Multi-disciplinary design optimization of a training aircraft wing

In the contemporary era, the reliability of simulation technology is high, but often time consuming. This poses a challenge in studies with limited schedule, where long simulation durations become a serious issue. Surrogate-based optimization (SBO) enhances a meta-model within an optimization framework, estimating objective and constraint functions by strategically placing various design experiment points. Over the past two decades, optimization techniques have evolved to reach optimal results. For achieving the most effective solutions, the meta-model is refined with a sufficient number of design experiment samples. A surrogate model improves to derive a mathematical representation of simulations. As an analytical tool, the meta-model serves as an effective means for optimizing structural components. SBO offers a potential approach to optimi zing a range of simulation data, simulations in engineering. This study expands a CFD setup into a 3D wing model, incorporating a realistic structural model. Aerodynamic design parameters are identified through literature review. Following the CFD analysis, the pressure distribution of the defined a wing is integrated into the structural finite element model, with initial results being assessed. The study then successfully validates the op timization algorithm using a speed reducer test case. It is applied surrogate-based model in tandem with a parameterized geometry, focusing on mass optimization while adhering to frequency and lift to drag ratio, and lift force constraints. The effectiveness of this optimization method is further corroborated by comparing it with one of established optimization techniques