New Insights into Multidisciplinary Design Optimization

Dear Colleagues,

In the field of complex system engineering (e.g. aerospace, automotive, energy, civil engineering), designers must manage increasingly challenging requirements. System specifications are narrowing due to factors such as safety regulations, environmental constraints, and cost considerations. Development timelines are contracting, and the imperative to establish system performance quickly and with sufficient accuracy adds another layer of complexity. The design of complex systems involves a multidisciplinary process that couples various domains, including aerodynamics, propulsion, structures, electric/hydraulic systems, and guidance, navigation, and control. Each of these areas encompasses distinct groups of highly skilled experts and relies on advanced high-fidelity simulation models.

To face these challenges, multidisciplinary design optimization has become a standard for apprehending the complexity of process design. This multidisciplinary process serves as a paradigm for general complex engineering systems, as traditional design approaches cannot deal with extensive, costly numerical simulations. This shift not only reduces design process expenses but also mitigates risks and accelerates development timelines. Addressing the challenges inherent in complex engineering system design leads to the need for advanced mathematical algorithms for design exploration and optimization. This Special Issue aims to showcase the latest approaches and applications involved in multidisciplinary system analysis and optimization. This study focuses on diverse areas, including advancements in surrogate modeling, multidisciplinary design analysis and optimization, uncertainty quantification, dimension reduction, multifidelity modeling, and machine learning.

Dr. Mathieu Balesdent
Dr. Loïc Brevault
Guest Editors

Manuscript Submission Information

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• multidisciplinary analysis and optimization
• optimization algorithms
• multifidelity simulation
• uncertainty quantification
• reliability-based design optimization
• design under uncertainty
• surrogate modeling
• sensitivity analysis