Proposal of Aerospace-informatics by Design of Ramjet Inlet Using Machine Learning

Seungho Lee, Sunho Lee, Jaehyuk Huh, and Sejin Kwon, "Proposal of Aerospace-informatics by Design of Ramjet Inlet Using Machine Learning", the 2023 Aerospace Europe Conference ( AEC ) joint event between the 10th European Conference for Aerospace Sciences ( EUCASS ) and the 9th Council of European Aerospace Societies ( CEAS ), July 2023

Paper

In this research, aerospace-informatics was proposed by design of ramjet inlet using machine learning. The model was organized in three steps. First, the maximum combustion chamber pressure and air mass flow rate error according to the shape was predicted. Second, the shape was discriminated whether it is feasible or not. Third, the shape with the high maximum combustion chamber pressure, the low air mass flow rate error, and the high feasibility was recommended. As a result, our proposed mechanism correctly predicted the pressure and the mass flow rate error, and sorted the ramjet design except crossing two less-important designs.