@inproceedings{0a9fa4b2bd694cadad06218c48bb83ed, title = "Dimension reduction and decomposition using causal graph and qualitative analysis for aircraft concept design optimization", abstract = "With the increasing design dimensionality, it is more difficult to solve Multidisciplinary design optimization (MDO) problems. To reduce the dimensionality of MDO problems, many MDO decomposition strategies have been developed. However, those strategies consider the design problem as a black-box function. In practice, the designers usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating knowledge of the design problem. Causal graph is employed to show the input-output relationships between variables. Qualitative analysis using design structure matrix (DSM) is carried out to automatically find the variables that can be determined without optimization. According to the weight of variables, the MDO problem is divided into two sub-problems, the optimization problem with respect to important variables, and the one with less important variables. The novel method is performed to solve an aircraft concept design problem and the results show that the new dimension reduction and decomposition method can significantly improve optimization efficiency.", keywords = "Aircraft concept design, Causal graph, Dimension reduction, Dimensional analysis, Multidisciplinary design optimization", author = "Di Wu and Eric Coatanea and Wang, {G. Gary}", year = "2017", doi = "10.1115/DETC201767601", language = "English", booktitle = "43rd Design Automation Conference", publisher = "The American Society of Mechanical Engineers ASME", }