Insights from developing a multidisciplinary design and analysis environment

• Insights from developing a multidisciplinary design and analysis environment are presented.
• Components are used to support application and information integration services.
• Design collaboration is supported by project and workflow management techniques.
• Tailored modeling tools are developed for flexibly manipulating geometric data.
• Numerical optimization techniques are used to achieve global design optimization.

This paper presents technical insights gained from developing a multidisciplinary design and analysis (MDA) environment, which proficiently integrates, coordinates and controls disciplinary software packages, data sources, and human factors. The MDA environment cost-effectively boosters global optimization of product design problems by means of integrating and coordinating engineering resources, implementing optimization approaches, and reconciling contradicting disciplinary objectives. This paper begins with depicting a multidisciplinary view of a generic complex engineering system, which sets the basic tone of developments of the environment. Subsequently, this paper proceeds with descriptions of information techniques or software utilities that constitute core competencies of the MDA environment. Firstly, software components based integration techniques are implemented to enhance interoperability among heterogeneous engineering applications and data sources. The integration techniques have viably overcome engineering inefficiencies, system incongruences, and information inconsistencies caused by ‘automation islands’ problems. Secondly, project and workflow management utilities are developed to support collaborative design, which allows better utilization of engineering resources and effective coordination. Thirdly, tailored geometry modeling techniques are implemented to enable expeditious representations of shape variations in congruence with outcomes of multiphysics analyses and simulations. Fourthly, optimization strategies, sensitivity analysis, surrogate models and searching algorithms are coded to enable global engineering optimization. Finally, this paper concludes with insights gained from developments of the MDA infrastructure.