Revolutionizing aerospace design: the synergy of CFD, ML, and optimization for enhanced vehicle performance

Aerodynamic design optimization has emerged as a vital process leveraging computational fluid dynamics (CFD) and advanced machine learning techniques pushing the boundaries for system performance. Various case studies explored by the author demonstrate the power and versatility of data-driven approaches in optimizing aerospace vehicle designs.
Case studies explored aerodynamic design optimization of complete aerospace vehicle considering several design variables addressing global optimization, stability management of aerospace vehicles investigating the complex coupled effect of aerodynamic surfaces and optimized canard shape for maximizing control effectiveness of a highly maneuvering vehicle.
The automated CFD data-driven approach established using modeFRONTIER for process automation and design optimization highlights the state-of-the-art advancements in aerodynamic design optimization considering large volumes of high-fidelity CFD data, showcasing the mission critical performance improvements across various aerospace applications.
The synergy of CFD, machine learning and optimization techniques is transforming the design process of aerospace vehicles, marking a revolutionary leap in the field by enabling accelerated CFD workflows and exploration of novel design concepts.