An integrated framework for static stability design of tailless BWB mini-UAVs based on XFLR5, LHS, and Gaussian Process Regression

Tailless blended wing body (BWB) mini-UAVs offer important aerodynamic and structural advantages, but the absence of conventional tail surfaces makes inherent static stability difficult to achieve during conceptual design. This study proposes an integrated framework for early-stage static stability assessment of tailless BWB mini-UAVs by combining XFLR5 aerodynamic analysis, Latin Hypercube Sampling (LHS), and Gaussian Process Regression (GPR). Seven geometric variables describing chord distribution, sweep, twist, and dihedral are sampled within prescribed ranges, and XFLR5 is used to compute the key longitudinal and lateral-directional stability derivatives Cmα, Cm0, Clβ, and Cnβ. GPR surrogate models are then trained to predict these derivatives and enable rapid exploration of the design space. Cross validation shows that an LHS size of 50 provides the best compromise between predictive accuracy and computational cost among the tested sampling levels. Sensitivity analysis indicates that root chord and root-tip twist difference dominate longitudinal stability, outward dihedral mainly influences roll stability, and kink chord strongly affects directional stability. The framework also identifies feasible tailless BWB configurations satisfying the static stability criteria. The main contribution of this work is a unified and computationally efficient methodology for multi-axis static stability screening of tailless BWB mini-UAVs at the conceptual design stage.