Advancements on the use of Filtered Rayleigh Scattering (FRS) with Machine learning methods for flow distortion in Aero-Engine intakes

• Published in: Experimental thermal and fluid science Vol. 160; p. 111325
• Main Authors: Migliorini, Matteo, Doll, Ulrich, Lawson, Nicholas J., Melnikov, Sergey M., Steinbock, Jonas, Dues, Michael, Zachos, Pavlos K., Röhle, Ingo, MacManus, David G.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2025
• Subjects: Aero-engine intakes; Filtered Rayleigh Scattering; Flow diagnostics; Flow distortion; Machine learning; Non-intrusive; Filtered Rayleigh Scattering; Non-intrusive; Flow distortion; Flow diagnostics; Aero-engine intakes; Machine learning

•Applied Filtered Rayleigh Scattering (FRS) for multi-quantity flow distortion measurements in aero-engine intakes.•FRS measurements can characterize simultaneously pressure, temperature and 3D velocity across a full aerodynamic interface plane.•The proposed FRS concept measured velocity with an acc...