Robust High-Fidelity Dataset-Based Shape Optimization for Aircraft Design

Reliable and robust aircraft design requires accurate understanding of aerodynamic behavior across the full flight envelope to predict mission performance, as well as stability and control characteristics. This work demonstrates the potential of automated, high-fidelity dataset-based shape optimization of aircraft for mission-level performance evaluation, using a simple yet realistic test case: the Optimization Test Interceptor with Fan (OTIFAN) configuration. To ensure a viable aircraft design, lower-fidelity methods are incorporated for the disciplines of mass properties, flight mechanics, and structural analysis. Three optimization strategies have been implemented into an automated framework: Grid search (GS) for framework setup and validation, gradient-based optimization (GO) for efficient local optimization and Bayesian optimization (BO) for global, gradient-free optimization. The strategies are applied across two objective functions, illustrating the applicability of the framework on geometric and mission profile optimizations.