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Systems modeling for product design
Published in Adedeji B. Badiru, Systems Engineering Models, 2019
Under collaborative design paradigm (Wu et al., 2011), the first common topic is Multidisciplinary Design Optimization (MDO) which is defined as “an area of research concerned with developing systematic approaches to the design of complex engineering artifacts and systems governed by interacting physical phenomena” (Alexandrov, 2005). Researchers agree that interdisciplinary coupling in the engineering systems presents challenges in formulating and solving the MDO problems. The interaction between design analysis and optimization modules and multitudes of users is complicated by departmental and organizational divisions. According to Braun and Kroo (1997), there are numerous design problems where the product is so complex that a coupled analysis driven by a single design optimizer is not practical as the method becomes too time consuming either because of the lead time needed to integrate the analysis or because of the lag introduced by disciplinary sequencing. Some researchers have taken project management as a means to facilitate and coordinate the design among multi-discipline product design (Thal, Badiru, and Sawhney, 2007).
On the application of CFD and MDO to aircraft wingboxes using commercial software
Published in Alphose Zingoni, Current Perspectives and New Directions in Mechanics, Modelling and Design of Structural Systems, 2022
To generate a design for an aircraft wingbox, there are many design requirements that need to be satisfied such as weight, stress, and aerodynamic performance. In a traditional design process, an initial design is often generated using past experience and previous designs as a basis to be modified. Multidisciplinary Design Optimization (MDO) automates this process by optimizing based on cost functions and constraints. This method can also allow the generation of designs that are vastly different from the initial given enough iterations.
Reliability-based multidisciplinary concurrent design optimization method for complex engineering systems
Published in Engineering Optimization, 2022
Xin-Jia Meng, Li-Xiang Zhang, Yue Pan, Zhi-Min Liu
Complex engineering systems generally involve multiple disciplines or subsystems. In the design process of such systems, therefore, it is critical to take the subsystem interactions into account (Gray et al. 2019). Multidisciplinary design optimization (MDO) is described as ‘a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergy of mutually interacting phenomena’ (Shi et al. 2019). It is widely known that MDO is a suitable and effective technology for complex engineering design. One significant feature of MDO is the use of decomposition and coordination strategies to reduce the design complexity and to achieve an optimal design. Well-known MDO methods using these strategies are described in Haftka, Sobieszczanski-Sobieski, and Padula (1992), Martins and Lambe (2013) and Yi, Shin, and Park (2008), among which the multilevel MDO methods, such as collaborative optimization (CO), concurrent subspace optimization (CSSO) and bi-level integrated system synthesis (BLISS), are more popular in practical applications. In these multilevel methods, the design problem is decomposed into several concurrent subsystem problems along disciplinary or design team lines, and the optimization is performed at both system and subsystem levels (Haftka and Watson 2005; Price, Keane, and Holden 2011). Such methods can naturally achieve the advantages of simultaneous multiple subsystem design and concurrent optimization and, therefore, they are the well-accepted MDO methods for supporting the distributed design optimization in an industrial context.
Coupled-analysis assisted gradient-enhanced kriging method for global multidisciplinary design optimization
Published in Engineering Optimization, 2021
Xu Chen, Peng Wang, Huachao Dong, Xiaozhe Zhao, Deyi Xue
In solving an MDO problem, the optimization architecture and the algorithm are two factors that significantly influence the efficiency and accuracy of optimization. MDO architecture (Martins and Lambe 2013) is used to define the formulation of the whole MDO problem by organizing the coupled disciplines and the optimizer, while the optimization algorithm is used to achieve the optimal solution. Many MDO architectures have been developed in the past two decades, including multidisciplinary feasible (MDF) (Balling and Sobieszczanski-Sobieski 1996), individual discipline feasible (IDF) (Cramer et al.1994), concurrent subspace optimization (CSSO) (Bloebaum, Hajela, and Sobieszczanski-Sobieski 1992), collaborative optimization (CO) (Braun et al.1996), bi-level integrated systems synthesis (BLISS) (Sobieszczanski-Sobieski, Agte, and Sandusky 2000) and so on (Kim et al.2003; Chittick and Martins 2009). Some benchmarking studies have also been carried out to compare the performances of different MDO architectures (Tedford and Martins 2010; Zhang, Song, et al.2017). In most cases, the MDF architecture has been considered as the one with high computation efficiency and low implementation effort.