Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop

Abstract

Complex engineered systems are typically designed using a systems engineering framework that is showing its limitations. Multidisciplinary design optimization (MDO), which has evolved remarkably since its inception 25 years ago, offers alternatives to complement and enhance the systems engineering approach to help address the challenges inherent in the design of complex engineered systems. To gain insight into these challenges, a one-day workshop was organized that gathered 48 people from industry, academia, and government agencies. The goal was to examine MDO’s current and future role in designing complex engineered systems. This paper summarizes the views of five distinguished speakers on the “state of the research” and discussions from an industry panel comprised of representatives from Boeing, Caterpillar, Ford, NASA Glenn Research Center, and United Technologies Research Center on the “state of the practice.” Future research topics to advance MDO are also identified in five key areas: (1) modeling and the design space, (2) metrics, objectives, and requirements, (3) coupling in complex engineered systems, (4) dealing with uncertainty, and (5) people and workflow. Finally, five overarching themes are offered to advance MDO practice. First, MDO researchers need to engage disciplines outside of engineering and target opportunities outside of their traditional application areas. Second, MDO problem formulations must evolve to encompass a wider range of design criteria. Third, effective strategies are needed to put designers “back in the loop” during MDO. Fourth, the MDO community needs to do a better job of publicizing its successes to improve the “buy in” that is needed to advance MDO in academia, industry, and government agencies. Fifth, students and practitioners need to be better educated on systems design, optimization, and MDO methods and tools along with their benefits and drawbacks.

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

“…Although significant steps have been achieved in this direction, there are still several research gaps in terms of expanding MDO, and one of the most critical shortcomings according to paper I is still the breadth and depth of the disciplinary modeling as it has also been previously reported by Agte et al (2009). In addition to this, the review of paper II identified that there are also several hinders towards implementing MDO in the PDP, and as already stated in the work of Simpson and Martins (2011), there is still a shortage of technical publications, absence of MDO in higher education, uncoordinated research, problems with industrial adaptation, and barriers in the transmission of knowledge due to corporate secrecy (see Figure 17).…”

“…This leads to a more holistic view of the system interactions as well as a reduction of the costly iterations between the engineering teams, while depending on the fidelity of the tools, it can also allow for high-detail and thus more accurate design data to flow into the conceptual design stage (Agte et al, 2009). On the whole, the field of MDO has been constantly growing, and nowadays it is possible to enhance the speed and quality of the calculations by taking into account state-of-the-art analysis capabilities, advanced decomposition architectures, smart integration tools, and more efficient computing techniques (Simpson and Martins, 2011). …”

“…More specifically, the majority of cases are only concentrating on conceptual design, whereas most of them have also been neglecting important design requirements by focusing solely on the aeronautical aspects of the aircraft. At the same time, the studies with high-fidelity tools, and thus more industrial interest, are reporting many issues regarding the computational efficiency of MDO, and to this date, this as well as other integrational limitations have evolved as a major hinder towards the complete implementation of MDO within the PDP (Simpson and Martins, 2011). Finally, most publications on MDO have been excessively focused on proving the technical benefits of its implementation, and as a result, there are currently many case studies on how to get optimized designs, but hardly any research on how those can be meaningfully used by the manufacturing industry (Belie, 2002).…”

“…In an MDO scenario, the disciplinary models should be able to exchange information seamlessly, and for that reason, there is an additional motivation for people to come together and interact much more often compared to the traditional PDP structure (Safavi et al, 2012;Safavi et al, 2015). This new state allows all departments and teams to see clearly the main design objectives, and as a direct consequence, it generates a state of increased awareness that is argued to reduce the costly and timeconsuming iterations within the groups (Simpson and Martins, 2011).…”

Graph-based algorithms and data-driven documents for formulation and visualization of large MDO systems

“…Despite the high potential gains, MDO is not as widely used as one would expect. Both technical [1] and non-technical barriers are hampering its full exploitation, as discussed below in this section [4,27,28].…”