Optimization of Dynamic Mechanical Behavior in Aerospace Structural Design Enhanced by Quantum Computing

Background: Aerospace structures’ dynamic mechanical behavior is important to ensure their dependability and functionality in challenging environments. Aircraft materials and structures have complicated, dynamical characteristics that are difficult for traditional optimization techniques to handle, especially in various operating environments. Aim: This research aims to integrate innovative quantum computing (QC) approaches to optimize the dynamic mechanical behavior of aerospace structures. Approaches: Material characteristics, design limitations and operational factors for aircraft structures are all included in the dataset. Pre-processing utilizes data cleaning and normalization and principal component analysis (PCA) for feature extraction. CQG-SO’s method is assessed using Finite Element Analysis (FEA), which evaluates the mechanical behavior in real-world scenarios. The Chaotic Quantum Genetic fused Shark Optimizer (CQG-SO) investigates optimal design parameters by handling high-dimensional data and solving stochastic optimization problems efficiently. Results: The findings demonstrate that the suggested method significantly improves structural optimization, obtaining greater vibration-damping values and requiring less computation time. These developments make it possible to create aerospace structures that are stronger, lighter and more durable. This research demonstrates key results, including an Accuracy of 94.2%, Precision of 91.8%, Recall of 90.5%, F1-score of 92.1% and Load-bearing capacity of 94.5%. Additionally, the proposed method enhances structural optimization, achieving 87.5% vibration damping and reducing computational time to 92.0%, leading to aerospace structures that are stronger, lighter and more durable. Conclusion: Finally, incorporating QC into aircraft structural design is revolutionary regarding performance and efficiency improvements.