Parametric Reduced-Order Model Drag Polars for Supersonic Transport Conceptual Design

Traditional low-fidelity, semi-empirical aerodynamic models are typically used to provide drag polars needed for conceptual-level mission analysis but are insufficient for unconventional vehicles. In such scenarios, higher-fidelity, computationally expensive models commonly used in preliminary design are required. Due to the associated high computational cost and longer model evaluation time, it is impractical to estimate aerodynamic performance for all the design perturbations explored in the conceptual sizing stage. This paper presents a novel approach for efficiently approximating parametric mission drag polars by integrating sensitivity analysis, multifidelity surrogate models, and parametric reduced-order modeling techniques. The standardized regression coefficients method is used to select the geometry design parameters that have the largest impact on key vehicle performance metrics. These parameters, combined with hierarchical kriging surrogate models, are employed to generate mission drag polars for fixed vehicle geometries. Then, the proper orthogonal decomposition reduced-order model with kriging interpolation is used to approximate the coherent structure of mission drag polars. The proposed method is demonstrated on a supersonic commercial aircraft. Results show that both the multifidelity surrogate model and the reduced-order model effectively emulate vehicle drag polars well for fixed and varying vehicle geometries, respectively.