... the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two...

... Mach number profile for aerodynamic performance evaluation. To achieve blade sections with smooth surface and lenticular Mach number profile concave up on the pressure side and concave down on the suction side, the base algorithm is tuned by a surrogate inverse model trained by machine learning from...

... accuracy, which only work in a limited part of the overall design space. This paper shows that machine learning can be used to augment a designer in the process of developing loss models for complex flows. It is shown that it is able to help a designer discover new, more accurate and general, physical...

... especially at off-design conditions. To enable intensive modeling and optimization of complete vehicle powertrains for different drive cycles, the current piece of work seeks to combine the advantages of machine learning techniques and physical meanline modeling to facilitate faster, more accurate...

... 09 2021 27 09 2021 05 11 2021 machine learning film cooling single hole adiabatic efficiency convolutional neural network distribution prediction heat transfer National Natural Science Foundation of China 10.13039/501100001809 52076053 Natural Science...

...Harshal D. Akolekar; Yaomin Zhao; Richard D. Sandberg; Roberto Pacciani This paper presents the development of accurate turbulence closures for low-pressure turbine (LPT) wake mixing prediction by integrating a machine-learning approach based on gene expression programming (GEP), with Reynolds...

... turbulent Prandtl number (Pr t ), fail to accurately predict heat transfer in film cooling flows. In the present work, machine learning models are trained to predict a non-uniform Pr t field using various datasets as training sets. The ability of these models to generalize beyond the flows on which...