Interdisciplinary Coupling in Multidisciplinary Design Optimization (MDO): Challenges and Innovative Solutions
Keywords:
Multidisciplinary Design Optimization (MDO), Interdisciplinary Coupling, Computational Complexity, Surrogate Modeling, Multi-Fidelity ModelingAbstract
Multidisciplinary Design Optimization (MDO) is a powerful computational approach for optimizing complex engineering systems by integrating multiple disciplines. One of the fundamental challenges in MDO is interdisciplinary coupling, which arises due to interdependencies between different subsystems. These couplings introduce significant computational complexity, data integration issues, uncertainty propagation, and convergence difficulties, making optimization challenging. This review article explores the key challenges associated with interdisciplinary coupling in MDO, including computational costs, model uncertainties, and disciplinary conflicts. Additionally, the paper highlights innovative solutions such as surrogate modeling, machine learning-based acceleration techniques, multi-fidelity modeling, coupled solvers, and blockchain-enabled data sharing. Recent advancements in aerospace, automotive, and civil engineering applications demonstrate the effectiveness of these techniques in overcoming interdisciplinary coupling issues. The study concludes that integrating adaptive artificial intelligence-driven MDO frameworks can further streamline interdisciplinary collaboration and enhance optimization efficiency. Future research should focus on improving computational scalability, uncertainty quantification, and real-time data integration in MDO systems.
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