<![CDATA[Increasing complexity in computational fluid dynamics (CFD) simulations demands faster, more accurate, and scalable approaches for solving highly nonlinear equations. Conventional high-performance computing remains effective in aerospace engineering but faces limitations in large-scale turbulence modeling, compressible flows, multiphysics interactions, and optimization requiring intensive iterations. This study evaluated the effectiveness of hybrid quantum-classical computing for solving complex nonlinear CFD equations and improving aerospace simulation performance. A mixed-methods sequential explanatory design involved 540 computational benchmark simulations and 240 experimental scenarios covering conventional solvers, hybrid quantum-classical optimization, and quantum-enhanced nonlinear solvers. Quantitative analyses used descriptive statistics, structural equation modeling, hierarchical regression, mediation, and moderation analysis, while qualitative evidence from expert interviews, computational observations, software evaluations, and document reviews underwent thematic analysis. Results showed that hybrid quantum-classical computing significantly improved convergence efficiency, numerical accuracy, turbulence prediction, scalability, residual error reduction, and simulation reliability. Hybrid optimization partially mediated the effect of quantum algorithms on computational efficiency, while mesh optimization strengthened convergence and engineering accuracy. These findings support integrating quantum computing with established CFD methods to enable faster optimization, stronger prediction, and scalable next-generation aerospace simulations.]]>
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