to reduce the computational cost and enhance optimization efficiency and the approximation accuracy for modeling complex systems with computational difficulties.…”

A TCP Acceleration Algorithm for Aerospace-Ground Service Networks

“…However, research on the PCC algorithm provided a large amount of information for subsequent analyses. Further, Google proposed an innovative congestion control scheme in 2016 named the dubbed BBR (Bottleneck Bandwidth and Round-trip propagation time) [ 21 , 22 ]. Certain concepts in the BBR are consistent with the PCC algorithm.…”

“…Certain concepts in the BBR are consistent with the PCC algorithm. Nonetheless, in [ 21 , 22 ], it was demonstrated that the BBR algorithm performed excellently in environments with a high bandwidth, long delay, and high packet loss rate.…”

Multidisciplinary design optimization of engineering systems under uncertainty: a review

“…As a powerful methodology to solve the complicated design problems of coupled engineering systems, multidisciplinary design optimization (MDO) has received extensive attention from industry and academia (Zhu et al, 2021a, b;Wang et al, 2018a, b;Zadeh et al, 2019;Meng et al, 2018Meng et al, , 2021aHe et al, 2020;Zhang et al, 2016;D ıaz et al, 2017;Xiao et al, 2015;Martins and Lambe, 2013). Following its success in aeronautics and astronautics engineering, the application field of MDO has been widely extended to other engineering fields, such as vehicles and ships (Wang et al, 2014;Jaeger et al, 2013;Balesdent et al, 2012;Miao et al, 2020a, b;Wu et al, 2017;Wang et al, 2017;Meng et al, 2021b;Yin et al, 2020;Shi et al, 2021;Li et al, 2021a, b). In MDO, design variables and parameters are often treated as deterministic input information.…”