How Artificial Intelligence and Machine Learning Can Accelerate Decades of Aerospace Engineering to Just a Few Hours

Research and development (R&D) processes are being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML), significantly accelerating advancements such as the supercritical airfoil design in aerospace engineering. Traditionally, R&D relied on iterative experimentation and observation, but AI/ML technologies now offer new possibilities for faster innovation. These technologies utilize digital prototyping, physics-informed neural networks, and surrogate models to tackle complex design challenges, allowing engineers to generate and test a multitude of design candidates rapidly. The use of computational fluid dynamics (CFD) and AI techniques on platforms like Rescale exemplifies this shift, enabling aerodynamicists to optimize airfoil shapes more efficiently than traditional methods. For instance, by applying ML techniques such as principal component analysis and Gaussian-process surrogate models, the optimization process can be accelerated, reducing simulation times significantly. Such advancements demonstrate the potential of AI-driven R&D to transform industries by providing scalable and efficient solutions to complex engineering problems.