As a loads and aeroelastics engineer with a deep passion for data science and machine learning, I am always fascinated when these fields are combined. I am happy and proud to share a method that can combine these fields. Proper Orthogonal Decomposition (POD) model, a mathematical technique that simplifies complex data into its most significant patterns, thereby reducing computational effort significantly while preserving critical information within the data.

I applied the POD to the Common Research Model (CRM) through 220 Computational Fluid Dynamics (CFD) calculations to establish the POD basis. By utilizing the first 37 modes, 99% of the modal energy was recovered, indicating that the data could be reduced by approximately 85% while achieving almost exact reconstruction.

At HSB Solutions, we are excited to extend this efficient model reduction methodology to our clients for loads and aeroelastic analyses. We believe that this technique can significantly simplify the analysis of complex data, allowing for more efficient and effective decision-making.

#Aerodynamics #Loads #Aeroelastics #DataScience #MachineLearning